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RECONSIDERING DOMESTIC VIOLENCE RECIDIVISM:TH E IMPACT O F COURT DISPOSITIONS AND STAKE IN CONFORMITY

A dissertation submitted to the

Division of Research and Advanced Studies of the University of Cincinnati

in partial fulfillment of the requirements for the degree of

DOCTORATE OF PHILOSOPHY (PH.D.)

in the Division of Criminal Justice of the College of Education

1999

by

Amy B. Thistlethwaite

B.A., University of Arkansas at Little Rock, 1991 M.A., Northeast Louisiana University, 1992

Committee Chair: Dr. Francis T. Cullen, Distinguished Research Professor


UMI Number: 9945581

UMI Microform 9945581 Copyright 1999, by UMI Company. All rights reserved.

This microform edition is protected against unauthorized copying under Title 17, United States Code.

UMI300 North Zeeb Road Ann Arbor, MI 48103


UNIVERSITY OF CINCINNATIJuly 1 99

_Amy__B_;__Thi stlethwaite__ _

hereby submit this as part of the requirements for the degree of:

Doctorate of Philosophy

iUm Criminal Justice

It is entitled- ̂ e c o n s i d e r i h i ^ Domestic Violence

Recidivism; The Impact of Court Dispositions and

Stake in Conformity

71

Approved by:


ABSTRACT

Support has been found for the idea that a domestic violence offender’s stake in

conformity mediates the effect of arrest on recidivism likelihoods. However, similar interactions

between stake in conformity and court dispositions have not yet been examined. This study

examined the effects of these interactions (in conjunction with main effects) on preventing,

reducing, and delaying rearrests for domestic violence across a sample of over 3,000 arrestees in

Hamilton County (Cincinnati), Ohio. The relative effects of individual- versus aggregate-level

measures of stake in conformity were also examined. The analysis of main effects yielded support

only for hypotheses related to neighborhood concentrated disadvantage and the speed o f case

processing. In contrast, the analysis of interactions revealed that more severe dispositions were

significantly more effective for reducing rearrests among persons with a greater stake in conformity

(defined in terms of greater residential stability).


ACKNOWLEDGEMENTS

I would like to take this opportunity to thank several individuals for their contributions to

this project. First, I would like to thank Dr. Francis T. Cullen, distinguished research professor for

serving as chairperson of my committee. Dr. Cullen’s insight and expertise provided the necessary

"social context" in which the study is framed. I would also like to thank the rest o f my committee

members: Drs. James Frank, Mitchell B. Chamlin, and J. Robert Lilly; each of whom were

extremely patient with me and provided valuable and indispensable comments in a timely manner.

I would like to thank the division head of the criminal justice program, Dr. Edward J.

Latessa for his support and for allowing me the numerous opportunities to teach while completing

this project.

Finally, I would like to thank my husband, Dr. John D. Wooldredge for his guidance and

support on this project.


TABLE OF CONTENTS

Chapter OneDomestic Violence as a Social and Criminological Problem

Chapter TwoTheoretical Framework

Chapter ThreeResearch Strategy

Chapter FourResearch Methodology

Chapter FiveResults

Chapter SixDiscussion

References

Appendix OneCorrelation Matrix

Appendix TwoIntake Interview Form

Appendix ThreeCode Book

Appendix FourZero-order Relationships Between Measures of Prior Record and Likelihood of Rearrest and Number of Rearrest for Domestic Violence Recidivism

Appendix FiveTests of Significant Differences in Relationships Between Court Dispositions and Recidivism Across Stake in Conformity Groups

Appendix SixGeographical Distributions of Domestic Violence Recidivism in Hamilton County, Ohio.

Appendix SevenGeographical Distributions of Domestic Violence Recidivism in Cincinnati, Ohio.


Table 1.1

Table 4.1

Table 5.1

Table 5.2

Table 5.3

Table 5.4

Table 5.5

Table 5.6

Table 5.7

Table 5.8

Table 5.9

Table 5.10

Table 5.11

Table 5.12

LIST OF TABLES

Summaiy of Findings of the Minneapolis Domestic Violence Experiment and Subsequent Replications

Variables and Frequencies

Zero-order Relationships Between Predictors and Outcome Measures of Domestic Violence Recidivism

Multivariate Analyses Predicting Domestic Violence

Empirical Relationships Between Court Dispositions and Recidivism Specified by Categories of Stake in Conformity: Predicting Rearrest for Domestic Violence

Empirical Relationships Between Court Dispositions and Recidivism Specified by Categories of Stake in Conformity: Predicting Number of Rearrests for Domestic Violence

Empirical Relationships- Between Court Dispositions and Recidivism Specified by Categories of Stake in Conformity: Predicting Time to Rearrest for Domestic Violence

Tests of Significant Differences in Relationships Between Court Dispositions and Recidivism Across Stake in Conformity Groups: Predicting Rearrest for Domestic Violence

Tests of Significant Differences in Relationships Between Court Dispositions and Recidivism Across Stake in Conformity Groups: Predicting Number of Rearrests for Domestic Violence

Tests of Significant Differences in Relationships Between Court Dispositions and Recidivism Across Stake in Conformity Groups: Predicting Time to Rearrest for Domestic Violence

Survival Analysis of Time to Rearrest After Sentence Completion: Cumulative Proportions Not Recidivating

Survival Analysis of Time to Rearrest A fter Sentence Completion: Standard Errors of Cumulative Proportions Not Recidivating

Survival Analysis of Time to Rearrest After Sentence Completion: Proportions Exposed to Risk Recidivating During Specific Month

Survival Analysis of Time to Rearrest After Sentence Completion: Standard Errors of Proportions Exposed to Risk Recidivating During Specific Month


CHAPTER ONE

DOMESTIC VIOLENCE AS A SOCIAL AND CRIMINOLOGICAL PROBLEM

Introduction

Domestic assault is the most common form of violence encountered by the police

(Sherman et al. 1992). More women are injured by the hands o f a spouse or partner than any

other source (Zorza 1992). Despite such trends, domestic assault historically has only become an

issue of public concern during periods when crime has been rampant. Efforts to criminalize

domestic violence and subsequent efforts to enforce these laws thus have tended to occur in times

of social turmoil when criminal behavior has been attributed to a breakdown in law and order

(Pleck 1989).

Police response to the problem of domestic violence has traditionally been one of non

intervention. Help for domestic violence victims was left up to private organizations and charities.

Beginning in 1970 and lasting until 1983, however, several jurisdictions established legislation

intended to modify official and societal responses to the problem of domestic violence (Buzawa

and Buzawa 1985). Despite such legislative trends, evidence suggests that during this time the

police remained largely apathetic toward domestic violence (Zorza 1992).

In the early 1980s a research study was undertaken that would help to dramatically alter

traditional police responses to domestic violence. In 1983, Lawrence Sherman and Richard Berk

(1984) conducted a controlled experiment in Minneapolis to determine which police response—

arrest, separation, or mediation—would yield the largest decrease in domestic violence recidivism.

The experiment found that arrested offenders were less likely to recidivate compared to offenders

who were either separated o r counseled by the police (Sherman and Berk 1984). Drawing from the

experiment’s conclusion, mandatory arrest policies were widely adopted across jurisdictions in the

3


United States, and arrest became the predominant response to domestic violence (Sherman et al.

1992).

It was not until the original experiment was replicated five years later that questions were

raised as to the effectiveness of mandatory arrest policies. Experiments conducted in Milwaukee,

Omaha, and Charlotte revealed that arrest actually escalated the occurrence of domestic violence

(Dunford et al. 1990; Hirshel et al. 1990; Sherman et al. 1992). Researchers began examining

differences across jurisdictions and found that extra-legal factors such as employment and marital

status either increased or decreased the likelihood that arrest would deter this behavior. In

addition, the social structure of a community (proportion of unemployed males or unmarried

couples) was hypothesized to determine how residents react to arrest (Sherman et al. 1992).

W hether arrest deters domestic violence offenders remains unknown (Sherman et al.

1992). Further, the effectiveness of arrest could depend upon what happens to the offender after

arrest. Offenders who are arrested are not necessarily prosecuted, convicted, and sentenced.

Further, arrested offenders spend different amounts of time in pretrial detention. While the

relationship between time spent in custody upon arrest, prosecution, and domestic violence

recidivism has been examined (Berk et al. 1992; Hirschel et al. 1992; Sherman et al. 1992), no

studies exist that explore the possible relationship between other (and more punitive) sanctions

and recidivism.

This study is not offered as a replication of prior studies examining the effectiveness of

arrest on domestic violence recidivism. Rather, the study is intended to expand upon prior

research examining the separate and combined effects of informal and formal social control on

domestic violence recidivism. The study will examine both the main effects of different types of

informal social control processes and formal social control (prosecution, conviction, and various

sanctions) on domestic violence recidivism. In addition, interaction effects between formal and

informal social control processes are examined as well. Interactions have been examined in

previous research (e.g., Berk et al. 1992; Dunford et al. 1990; Sherman et al. 1992), but have been

4


limited to analyzing the interactions between arrest employment, education, and martial status.

This study builds upon the existing body of research by exploring additional informal social control

variables such as type of employment and family ties. The present study further builds upon past

research by examining the effects of these social control processes at the individual level as well as

the contextual effects of community characteristics on individual behavior. Aggregate-level

measures (i.e., proportion of college educated individuals, proportion of employed individuals, and

the proportion of two-parent families) are included to determine the extent that these

neighborhood characteristics influence an individual’s propensity to recidivate and/or increase or

decrease the effectiveness of formal social control.

What follows is a brief historical summary of the criminalization of domestic violence in

the United States from the colonial period to the present time. Included in this summary is a

description of the original Minneapolis Domestic Violence Experiment, its conclusions and

influence on criminal justice policy, and findings from the six replication studies. Chapter one

concludes with an exploration of why the domestic violence replication studies have produced

conflicting results. Differences have been attributed to variations in the research designs of each of

the replication studies (Gam er et al. 1995; Sherman et al. 1992). A second possible explanation

lies in the variations in informal social control processes between the replication cities and among

individual offenders as well as variation in what happens to an offender after arrest.

Chapter two provides the theoretical orientation underlying the study by focusing on the

second explanation—that the deterrent effect of formal sanctions depends upon informal social

control processes at both the individual level and aggregate level. Informal social control refers to

social and social psychological processes that inhibit criminal behavior (Reiss 1951). For example,

married and employed individuals may have more to lose if caught committing crimes and may

thereby be less likely to engage in criminal behavior. Both sets of factors are hypothesized to

operate at both the individual level and aggregate level. That is, community-level characteristics

are hypothesized to have contextual effects upon individual members. Individuals residing in

5


communities comprised of a significant proportion of married and employed individuals are more

likely to commit significantly fewer crimes than individuals residing in communities with significant

proportions of single and unemployed individuals. Formal social control will refer to prosecution,

conviction, and sentencing.

Chapter three outlines the research strategy of the study. The specific interactions between

formal and informal social control processes are outlined and discussed. Chapter four details the

research methodology for the study. Finally, chapter five presents the results derived from the

study and chapter six provides a discussion of these results.

The Criminalization of Domestic Violence

Early Laws (1641-1680)

Efforts to criminalize spousal assault coincide with larger social movements attributing

crime in general to a breakdown in law and order (Pleck 1989). Initial efforts date back to 1641

when the Puritans of Massachusetts included in their Body o f Laws and Liberties provisions against

spousal assault (Pleck 1989). Interest in family violence was limited to the New England colonies

primarily because they were founded by religious dissenters. These early laws reflected Puritan

religious principles in which family violence was viewed as amoral and a sin against God (Pleck

1989). Despite criminalization, however, laws against spousal abuse were seldom enforced.

Enforcement took a back seat to efforts to keep families united and to maintain respect for family

privacy (Pleck 1987). Inhabitants of Plymouth Colony were the last to pass a law against spouse

abuse in the 17th century, and by the end of the century, limited enforcement turned into

nonenforcement (Cushing 1977).

Interest in domestic violence was virtually nonexistent throughout the 18th and the first

half of the 19th century (Kerber 1980). Family violence, once again became a private matter and

was no longer subject to state interference (Pleck 1987). The few cases brought before the courts

6


met with opposition. If a wife tried to leave her abusive husband, she could be tried for

abandonment, and if she dismissed a complaint, she could be held in contempt of court (Fleck

1989). Although two states passed laws against spousal assault during the 1850s (Tennessee and

Georgia), enforcement was still rare (Pleck 1989). Convicted offenders could choose between the

penalties of jail or paying a fine. Too poor to pay, the majority went to jail, leaving families unable

to take care of themselves (Friedman and Percival 1981). Fearing impoverishment, many wives

dismissed their complaints, which resulted in an unwillingness of the police to continue making the

arrests (Friedman and Percival 1981).

Increase in Enforcement 11875-1890)

Interest in spousal abuse was renewed during the last quarter of the 19th century. Crime

was once again a public concern. As the industrial revolution advanced, crime and disorder

emerged as salient public concerns. Urbanization and immigration threatened the conservative

values of middle-class Americans who grew increasingly fearful o f crime. The solution was to

impose a Protestant morality on the underclasses (Pleck 1989).

Three groups emerged as advocates for protecting females from spousal abuse. Lawyers

and judges campaigned for stricter penalties for domestic violence offenders. Between 1876 and

1906, legislatures in twelve states and the District of Columbia voted on whether to allow corporal

punishment for domestic violence offenders (Pleck 1979). Laws allowing such penalties were

passed in three of these states: Maryland, Delaware, and Oregon (Pleck 1979).

Early feminists supported these laws, however, were more interested in protecting and

assisting female victims. Despite their efforts in Massachusetts, legislation to increase access to

legal remedies never passed, resulting in feminists joining forces with supporters of corporal

punishment for offenders (Pleck 1989).

A third group of advocates belonged to various women’s temperance organizations. These

women believed that the cause of domestic violence was alcohol; thus the solution lay in

7


prohibition and stricter penalties for offenders (Pleck 1989). As a result of their efforts, several

jurisdictions passed civil laws permitting a wife to sue saloon owners for damages resulting from

intoxication provided the wife had requested the owner not to serve her husband (Pleck 1989).

These groups were also successful in forming organizations to provide financial and legal assistance

to female victims.

Perspectives on Domestic Violence During the Twentieth Century

During the Progressive era (1900-1920), interest in domestic assault changed its focus. As

rehabilitation became the dominant approach to dealing with criminality, the state embraced a

social service approach, as opposed to law enforcement and punitive penalties, for dealing with the

problem of domestic assault (Rothman 1980). Crime was now blamed on impoverished

neighborhoods and criminal genes. The solution to domestic assault lay in policies designed to

improve social conditions and providing psychological treatm ent to offenders (Pleck 1989).

Maintaining the family unit became the goal of intervention.

The social service approach for dealing with spousal assault endured for the next eighty

years. Public interest remained low until the early 1960s when spouse abuse once again became a

social problem—but this time one of national concern. Several factors were responsible for the

increasing interest in domestic violence. First, the public became more aware of the extent of

domestic violence. Constituting the single largest cause of injury to females in the United States,

police officers respond to between two and eight million incidents of domestic violence each year

(Sherman et al. 1992; Zorza 1992). Second, crime rates were increasing as well as public and

political support for punitive crime control strategies (Binder and Meeker 1988). Both liberals and

conservatives began to question the effectiveness of state efforts to rehabilitate criminal offenders

(Cullen and Gilbert 1982).

Domestic assault also became an issue for feminist organizations in the 1970s. Battered

women’s groups and victims’ rights groups were organized to provide shelters and legal assistance

8


for victims of spousal abuse (Pleck 1989). These groups were also responsible for calling attention

to the lack of police response to the problem of domestic assault. From 1970 to 1983, thirty-six

states and the District of Columbia established legislation intended to modify official and societal

responses to the problem of domestic violence (Buzawa and Buzawa 1985). For example, several

states passed laws permitting an officer to make a warrantless arrest if the officer had probable

cause to believe that a protection order had been violated. Further, states imposed additional

responsibilities upon officers such as remaining with the victim until he/she was out of danger or

taking the victim to a shelter. Despite such legislative trends and the fact that domestic assault is

the most common form of violence encountered by the police, evidence suggests that during this

time the police were apathetic towards domestic violence and it was considered a low priority

(Sherman et al. 1992; Zorza 1992). Most police departments had explicit non-arrest policies.

Officers were instructed to discourage victims from pressing charges, and the typical police action

was to ask the offender to leave and "cool off' (Zorza 1992). Such inaction by the police often

appeared to make matters worse. According to a study on spousal homicides conducted by the

Police Foundation in 1977, eighty-five percent of the sample had called for police assistance at

least once in the preceding two years and fifty-four percent of the sample had called five or more

times (Sherman et al. 1992). W hat remained unclear from the study was whether official action

taken by the police would have reduced the number of homicides.

A fourth factor was an increase in litigation against police departments. Two class action

suits were filed in 1976: one for failing to arrest domestic violence offenders against the Oakland

Police Department and the second against the New York City Police Department (Zorza 1992).

The end result of such litigation was a change in departmental policies allowing police officers to

make warrantless arrests of felonious domestic violence offenders based on probable cause

(Sherman et al. 1992).

The threat of liability was enough for many police departments to follow suit and

implement voluntary changes in their policies. Most departments, however, were still deficient in

9


their policies, and very few allowed warrantless arrests based on probable cause for misdemeanant

domestic assault. It was not until the late 1980s that all states, with the exception of Alabama and

West Virginia, allowed the misdemeanor arrest of an offender based on probable cause when the

assault does not occur in the officer’s presence (Zorza 1992).

While most jurisdictions were struggling with the decision of when to allow police officers

to make warrantless arrests for domestic violence, the state of Oregon passed a bill in 1977

requiring police officers to arrest based on probable cause (Zorza 1992). Despite the bill, several

departments continued instructing their officers to avoid arrest if possible and respond in terms of

crises intervention (Zorza 1992). It was not until 1980 when a civil suit for police inaction was filed

against an Oregon police department that the courts forced the implementation of the bill (Zorza

1992). By 1982, five states required the mandatory arrest of domestic violence offenders based

upon probable cause (Zorza 1992).

The Minneapolis Domestic Violence Experiment

Description

Perhaps the most important factor that contributed to the dramatic shift in police policy

for responding to domestic violence incidents was an experiment conducted by Lawrence Sherman

and Richard Berk with the Police Foundation and the Minneapolis Police Department for the

National Institute of Justice. Beginning in 1978, police officers in Minneapolis were authorized to

make warrantless arrests based on probable cause for misdemeanant domestic violence cases

(Sherman 1992). Arrest was not mandatory, however. Thus, before the experiment was initiated, it

was left up to the individual officer’s discretion to either arrest, separate the parties, or attempt

mediation and counseling.

The experiment began in early 1981 and lasted until mid 1982. The purpose of the

experiment was to determine which of the above three police responses produced the lowest

10


recidivism rates for offenders (Sherman and Berk 1984). The experiment involved a randomized

lottery selection between three treatments: arrest (with a t least one night in jail); separation of the

parties for at least eight hours (or arrest if the parties refused); or mediation (providing some type

of counseling to the parties) (Sherman and Berk 1984). Police officers carried color coded report

forms for the three responses and were instructed to respond to each case of misdemeanant

domestic violence in terms of the predetermined response (Sherman and Berk 1984). The purpose

behind the lottery selection was to control for preexisting differences among offenders.

Results

The experiment included two measures of recidivism: official arrest records and victim

interviews (Sherman and Berk 1984). Both measures revealed that the arrested offenders were less

likely to recidivate compared to offenders that were counseled or separated during a six-month

follow-up period (Sherman and Berk 1984). Specifically, according to official records, ten percent

of the arrested group had at least one repeat offense; nineteen percent o f the counseled group;

and twenty-four percent of the separated group (Sherman and Berk 1984). Results from the victim

interviews revealed somewhat higher figures. Nineteen percent of the arrested group, thirty-three

percent of the separated group, and thirty-seven percent of the counseled group committed at least

one repeat offense (Sherman and Berk'1984).

Impacts

Based on their conclusions, the authors made three recommendations. First, it was

suggested that all states adopt policies permitting an officer to make warrantless arrests for

domestic violence offenses not occurring in a police officer’s presence. Second, although the

authors cautioned against a policy o f mandatory arrest, they did contend that "on the basis of this

study alone, police should probably employ arrest in most cases of minor domestic violence"

11


(Sherman and Berk 1984). Finally, the authors recommended that the study be replicated in other

locations.

The full impact of the Minneapolis Domestic Violence Experiment on domestic violence

policy is unknown. Dramatic changes in those policies did occur following the release of the

experiment’s results. By 1989, eighty-four percent of police agencies in large urban areas adopted

policies favoring arrest and seventy-six percent imposed a mandatory arrest policy (Sherman 1992).

Further, Chapter 3 (under Title IV) of the 1994 Crime Bill advocates the adoption of mandatory

arrest policies for domestic violence offenders throughout the United States.

Although mandatory arrest policies were widely adopted across the United States, how the

policies were implemented remains unknown. Police were making more arrests for domestic

violence than in years past (Sherman et al. 1992; Sherman and Cohen 1989). However, studies of

the Phoenix, Minneapolis, and Milwaukee Police Departments found that only Milwaukee

attem pted a strict implementation of their mandatory arrest policy (Sherman et al. 1992). Cases

continue to be evaluated according to a variety o f legal, ideological, practical, and political issues.

Police officers continue to be reluctant to arrest domestic violence offenders if they perceive that

their efforts will be a waste of time because offenders are rarely prosecuted (Ferraro 1989).

Replication Studies

Prior to the replication of the Minneapolis results in other cities, data from the original

experiment were first reanalyzed. A replication by Witte et al. examining only the victim interview

data found a deterrent effect for arrest, but the deterrent effect disappeared after the six month

follow-up (cited in Sherman et al. 1992). Additionally, examining the same data, Lowery found

that the deterrent effect of arrest was strongest among offenders with less prior involvement in the

criminal justice system (cited in Sherman et al. 1992).

Several replications were also conducted without the use of an experimental design. A

study of domestic violence cases in a southern California county found support for the deterrent

12


effect of arrest (Berk and Newton 1985). An examination of victim interviews by Jaffe et al. (1986)

found that recidivism rates were lower when the police pressed charges compared to when the

police did not press charges. Finally, Fagan et al. (1984) found support for a deterrent effect in

Florida, Vermont, Ohio, and North Carolina for arrest, but the effect was limited to cases

involving only minor injuries.

The Minneapolis Domestic Violence Experiment itself was replicated six times in five

different cities: Omaha, Charlotte, Milwaukee, Colorado Springs, and Miami. All of these

replications utilized an experimental design and were close approximations (although not exact) of

the original Minneapolis experiment.

Omaha

The study by Dunford et al. (1990) failed to find an overall deterrent effect for arrest

above and beyond separation or counseling based on official measures for a six month follow-up

period. A slight difference favoring arrest was found among the victim interview data (Dunford et

al. 1990). Support for an escalation effect for arrest, however, was found among the official

measures after the initial six-month follow-up period (Dunford et al. 1990). Over time, arrest

appeared to be making things worse. A subgroup analysis revealed that the deterrent effects were

greatest among offenders who were employed at the time of arrest and escalation effects (arrest

actually increased the occurrence of domestic violence) were greatest among the unemployed

(Dunford et al. 1990).

A second experiment was conducted in Omaha and was the only study to examine those

cases in which the offender fled the scene prior to arrival by the police. This category constitutes

approximately one-half o f all domestic violence cases (Sherman et al. 1992). Absent offenders were

randomly assigned to one of two groups: a group in which the prosecutor issued an arrest warrant

and a group in which the victim was instructed how to file a complaint (Dunford et al. 1990).

13


Offenders in the no-warrant group were twice as likely to recidivate during a six and twelve month

follow-up period (Dunford et al. 1990).

Charlotte

The study conducted in Charlotte offers the closest replication to the Minneapolis

research design. The Charlotte experiment involved testing the separate effects of immediate

arrest, issuing a citation ticket to appear in court, and separation or counseling. Results from

Charlotte reveal a similar pattern as Omaha. No deterrent effects favoring arrest were found in

either the official measures or in the victim interview data for a six month follow-up period

(Hirshel et al. 1990). O f all of the replication studies, the official data on recidivism in Omaha

showed the strongest escalation effects for formal police action (Hirschel et al. 1990). Formal

action by the police increased the risk o f recidivism by a ratio of 1.61 to 1.

Milwaukee

Researchers in Milwaukee also failed to find an overall deterrent effect for arrest in either

the official measures or in the victim interview data (Sherman et al. 1992). A deterrent effect was

found, however, for offenders spending thirty days in jail subsequent to arrest. As in Omaha, the

deterrent effect of arrest was greatest among the employed, while the escalation effect was greatest

among the unemployed (Sherman et al. 1992). Sherman and his colleagues also examined three

additional treatments: short arrest (an average of three hours in custody), long arrest (an average

of twelve hours in custody), and a warning to arrest if the police had to return to the scene. An

escalation effect for arrest was found in the official measures for short arrest (Sherman et al.

1992).

14


Colorado Springs

Researchers in Colorado Springs randomly assigned offenders to one of four treatment

groups: arrest with a protection order to stay away from the victim; crises counseling for the

suspect at police headquarters with a protection order; a protection order only; and counseling on

the premises only (Berk et al. 1992). Although the official measures failed to produce any overall

deterrent effect for arrest above and beyond the other three treatments, the victim interview data

revealed a clear deterrent effect (Berk et al. 1992). Further, the researchers examined the

interactions between employment and arrest and found strong support for a deterrent effect

among the employed and an escalation effect among the unemployed (Berk et al. 1992).

Miami-Dade County

The experiment in Miami-Dade County involved randomly assigning offenders to one of

four different treatment groups:arrest with follow-up counseling arrest without follow-up

counseling, no arrest with follow-up counseling and arrest without follow-up counseling. The

follow-up counseling was conducted by specially trained police units. Results of the Miami-Dade

County replication study provide the strongest evidence verifying the deterrent effect of arrest

found in Minneapolis. Research in Dade County revealed a deterrent effect of arrest based on

victim interview data (Pate et al. 1991). Further, official measures not limited to the same suspect

as the offender against the same victim also show a deterrent effect for arrest beyond the other

treatment groups (Pate et al. 1991). Not only were no escalation effects revealed in either the

official or victim interview data, but repeat arrests for domestic violence offenses also produced

deterrent effects (Pate et al. 1991).

15


Table 1.1 Summary of Findings of the Minneapolis Domestic Violence Experiment and Subsequent Replications

City

Minneapolis Omaha Charlotte Milwaukee Colorado Springs Miami

Findings

6 monthdeterrenteffectofficialmeasures yes no no no no 1 of 2

6 monthdeterrenteffectvictiminterviews yes slight no no yes yes

6-12 monthescalationeffectofficialmeasures n o yes yes yes no no

6-12 monthescalationeffectvictiminterviews * no no no no no

escalation effect for unemployed * yes * yes yes *

deterrent effect for employed * yes * yes yes *

key:*no relationship reported

16


Why the Inconsistent Results?

The only conclusion that can be drawn from these studies as a whole is that no clear

relationship between arrest and recidivism for domestic violence exists. I t is possible that the

inconsistent findings could be a product of the treatment and methodological differences between

replications. According to G am er et al. (1995), the Minneapolis Domestic Violence Experiment

has yet to be replicated using its original measures and analysis. Treatment differences include

variations in the actual time an offender spent in jail and in the rates at which offenders were

prosecuted (Gamer et al. 1995; Sherman et al. 1992). For example, in Milwaukee a deterrent

effect for arrest was found only for offenders who spent thirty days in jail subsequent to arrest

(Sherman et al. 1992). Short arrest (an average of three hours in custody) appeared to escalate the

occurrence of repeat domestic violence. The deterrent effects of criminal justice sanctioning

beyond arrest and time spent in custody subsequent to arrest has yet to published using the data

yielded from all of the replication studies. Studies which examined prosecution rates have yielded

mixed results (Jaffe et al. 1986; Sherman et al. 1992).

Methodological differences exist as well. For example, in Minneapolis, individual police

officers randomly assigned cases to the treatment groups; in Omaha, Charlotte, Miami, and

Colorado Springs, a police dispatcher made the assignments; and in Milwaukee, assignments were

made by a staff of civilians (Sherman et al. 1992). A consequence of allowing the police officers to

assign cases was that the officers would sometimes omit a case from the experiment to avoid

arresting a suspect that responded to them in a respectful manner (Sherman et al. 1992).

A thorough analysis of the relationship between methodological differences and study

outcomes has yet to be published to-date. Such would require pooling of the data yielded from the

replication studies and analyzing this data as whole, accounting for variations in study designs.

From the existing literature it is impossible to determine whether consistencies in research designs

yield consistent results in study outcomes. Because this study is not intended as a replication of the

Minneapolis Domestic Violence Experiment, or any of its subsequent replications, the study will

17


only explore an alternative explanation for the inconsistent results across replication cities. The

inconsistent findings could be a result o f variations in formal and informal social control processes

(Sherman et al. 1992).

Social controls are the perceived benefits and punishments that are likely to result when

an individual either conforms to, or deviates from, a system of consensually agreed upon

definitions of what constitutes conformist and deviant behavior (Komhauser 1978). There are two

types of social control: formal and informal. Formal social control refers to those actions taken by

the criminal justice system (i.e., arrest, prosecution, and sanctioning). Informal social control refers

to social and social psychological processes that inhibit criminal behavior (Reiss 1951). According

to control theorists, all controls are social in the sense that they develop and are maintained

through a process of social interaction (Komhauser 1978). Furthermore, it is through a process of

social interaction with an individual’s primary social groups that social control originates and

becomes strengthened (Reiss 1951).

There are several dimensions o f informal social control. Reiss refers to these dimensions

as personal and social control. Nye (1958) argues that there are three: direct control, indirect

control, and internal control. Hagan uses the terms "instrumental" (referring to direct control) and

"relational" (referring to indirect control). Reckless (1967) refers to the different dimensions as

factors in outer and inner containment. According to Hirschi (1969), these processes include an

individual’s attachment, commitment, and investment in such institutions as the family, work, or

school as well as an internalization and acceptance of society’s values and rules.

Although the dimensions of informal social have been called by different terms by

different criminologists, what they all have in common is an emphasis on two specific elements:

direct and indirect social control (Komhauser 1978). A breakdown in either type is sufficient to

explain delinquency and/or criminal behavior. Direct control encompasses attempts made by such

social institutions as the family, school, and work to restrain an individual’s behavior through

supervision (Hagan et al. 1987; Hirschi 1969; Nye 1958; Reckless 1961; Reiss 1951; Wells and

18


Rankin 1988). Supervision is an important component of social control and is exercised by these

social groups in two ways. Parents may impose a curfew upon a child to restrict that child’s

behavior, monitor their child’s behavior, and finally punish their child for failing to abide by the

curfew (Wells and Rankin refer to this as direct parental control). According to Hagan et al.

(1987), the degree of supervision exercised by parents over their children has a direct influence on

a child developing a preference for taking risks and subsequently engaging in delinquent behavior.

Supervision is also exercised in the form of restricting an individual’s opportunity to engage in

criminal behavior. If a child is involved in conventional activities their opportunity to engage in

unconventional behavior becomes limited (Hirschi 1969).

The same social institutions responsible for exercising direct control over the behavior of

its members are also responsible for providing indirect social control. Indirect social control

involves an internationalization of conventional values and norms (Hirschi 1969; Reckless 1961;

Reiss 1951). Indirect social control emerges out of a process of socialization and interdependency.

Individuals are taught what the values and rules of society are and, through a process of social

interaction with institutions of informal social control, are trained in conformist roles (Reckless

1967). It is the strength of these social relationships that provide an explanation of criminal

behavior. Further, individuals who are strongly connected to informal social control institutions

(i.e. the family, work, or school) are provided with the necessary social and psychological

resources, such as conformist self-identity and emotional support, which to draw upon when faced

with the temptation of criminal behavior (Braithwaite 1989; Hagan et al. 1987; Sampson and Laub

1993). Indirect social controls need not be overt and intended (Elliott 1995). A strong attachment

towards another person can result in conformist behavior regardless o f any action taken by the

individual with whom the attachment is made (Elliott 1995; Hirschi 1969). This particular

dimension is relevant for explaining domestic violence recidivism in that a consistent finding in the

research is that married individuals are less likely to recidivate compared to their counterparts

(Pate et al. 1991).

19


Informal social control processes may also operate at the both the individual level and

aggregate level. Community characteristics are hypothesized to influence the behavior of individual

residents. Factors such as poverty, transiency, and heterogeneity can result in a neighborhood

becoming "disorganized" (Shaw and McKay 1942); a consequence of neighborhood disorganization

is that the neighborhood—an institution of informal social control in and of itself—is no longer able

to exercise control over the behavior of its members either directly through supervision or

indirectly by transmitting conventional values and norms (Komhauser 1978). Durkheim (1938)

refers to this breakdown in social solidarity as "anomie" or normlessness in which individuals are

left to rely upon their own resources or the resources of unconventional groups (Shaw and McKay

1942) when faced with problems.

Variations in informal social control institutions could explain why arrest "worked" for

some individuals and in come communities. For example, subgroup analyses conducted in Omaha,

Milwaukee, and Colorado Springs revealed a deterrent effect for arrest among the employed and

an escalation effect for arrest among the unemployed (Berk et al. 1992; Dunford et al. 1990;

Sherman et al. 1992). Further, Miami-Dade County produced results most consistent with

Minneapolis, finding that arrest does deter future domestic violence. In Miami, seventy-nine

percent of the sample of offenders were married, a factor which has been shown to be correlated

with recidivism for o ther types of criminal offenses (Sampson and Laub 1993).

In addition, the deterrent effect of arrest could actually depend upon what happens to the

offender after arrest. Not all arrested offenders are prosecuted o r are convicted. Variation also

exists in the sentences of those offenders who are convicted. Some offenders serve a jail sentence

while others receive a sentence of probation and/or are required to receive domestic violence

treatment.

20


Conclusion

Findings from the Minneapolis Domestic Violence Experiment and its subsequent

replications present the possibility that the effects of criminal justice intervention (formal social

control) will produce different results depending upon an individual’s "stake in conformity." That

is, the more an individual has to lose by committing a crime (i.e., loss of employment), the more

likely that individual will be deterred from committing a crime. Further, individuals residing in

communities comprised of significant proportions of employed individuals may be more likely to

feel that they have more to lose by committing crimes, may be more likely to feel stigmatized if

arrested, and may therefore be more likely to be deterred through criminal justice intervention.

W hether domestic violence offenders are deterred through formal social control could

depend upon the specific type of criminal justice intervention. Arrest can be considered

punishment in that the offender spends some amount of time in detention, but arrest does not

automatically lead to prosecution, conviction, and jail time. The argument can be made that

offenders who are prosecuted are subjected to more punishment than offenders who are arrested

and released without charges being pressed (Black 1976). Further, offenders who are convicted are

subjected to even more punishment, followed by offenders who receive probation and/or serve a

jail sentence. ,

Chapter two details the theoretical framework for the study. This framework focuses on

the relevance of theories of formal and informal social control for explaining domestic violence

recidivism. Research on deterrence theory and the deterrent effect of punishment is presented as

well as findings from studies examining the deterrent effect of formal social control (arrest) for

domestic violence recidivism. Further, informal social control theory is traced from its origins to

current revisions of the theory. Both the early theories and subsequent modifications contain

elements that are pertinent to explaining variations in domestic violence recidivism.

21


CHAPTER TWO

THEORETICAL FRAMEWORK

Introduction

Findings from the Minneapolis Domestic Violence Experiment and the six replication

studies raise questions about the ability of criminal justice sanctions to effectively reduce criminal

behavior. Similar criminal justice sanctions can produce different effects on different types of

offenders and in different types of communities (Sherman 1993). Braithwaite (1989) argues that

the effects of criminal sanctions can either be "stigmatizing" or "reintegrative" depending on

individual offender characteristics and community-level characteristics. Sanctions that stigmatize

can increase crime; while reintegrative sanctions can decrease crime (Braithwaite 1989). Further,

individuals vary in their perceptions of criminal justice sanctions as fair or unfair (Tyler 1990).

Individuals perceiving a criminal sanction as fair are more likely to believe in the legitimacy of the

law and are thus less likely to break it (Tyler 1990). Several factors can influence the perception of

fairness, including informal social control processes (Scheff and Retzinger 1991). A recent

reanalysis of the data from the Milwaukee replication study supports the contention that domestic

violence offenders who perceive themselves to be treated fairly by the criminal system had lower

rates of recidivism compared to offenders who perceived themselves treated unfairly (Paternoster

et al. 1997).

Studies on domestic violence recidivism reveal that arrest can either deter future domestic

violence, have no effect, or escalate its occurrence (Berk et al. 1992; Dunford et al. 1990; Hirschel

et al. 1990; Pate et al. 1991; Sherman 1992; Sherman and Berk 1984). The deterrent effect appears

to be related to individual-level and aggregate-level informal social control processes such as

employment and marital status (Berk et al. 1992; Dunford et al. 1990; Sherman et al. 1992).

22


What follows is a brief summary of the research on deterrence theory. Although this study

is not a direct test o f deterrence theory, prior studies of deterrence theory provide insight into the

difficultly establishing a clear link between criminal justice sanctions and criminal behavior. A

consistent finding in the deterrence literature is that informal social control processes (i.e.,

employment and marital status) offer some explanation as to why criminal sanctions can produce

different results.

Chapter two also provides a summary of social control theory beginning with the origins of

social control theory and recent modifications of the theory. An understanding of social control

theory furnishes insight into how informal social control processes can insulate individuals from

the enticements of crime. This study is not intended as a direct test of social control theory.

Rather, the study examines the interactions between formal social control variables (criminal

sanctions) and informal social control factors such as employment, marital status, family ties, and

education. Further, the study examines the possible contextual effects of aggregate-level measures

on individual domestic violence recidivism.

Deterrence Theory (Formal Social Control)

Deterrence seeks to reduce future criminal behavior through punishment. Criminal and

noncriminal behavior is believed to be a result of a conscious calculation of what benefits will be

derived from a particular act or what costs are likely to result (Gibbs 1975). When an individual

commits a crime, his/her behavior is based on the perception that what can be gained is either

worth the risk of being punished or that the punishment is not severe. Two types of deterrence

exist: general and specific. General deterrence is aimed at preventing criminal behavior at the

societal level by making an example of other criminals. Specific deterrence attempts to prevent an

individual offender from engaging in additional criminal behavior (Gibbs 1975). There are three

dimensions of punishment that are necessary if punishment is to deter criminal behavior: severity,

certainty, and celerity.

23


Few studies of deterrence theory existed prior to the 1960s. The first studies of deterrence

theory focused on aggregate relationships between the certainty and severity o f punishment at the

state level and officially recorded crime rates (Paternoster 1987). Support for increasing the

severity and certainty o f punishment appeared to come from studies conducted by Erlich. Erlich

(1972) found that the higher the probability of imprisonment for robbery, the lower the crime rate

for robbery. Tittle and Rowe (1974) contend that clearance rates for property offenses would have

to increase from around twelve percent to approximately thirty percent to produce a noticeable

effect on crime rates.

Finding few favorable results supporting deterrence theory, researchers began focusing on

the relationship between the perception (as opposed to the actual level) of the severity and

certainty of punishment and self reported, prior delinquency/criminal behavior (Paternoster 1987).

If individuals perceive that their criminal behavior is likely to be punished and that the punishment

is perceived as severe, the less likely it is that these individuals will engage in future criminal

activities. Some studies examining the deterrent effect of the perceived certainty o f punishment

produced such an inverse relationship, but the relationship itself was weak. Most of the twenty-five

studies published since 1972 that Paternoster reviewed looked at only bivariate relationships

between respondents’ estimations of the certainty of punishment and their self-reported

participation in delinquency and examined only cross-sectional data (Paternoster 1987). Most of

the fourteen studies published since 1972 examining severity revealed a positive relationship

(Paternoster 1987), a finding that contradicts deterrence theory.

There are two problems with studying the relationship between perceptions and prior

criminal behavior. The first is the difficulty in establishing temporal order. An inverse relationship

could possibly be the result of an "experiential effect" in which prior involvement in criminal

behavior reduces an individual's perception of the severity and/or certainty of punishment

(Saltzman et al. 1982).

24


There have been studies conducted to determine the extent that prior involvement in

criminal behavior influences perceptions of risk, but the results are mixed (e.g., Cohen 1978;

Piliavin e t al. 1986; Richards and Tittle 1981). Most of these studies include samples of college

students with limited involvement in criminal behavior, particularly serious criminal behavior

(Homey and Marshall 1992). A study carried out by Homey and Marshall (1992) that included a

sample of incarcerated felons reveals that an inverse relationship does exist between prior criminal

involvement in a particular offense and the perception of risk of arrest for that particular offense.

A second potential problem with perceptual deterrence research is that the relationship

itself is spurious. Studies that control for informal social control factors (Le., social bonds and

moral beliefs) reveal that these other sources of social control are more important in determining

the deterrent effect of any criminal sanction (Paternoster 1987). Subsequent deterrence research

including measures of informal social control demonstrates that factors such as church attendance

and religiosity do in fact condition the deterrent effect of criminal sanctions. For example,

Bainbridge (1989) found a significant negative association between rates of church membership

and rates of crime. In their study of high school youths, Burkett and Ward (1993) found that

religious based beliefs condemning the use of marijuana are negatively associated with marijuana

use. Finally, in their study of the relationship between religiosity and criminal behavior, Evans et

al. (1995) establish that participation in religious activities serves as an informal social control

factor by inhibiting involvement in crime.

Arrest as a Deterrent for Domestic Violence

Although the original Minneapolis Domestic Violence Experiment found a deterrent

effect for arrest on domestic violence, only three of the six replication studies—Milwaukee (for long

arrest), Colorado Springs and Miami-Dade County—confirmed these results (Berk et al. 1992; Pate

et al. 1991; Sherman et al. 1992). Further, findings from Omaha, Charlotte, and Milwaukee (short

arrest) demonstrate that arrest produces a criminogenic effect in that recidivism rates increased

25


(Dunford et al. 1990; Hirschel et al. 1990; Sherman et al. 1992). These inconsistent findings

question w hether or not arrest is actually deterring future domestic violence.

W hat appears to be d ea r from the replication studies that conducted subgroup analyses

(Omaha, Milwaukee, and Colorado Springs) is that arrest appears to deter a portion of the

offender sample. Specifically, offenders who are employed or serving in the military appear to be

deterred from committing additional domestic violence offenses (Berk et al. 1992; Dunford et al.

1990; Sherman et al. 1992). Employment appears to be conditioning any deterrent effect for arrest.

Further, employment appears to have a contextual aggregate-level effect as well. Arrest "works" for

those offenders who are employed and/or live in communities with low rates of unemployment. A

deterrent effect has also been found among m arried individuals and for individuals residing in

cities with a high proportion of married people (Pate et al. 1991).

The mixed results from the replication studies could also be a result of differences across

jurisdictions in what happens to offenders subsequent to arrest. Arrest may work in some

jurisdictions if perhaps the jurisdiction has a high rate of conviction for domestic violence

offenders. Although this study does not examine the direct effects of jurisdiction differences

(because the study is conducted in one jurisdiction only), it does examine the effects of different

court dispositions on individual offenders.

Formal or Informal Social Control?

The finding that employment and martial status conditions the deterrent effect of arrest

for domestic violence raises the issue of whether or not such conclusion supports deterrence theory

or renders support for social control theory. In other words, what "counts" as a deterrent effect? A

debate currently exists among criminologists as to whether o r not social control factors, such as

employment and martial status, should be treated as intervening or extraneous variables (Akers

1990; Gibbs 1975; Grasmick and Bursik 1990; Williams and Hawkins 1989).

26


According to some criminologists (i.e., Grasmick and Bursik 1990; Green 1989; Nagin and

Paternoster 1991; Paternoster 1989) deterrence theory can be expanded to include "informal

deterrence." Informal deterrence refers to the perceived social costs (Le., stigma, an individual’s

own feelings of guilt, loss o f respect; or even loss of a significant other or employment) that would

prevent an individual from engaging in criminal activities (Grasmick and Bursik 1990; Green 1989;

Nagin and Paternoster 1991; Paternoster 1989).

Grasmick and G reen (1980) offer three sets of "exhaustive factors" that serve to inhibit an

individual from engaging in criminal and/or delinquent behavior: moral commitment, social

disapproval, and the threat o f legal punishment. Grasmick and Green (1980) further argue that all

of these factors fit into a deterrence framework. Moral commitment refers to an internalization of

conventional or legal beliefs. Social disapproval includes fear of disapproval from an individual’s

family or friends. Threat of legal punishment involves an individual’s perceived certainty and

severity of criminal justice punishment. All three measures have been shown to have significant

direct effects on an individual’s involvement in illegal behavior (Grasmick and Green 1980).

Support for the two informal deterrence variables—social disapproval and moral commitment—have

also been found to have a significant direct effect for explaining an individual’s involvement for

driving under the influence (Green 1989).

Williams and Hawkins (1989) argue that the deterrent effect of informal sanctions may be

precipitated by formal criminal justice sanctioning. In their study of the deterrent effect of arrest

for spouse abuse, Williams and Hawkins (1989) found that arrest may deter future domestic

violence because some individuals anticipate negative social consequences in addition to the

negative consequences of arrest itself. Williams and Hawkins (1989) include three sets of informal

deterrence factors. The first involves commitment costs or an individual’s perception that their

"stake in conformity" may be threatened if arrested for spousal assault. Commitment costs would

include an individual’s loss o f educational or employment opportunities. Attachment costs refers to

27


the loss of significant others resulting horn an arrest. Finally, the stigma of arrest involves damage

to an individual’s reputation if arrested for spousal assault.

Nagin and Paternoster (1991) failed to find strong consistent support for Williams and

Hawkins (1989) study in their own research. Although they found modest support for an

interaction between perceived certainty of being caught committing an illegal offense and

commitment costs, a significant and negative direct effect was found for perceived certainty by

itself.

Williams and Hawkins (1992) further offer a measurement strategy for testing deterrence

theory that distinguishes between the social costs of crime (loss o f attachments and respect from

significant others and loss of respect) and those costs produced by legal sanctions. The authors

asked a panel of male respondents to estimate the likelihood of losing their spouse, respect from

their friends, and respect for themselves if they committed an act of domestic violence; they were

then asked to estimate the same likelihoods if they were actually arrested for domestic violence

(Williams and Hawkins 1992). The purpose of the distinction was to determine if the two estimates

are empirically distinct from one another. The study revealed that although the two estimates are

related to one another, they are empirically distinct. Based on their responses, the males in the

sample perceived arrest as a severe criminal sanction in terms of the consequences arrest might

pose for their social stability and self-esteem (Williams and Hawkins 1992). Further, the perceived

consequences of loss of self-esteem are negatively related to self-reported involvement in domestic

violence (Williams and Hawkins 1992). Williams and Hawkins (1992) conclude that research on

deterrence theoiy should include measures of informal social control processes as intervening

variables.

Akers (1990) argues that tests of deterrence theory should focus only on those variables

measuring the deterrent effects of form al social control. The finding that informal social control

variables condition the deterrent effect of punishment offer support for social bonding theoiy, not

deterrence theoiy. This study will not attempt to address this debate among criminologists. The

28


study is offered only as a test of social control theory, not as a test of deterrence theoiy that

includes informal social control variables as measures of informal deterrence. It is recognized,

nonetheless, that any findings supporting the importance of informal social control variables could

potentially render support for this expanded concept of deterrence theory. Such a direct test,

however, would require data pertaining to an offender’s attitudes and perceptions of the negative

social consequences as well as the anticipated negative consequences derived from formal criminal

justice sanctioning. For example, past studies (e.g., Grasmick and Bursik 1990; Grasmick and

Green 1980; Green 1989) involved asking respondents such questions as "how big a problem would

a particular punishment create for your life?" and "how would the adults that are closest to you

feel if they knew that you drove your m otor vehicle while under the influence of alcohol?".

The assumption that some individuals refrain from criminal behavior as a result of

informal social control processes and that individuals differ with respect to these informal social

control processes exemplifies arguments put forth by social control theorists. Social control theory

grew out of the work of early Chicago School criminologists in which crime is believed to be the

result of a breakdown in neighborhood organization which in turn results in a deterioration of

informal social control processes.

Social Control Theory

The idea that crime is caused by a breakdown in informal social control represents a

dominant theme in field of criminology. Not only do several variations of control theory exist, but

elements of control theory are found in other sociological theories as well.

The concept of informal social control was first mentioned by Durkheim (1933) in his

theory of anomie. Durkheim (1933) defined anomie as relative normlessness in society that occurs

from the destruction of the fundamental bonds that unite individuals in a collective social order.

The consequence is that individual members are forced to solve problems on their own. Social

29


solidarity is maintained by two factors: integration or cohesion and regulation--the controls which

bind individuals to the norms and values of society (Durkheim 1933).

Control theory per se, grew out o f the work o f Shaw and McKay from the Chicago school.

According to their theory o f social disorganization, one o f the consequences of social

disorganization is a breakdown in informal social control (Shaw and McKay 1942). Institutions

such as the family, school, and church lose their ability to exercise informal social control over the

individual members of a neighborhood (Shaw and McKay 1942). Parents are unable to m eet their

children’s needs and lose their ability to effectively socialize their children.

Control theories offer a different approach to crime causation compared to traditional

theories (strain, differential association, and labeling). Control theorists attempt to explain

conformity to the rules and norms of society, not deviancy. Deviant behavior is a given, conformity

must be explained. Social control theory offers an explanation of both individual criminal behavior

and explains variations in rates of crime across communities. What follows is a summary o f social

control theory at the individual level beginning with Albert Reiss (1951) and ending with Sampson

and Laub (1993). Following this summary is a synopsis of the aggregate-level social control

theories, including Shaw and McKay’s social disorganization theoiy, Robert Sampson’s work on

community informal social control processes, Braithwaite’s theory of reintegrative shaming, and

Hagan’s concept of social capital.

Individual-Level Social Control Theories

Reiss’s Theory of Personal and Social Control

The first sociologist after Shaw and McKay to pick up on the idea of crime resulting from

a breakdown in control was Albert Reiss in 1951. Reiss (1951) argued there are two types of

control: personal and social. Personal or internal control refers to the ability of an individual to

resist meeting his or her needs in ways that conflict with society’s norms and values. Social or

30


external control refers to the ability of social institutions and groups to make rules and norms

binding on the individual members. Delinquency is a product of a breakdown in either type of

control o r both (Reiss 1951).

Toby’s Stake in Conformity

About the same time control theory was being proposed, Jackson Toby (1957) introduced

his stake in conformity hypothesis. Toby’s hypothesis was in response to claims made by social

disorganization theorists that neighborhood factors such as high rates o f poverty, transiency, and

heterogeneity explain rates of delinquency (Shaw and McKay 1942). According to Toby (1957),

such factors are insufficient in their explanation because they fail to explain why some individuals

residing in socially disorganized areas do not become delinquent. Individuals differ with respect to

their stake in conformity. Some individuals have little or nothing to lose by committing crimes. As

such, individuals with a low stake in conformity are less likely to be deterred by formal criminal

sanctions.

Toby’s stake in conformity hypothesis emulates control theory with the idea that

individuals who do have a significant stake in conformity are insulated from the enticements of

illegal behavior. Further, Toby (1957) asserts that communities comprised of large proportions of

individuals with low stakes in conformity (unemployment, poverty, less educated individuals) will

have higher rates of delinquency than communities with low proportions. Toby’s hypothesis offers

not only an explanation of an individual’s involvement in delinquency, but rates of delinquency as

well.

Nve’s Family-Focused Theory of Social Control

Ivan Nye (1958) took a different approach with his control theory by focusing on the role

of the family as a source of control. Nye’s (1958) family-focused control theory asserts that the

family is the primary socializing agent. As such, the family is responsible for supplying four types

31


of control: direct control, indirect control, internalized control, and alternative means of need

satisfaction. Direct control refers to external restraints placed upon a child (i.e., a curfew). Indirect

control is exercised by a parent when a child obeys the rules out of respect fo r authority.

Internalized control refers to an individual’s own internalized values and sense of conscience.

Finally, a family provides alternative means of need satisfaction by providing a child with the

means to satisfy his or her needs in socially acceptable ways.

Reckless’s Containment Theory

In the early 1960s, W alter Reckless proposed his version of control theory known as

containment theory. Reckless (1961) observed that not everyone who is exposed to a disease

actually catches that disease. The same is true for delinquency. Not all individuals exposed to

delinquent influences becomes a delinquent. Immunity is a m atter of control, of being "contained"

or "restrained" from the temptation of crime. These control or containment factors are of two

orders: outer containment and inner containment (Reckless 1961).

O uter containment refers to social pressures to conform and includes training in roles,

affiliation with a community, and a sense of tradition. Outer containment serves a defense against

crime because there are community standards condemning anti-social misconduct, training in

obedience to these standards, and little competition from antisocial values. Reckless further

stressed the importance of meaningful activities, reasonable limits and support, reinforcement, and

acceptance from personal relationships.

Inner containment refers to self control over an individual’s behavior. The presence of self

o r internal control is indicated by four factors: a perception of one’s self as a law abiding citizen;

reasonable goals; an ability to handle frustration constructively; and the acceptance and

internalization of conventional norms and values.

32


Matza’s Drift Theory

In the 1950s, Gresham Sykes and David Matza argued that there are five "techniques of

neutralization" that delinquents use to protect themselves from self blame. These techniques of

neutralization provide rationalization for delinquent behaviors and weaken the ability of social

control agents to effectively control behavior (Sykes and Matza 1957). The techniques of

neutralization include the following rationalizations: denial of responsibility (the delinquent

attributes his or her conduct to forces beyond his or her control, for example, a bad neighborhood

or delinquent peer influences); denial of injury (the delinquent fails to see the harm done by his or

her conduct because the harm is indirect, for example, vandalism); denial of victim (the delinquent

views his o r her conduct as a form of punishment or rightful retaliation); condemnation of

condemners (the delinquent focuses attention away from his or her conduct to the motives of

authorities by arguing that "everyone is picking on me"); and appealing to higher loyalties (the

delinquent makes the argument that his or her conduct was not a form of self-interest but was for

the benefit of an individual’s peer group or for some higher cause) (Sykes and Matza 1957).

A few years later Matza incorporated these techniques of neutralization into his drift

theory of delinquency. Matza (1964) argued that delinquency is a m atter of drift in which

juveniles are no more committed to delinquency than they are to conformity. Rather, they are

"transiently, intermittently, and casually" involved in delinquent conduct. Drift lies midway between

freedom and control. The techniques of neutralization only make delinquency possible. There are

two triggering factors that explain the actual occurrence of delinquency: preparation and

desperation. According to Matza, preparation is the process in which the juvenile realizes that a

particular delinquent act is possible, that he or she is capable of the act, and that he or she can

deal with any fear of apprehension. Desperation refers to a deep feeling of fatalism in which the

juvenile attempts to assert his or her own individuality by committing crime (M atza 1964).

33


Hirschi’s Social Bond Theory

In 1969, perhaps the most well known control theory was put forth by Travis Hirschi in his

Causes o f Delinquency. Hirschi’s social bond theoiy asserts that conformity can be explained by an

individual’s bond to society. This bond is comprised o f four related elements. A weakness of any

one element is sufficient to explain delinquency'. However, if an individual is weak on one bond,

they will tend to be weak on all bonds (Hirschi 1969).

The elements are as follows: attachment, commitment, involvement and belief. Attachment

refers to the extent an individual is emotionally invested in his or her family and friends.

Commitment refers to the extent an individual is invested in conventional activities. Involvement

refers to the amount of time and resources an individual invests in conventional activities and with

his or her family and friends. Finally, belief is the element that refers to the extent to which an

individual subscribes to and adopts the rules and norms o f society (Hirschi 1969).

Hirschi stressed the importance of the family in his theoiy by emphasizing the importance

of spending time with children, intimate communication, and affectionate identification. Hirschi

also believed that if the bond was strong, regardless of social class, race, or delinquent peer

influences, delinquency was less likely to occur.

Hirschi’s theory was put forth to eiqilain many different domains of criminal behavior

including both personal and property offenses. Hirschi’s own research examining the effects of the

bonding elements on delinquency appeared to support his theory, particularly the elements of

attachment, commitment and belief. Subsequent research, however, has found that the elements of

the social bond are only moderately related to less serious forms o f delinquency (Krohn and

Massey 1980). Further, associations with delinquent peers, regardless of the strength of an

individual’s bonds, is still a strong predictor of delinquency (Junger-Tas 1992; Akers and Cochran

1985). A longitudinal analysis of Hirschi’s theoiy by Agnew (1985) reveals that control variables

explain only one to two percent of the variation in self-reported delinquency making it difficult to

34


determine if weak bonds influence delinquency or whether o r not participation in delinquency

activities weakens the bond.

Sampson and Laub’s Adult Social Bond Theory

Hirschi’s social bond theory was originally offered as an explanation for delinquency.

Robert Sampson and John Laub (1993) have recently put forth a control theory to explain adult

criminal behavior that integrates Hirschi’s social bond theory with factors emergent over an

individual's life course. Sampson and Laub’s theory combines elements of control theory with

changes that occur over the life course of an individual. Their basic theory states that a lack of

informal social control exerted from parents and the school as well as some possible individual

difference constructs (for example, difficult temperament), explain juvenile delinquency (Sampson

and Laub 1993). Although behavior is fairly stable throughout the life course, a lack of informal

social control exerted from the bonds of employment and marriage could account for adult

criminal behavior. Sampson and Laub make the argument that prior delinquent involvement only

increases the propensity for adult criminal behavior. The actual occurrence depends on the

strength of bonds formed in adulthood. Changes in the life course—for example, quality

employment and a quality marriage—can redirect individuals onto a path of conformity, regardless

of prior delinquent involvement.

Although the research on Hirschi’s social bond theory as a whole tends to suggest only

moderate to weak relationships between social control factors and delinquent involvement, these

factors appear to be significant predictors of adult criminal behavior. Sampson and Laub (1993)

found that adult social bonds in the form of job stability and marital attachment are strong

predictors of criminal behavior. This bond can even be significant enough to negate the effects of

prior delinquent involvement.

Other studies examining adult social bonds have produced similar results. Lasley (1988)

tested Hirschi’s control theoiy on a population of automobile executives and found support for

35


control theory as an explanation of white-collar crime. Williams and Hawkins (1989) examined the

four elements o f the social bond as they apply to non-offending spouse abusers and found that

attachment and belief provide an understanding of why some men refrain from such behavior.

Although this study is not intended to offer a direct test of any of the specific versions of control

theory discussed above, it is designed to tap into the general concepts drawn from a variety o f

control theories. Specifically, the study will draw concepts from Toby’s stake in conformity

hypothesis, Hirschi’s social bond theory and Sampson and Laub’s adult social bond theory. Adults

with a significant stake in conformity (defined in terms of the elements of the social bond) are

believed to less involved in criminal behavior. Toby’s theory has never been fully operationalized

and tested directly, however, he does state that individuals with a significant stake in conformity

may be insulated from the enticements of illegal behavior. Further, it is recognized that some of

the factors that are explored in the study, such as martial status and employment, can be

considered structural antecedents of informal social control, not informal social control processes

in and of themselves. These factors are only offered as proxy measures for Toby’s stake in

conformity hypothesis.

Hindelang (1973) pointed out the types of variables mentioned above do not tap elements

o f social control theory directly due to the social psychological nature of these elements. For

example, to say that person who spends more time at a particular job is more "committed" to that

job is not necessarily true, and we cannot know the exact degree of commitment without

understanding the person’s attitude towards his/her job. Therefore, these measures are not perfect

operational concepts of the theoretical concepts. It is assumed, however, that there is some overlap

between the two (i.e., persons who have worked longer at particular jobs are more likely to be

m ore committed to their jobs than their counterparts).

36


Aggregate-Level Social Control Theories

Social control theory can be used to explain not only why individuals commit crimes, but

differences in rates of crime across jurisdictions. The informal social control processes which serve

to "insulate" some individuals from the enticements of crime also operate on a larger scale at the

aggregate level. Communities comprised of a significant proportion of "insulated" individuals

(employed and married) are likely to have lower crime rates than communities comprised of a

significant proportion of their counterparts (unemployed and single) (Berk et al. 1992; Dunford et

al. 1990; Sherman et al. 1992). The differences in these proportions can explain why some

communities have higher rates of crime than others.

Social Disorganization Theory

The idea that informal social control processes operate on an aggregate level and that

variations in these processes can explain rates of crime and delinquency forms the foundation of

social disorganization theory. Social disorganization theory grew out of the work of social

ecologists Robert Park and Ernest Burgess of the Chicago School of criminology. Park argued that

the development and organization of a city can be understood in terms of four social processes:

invasion, conflict, accommodation, and assimilation (Park et al. 1925). Further, a study of these

social processes is essential in any explanation o f criminal behavior. Burgess describes the growth

of a city in terms of "concentric zones". Cities grow outward from the central business district. The

area immediately outside of a city’s central business district constitutes the "zone-in-transition," an

area characterized by high rates of crime, mental illness, and suicide.

Clifford Shaw and Henry McKay (1942) built upon Park’s model by making the

observation that rates of officially recorded delinquency were highest in the "zone-in-transition,"

with few incidents of delinquency occurring in areas farthest away from the central business

district. Neighborhoods in the "zone-in-transition" were comprised of high rates of poverty,

transiency, and heterogeneity in terms of ethnic and racial groups. As a result o f the high rates of

37


poverty, transiency, and heterogeneity, neighborhoods become "disorganized." A consequence of

social disorganization is that informal social control institutions—family, churches, and schools—lose

their ability to exercise control over their members and crime flourishes. Shaw and McKay also

observed that these "disorganized" neighborhoods contain high rates o f repeat offenders.

According to social disorganization theory, neighborhood characteristics, as opposed to

individual characteristics, can either prevent or promote criminal behavior. By the late 1960s,

social disorganization theoiy was essentially dismissed by social scientists as being "marginal to

modem criminological thought." (Bursik 1988:519). Instead, social scientists diverted their

attention to theories explaining individual criminal behavior (Sampson 1986a). Social

disorganization theory’s diminished popularity had more to do with a lack of research testing its

theoretical concepts as opposed to research findings that refuted the theoiy. Further, research

patterned after the social disorganization tradition was fraught with several limitations. First, most

of the research on cities and crime relied primarily upon intra-city studies of crime and

delinquency (Bordua 1958; Chilton 1964; Lander 1954; Shaw and McKay 1942). Research

examining variations in crime rates within cities remained scarce (Sampson 1986a). Second, was a

lack of focus on cities as units of social control. Researchers tended to limit their focus to studies

of race and class stratification (Sampson 1986a). Finally, ecological studies examining the effects of

criminal justice sanctions as a source of community social control relied on aggregate measures of

punishment certainty and severity at the state level (Paternoster 1987; Sampson 1986a). Such

studies failed to explore the possible deterrent effect of criminal justice sanctions within a

community as well as ignoring any compositional and contextual effects (Sampson 1986a).

Community characteristics could also be used as explanatory factors for the behaviors of

individuals.

Interest in social disorganization was renewed during the 1980s and several empirical

studies of ecological theories were conducted (e.g., Blau and Blau 1982; Bursik 1988; Bursik and

Webb 1982; Sampson and Groves 1989; Warner et al. 1993). R obert Sampson’s research on

38


aggregate-level sources o f formal and informal social control offers a significant contribution to

ecological studies of crime and delinquency.

Examining National Crime Survey data, Sampson (1985) found that family structure,

residential mobility, and structural density had strong effects on personal victimization, while

percentage of female-headed households had strong effects on theft victimization. Further,

residential mobility produced the strongest effect on violent victimization (Sampson 1985).

Sampson (1986a) examined the effects of formal social control within a community, as

measured by police aggressiveness, risk of jail incarceration, and risk of prison incarceration rates

of homicide and robbery. Sampson (1986a) found that the deterrent effect of criminal sanctions

were conditioned by type of crime and offender characteristics (race and age). Criminal justice

sanctions appeared to have a deterrent effect for robbery, but not homicide. In fact, as the risk of

state incarceration for homicide increased, so did homicides rates (Sampson 1986a). Risk of

incarceration produced the greatest deterrent effect for juvenile and youthful offenders (both white

and black), while police aggressiveness appeared to have the greatest deterrent effect for black

adult robbery (Sampson 1986a). Further, Sampson (1986a) included a measure of family structure

(measured by divorce rates and percentage of married couple households) and found that family

disruption is a strong predictor of homicide rates. Cities with high divorce rates have higher white

homicide rates than their counterparts, while cities with significant proportion of black two-parent

families have lower black homicide rates than their counterparts (Sampson 1986a). Both measures

of family disruption produce significant effects on juvenile robbery, but the effect of divorce is

insignificant for white juvenile robbery offenders (Sampson 1986a). For adult robbery offenders,

the divorce rate produces the greatest effect for white offenders, while both measures have strong

effects for blacks (Sampson 1986a).

Sampson (1986b) argues that studies of delinquency should include both aggregate-level

and individual-level measures of socioeconomic status. Sampson (1986b) found that neighborhood

SES has a significant inverse effect on police contacts separate from self-reported delinquency,

39


while an inverse relationship exists between individual SES and court referrals separate from self

reported delinquency and police records.

Additional measures of community organization examined by Sampson (1987) include sex

ratios within a neighborhood. Sampson (1987) found a positive relationship between scarcity of

employed black males relative to black women and the prevalence of single female headed black

families in black communities, which in turn was positively related to rates of black murder and

robbery.

Sampson and Groves (1989) argue that there are certain factors that mediate the effects of

community structural characteristics on crime. A lack of friendship networks, community members’

perception of a lack of control over street com er gangs, and a low prevalence of organizational

participation were shown to mediate the effects of community structural characteristics put forth

by Shaw and McKay (poverty, residential mobility, and heterogeneity). Neighborhoods with few

friendship networks, unsupervised teenage peer groups, and low organizational participation did

have significantly higher rates of crime and delinquency compared to their counterparts (Sampson

and Groves 1989).

The Contextual Effects of Neighborhood Disorganization

Social disorganization theory as well as subsequent ecological theories of crime and

delinquency were put forth as aggregate-level theories to explain rates of crime and delinquency.

Structural characteristics of a community such as family disruption, residential mobility,

neighborhood socio-economic status were variables found to be related to the crime rate. A recent

body of research, however, renders support for examining the structural characteristics of a

neighborhood as influences on the behavior of individuals (Elliott et al. 1996; Gottfredson et al.

1991; Gunn et al. 1993; Simcha-Fagan and Schwartz 1986). It has been hypothesized that the

structural characteristics of a neighborhood can produce contextual effects on the behavior of

individuals (Sampson 1991; Sherman 1992).

40


Simcha-Fagan and Schwartz (1986) found that community disorganization, measured in

terms of family disorganization and neighborhood economic levels, maintains a significant

relationship with both self-reported and official rates of delinquency.

Sampson (1991) also provides an examination o f the contextual effects of community

characteristics on individual behavior. Specifically, Sampson (1991) looks at length of residence

and community residential stability and the role these variables play on an individual’s attachment

to community. Sampson (1991) found that an individual's length of residence as well as community

residential stability increase an individual’s attachment to a community by increasing an

individual’s friendships which in turn is correlated with self-reported victimization.

Gottfredson et al. (1991) create a disorganization factor defined by a high proportion of

female-headed households, high proportion of families on welfare and with incomes below the

poverty level, a high divorce rate, and a low level of male employment. Further, Gottfredson e t al.

(1991) create a second aggregate-level measure called affluence and education, defined by a high

proportion of families with incomes above the national median income, a high proportion of

persons employed in professional and managerial occupations, a high proportion of persons with a

high school degree, a high proportion of employed females, and a low ratio of families with farm

income to families with earnings from wages and salaries. The association between the two factors

was found to vary by gender and crime type. Disorganization was found to the positively related to

interpersonal aggressive crimes, however, the effects disappeared for males once individual

background measures (i.e., race and social class) were included in the model. The SES factor

maintained a significant negative affect for male theft and vandalism. Males in more affluent

neighborhoods report more theft and vandalism than males in less affluent neighborhoods. No

relationship was found for SES and female involvement in delinquency (Gottfredson et al. 1991).

Elliott et al. (1996) found that disadvantaged neighborhoods, defined by high proportions

of poverty, mobility, single-parent families, and ethnic diversity, weaken neighborhood informal

social control processes, which in turn increases the likelihood of delinquency. The combined

41


effect of aggregate and individual level influences was greater than the separate direct effects of

each.

Upon reviewing the inconsistent findings rendered from the replication studies on the

deterrent effect of arrest for domestic violence recidivism, Sherman (1992) hypothesized that

informal social control processes, such as employment and marital status, could also operate at the

aggregate level. That is, the higher the proportion of married and/or employed individuals residing

in a community, the greater an individual’s stake in conformity might be. Further, these individuals

may be more likely to be deterred through formal criminal justice sanctioning (Sherman 1992).

Sherman’s hypothesis ties into Sampson’s finding regarding community attachment. The argument

can be made that the structural characteristics of a neighborhood influence an individual’s

attachment to their community and that the stronger an individual’s attachment is to their

community, the greater will be that individual’s stake in conformity. Such individuals have more to

lose than their counterparts and are then more likely to be deterred through formal criminal

justice sanctioning.

Braithwaite’s Theory of Reintegrative Shaming

Labeling theorists argue that society’s reaction to crime is negative for an offender, while

deterrence theorists assert that society’s reaction is necessary to prevent additional criminal

behavior. John Braithwaite’s (1989) theory of crime, shame, and reintegration involves a synthesis

of ideas drawn from both the labeling and deterrence perspectives. Braithwaite (1989) makes the

distinction between societal reaction (shaming) that leads to stigmatization and pushes offenders

toward criminal behavior versus societal reaction that is reintegrative and controls crime.

Braithwaite’s theory also draws from subcultural and social control theories. Stigmatization

weakens an individual’s social bonds and increases the likelihood that a criminal subculture will

appeal to an individual (Braithwaite 1989). Social bonds are maintained with reintegrative shaming

which serve to insulate an individual from the criminal subculture (Braithwaite 1989).

42


The key concepts of Braithwaite’s theoiy are interdependency and conununitarianism.

Criminal sanctions or formal social control can produce different results depending on individual

level and community level characteristics (Braithwaite 1989). Interdependency can best be

understood in terms of an individual's attachment to parents, school, neighbors, and employer.

Individual level characteristics such as age, gender, martial status, education and employment

influence an individual's degree of interdependency. Communitarianism is influenced by the

degree of urbanization and residential mobility within a community (Braithwaite 1989).

Reintegrative shaming (shaming that controls crime) works best in communities containing

significant proportions of mutually dependent individuals.

Hagan's Theory of Crime and Disrepute

John Hagan’s (1994) theory o f crime and disrepute is an extension of conflict theory in

which social inequality in America breeds criminal behavior. According to Hagan (1994), when

inequality is increased in society during periods of economic decline, crime flourishes, particularly

among the lower classes. Social inequality coupled with a declining economy generates a process of

"capital disinvestment" caused by residential segregation, race-linked inequality, and concentrations

of poverty (Hagan 1994). These processes o f capital disinvestment discourage the creation of social

capital. Social capital refers to the capabilities and efforts of individuals, families, and groups to

solve commonly experienced problems (Hagan 1994). Further, social capital involves an investment

in other individuals and in an individual’s larger community (Hagan 1994). The concept of social

capital is analogous to Shaw and McKay’s (1942) idea of social organization and Braithwaite’s

(1989) concepts of interdependency and communitarianism.

Social capital can be used by individuals, families, and groups to acquire cultural capital.

Cultural capital refers to higher education, cultural opportunities, and cultural involvement (Hagan

1994). Individuals residing in communities characterized by an abundance of social and cultural

capital are more likely to subscribe to and adopt conventional values, and behaviors and

43


communities comprised of a significant proportion of individuals possessing an abundance of social

and cultural capital are likely to have low rates of crime (Hagan 1994). Individuals without such an

abundance of social and cultural capital are likely to "recapitalize" and seek out unconventional,

including criminal, means of achieving their goals (Hagan 1994). Communities comprised of a

significant proportion of such individuals are more likely to have higher rates of crime and

delinquency than their counterparts (Hagan 1994).

This study is not intended as a direct test of any of the specific above mentioned

aggregate-level control theories. Instead, the study includes aggregate-level variables drawn from

these aggregate-level theories as a whole. These variables are only intended as proxy measures for

concepts drawn from the aforementioned control theories. Aggregate-level measures are offered as

a test o f the "community contextual effect" hypothesis derived in part from social disorganization

theory, Sampson’s work on community-level informal social control processes, Braithwaite’s

concepts of interdependency and communitarianism, and finally Hagan’s idea of social capital.

Alternative Theoretical Explanations

The individual-level informal social control hypotheses are derived from social control

theory, particularly Toby’s idea of stake in conformity. Individuals with a significant stake in

conformity are hypothesized to have fewer rearrests for domestic violence than their counterparts.

W hether or not an offender is employed, the length o f employment, type of employment (skilled

or unskilled), level of education, and economic status are some of the variables included in the

study that are offered as proxy measures of Toby’s stake in conformity hypothesis. The

aforementioned measures, however, also may be seen as proxies for concepts offered by

criminological theories other than control theory. As mentioned previously in this chapter, these

variables could represent proxy measures derived from deterrence theory, particularly the concept

of informal deterrence offered by Grasmick and Bursik (1990), Green (1989) and Nagin and

Paternoster (1991). Although this study is not intended as a direct test of deterrence theory,

44


support for these measures could potentially be construed as rendering support for deterrence

theory or at least for certain specific concepts derived from the theory.

The above-mentioned measures could also represent concepts drawn from the field of

feminist criminology, most notably the work of Messerschmidt (1993). In his book Masculinities

and Crime, Messerschmidt offers an explanation for two of the most well documented trends in the

study of domestic violence: the fact that males are grossly over-represented as perpetrators of

domestic violence (Dobash et al. 1992) and that domestic violence is highly concentrated among

the lower classes (Messerschmidt 1986).

Feminist theory, a derivative of conflict theory, asserts that social interactions, including

interactions within the family, are shaped by divisions of labor and power in society. According to

Hagan et al. (1987), the increase in the number of females working outside of the home has

resulted in a shift from a primarily patriarchal family structure to an egalitarian one. Despite this

trend, females are still primarily responsible for child-rearing and household labor (Hochschild

1992). This division of labor within the family serves to increase a husband’s power to define the

family structure according to traditional patriarchal convictions (Messerschmidt 1993). Husbands

continue to make most of the important decisions within the household and are also still

considered to be primary initiators of sexual relations (Connell 1987; Komter 1989). It is within a

patriarchal family structure that husbands are more likely to use violence against their spouse as a

means of controlling their spouses and reaffirming their masculinity (Messerschmidt 1993).

Spousal abuse occurs in all types of homes and among all socioeconomic classes, however,

its occurrence is over-represented among the lower classes, and most particularly among lower-

class households in which the patriarchal division of labor and power is the most pronounced

(Messerschmidt 1986; Straus et al. 1980). Within households in which the husband is either

unemployed or employed part-time, spousal assault is three times more likely to occur than within

households in which the husband is employed full-time (Straus et al. 1980). Furthermore, domestic

assault occurs twice as often within lower-class households than within middle-class and upper class

45


households (Straus et al. 1980). A study o f risk factors associated with domestic violence

conducted by Smith (1990) further revealed that males with low incomes, less education, and

employed in low-status jobs were significantly more likely than their counterparts not only to

engage in domestic violence but also to subscribe to conventional patriarchal beliefs. These types

of males are not likely to be in a position to assert their power and authority at work, and so may

be more likely to assert their power and authority through violence within the home

(Messerschmidt 1993). Unemployment serves to undermine a male’s status of "provider" for the

family, and it is these males who are most likely to abuse their spouse as a way to reaffirm their

masculinity (Ferraro 1988; Messerschmidt 1993).

Employment, length of employment, type of employment (skilled or unskilled), level of

education, and economic status are variables that are included in the study as proxy measures of

Toby’s stake in conformity hypothesis. These measures, however, could potentially proxy concepts

drawn feminist theory and could be used to explain why males commit significantly more acts of

domestic violence than females and why domestic violence is heavily concentrated among the

lower classes. Although support for these measures could potentially render support for feminist

theory (particularly the ideas of Messerschmidt), this study will not attem pt a direct test of the

aforementioned concepts. Such a test would require both victim interview data as well as

information pertaining to an offender’s attitudes toward gender roles and expectations. In addition,

the concept of patriarchy has yet to be fully conceptualized and measured in past research (Akers

1997), and information relating to this concept is simply not available for this study which relies on

official court data.

Regarding the interactions between the individual-level informal social control variables

and the formal criminal justice sanctions, one could hypothesize that offenders who are either

employed in low-skilled positions or unemployed may perceive formal sanctioning as a further

threat to their masculinity and would thus be more likely to be rearrested for domestic violence

recidivism than their counterparts. The escalation effects for unemployment and arrest that have

46


been found in previous research on domestic violence recidivism were attributed to social control

theory. However, an interesting alternative and potential topic for future research would be to

examine such interactions from a feminist perspective.

Conclusion

Informal social control processes serve to insulate individuals from the enticements and

perceived benefits of some types of crime. Informal social control processes include, but are not

limited to, an attachment to family and friends, a commitment and investment in conventional

social control institutions such as school and work, opportunities for conventional behavior, and a

belief in and an internalization of society’s conventional values and rules. Individuals possessing

these characteristics are less likely to engage in criminal and delinquent behavior than individuals

who do not possess these characteristics. According to Toby (1957), these individuals are believed

to have a significant stake in conformity.

Further, informal social control processes are hypothesized to operate on both an

individual level and aggregate level. Researchers examining the contextual effects of neighborhood

characteristics on an individual’s behavior have found that individuals residing in communities

consisting of a significant proportion o f individuals who are educated, employed, interdependent,

and involved in conventional activities are less likely to commit delinquent and/or criminal acts

than their counterparts (Elliott et al. 1996; Gottfredson et al. 1991; Sampson 1991; Simcha-Fagan

and Schwartz 1986). These communities can become informal social control processes in and of

themselves by providing members with legitimate ways to solve problems and attain goals.

Informal social control processes can also determine the effects of formal social control on

the effects of criminal justice sanctions. According to deterrence theory, the purpose of criminal

justice sanctions is to deter, that is, to prevent individuals or groups of individuals from engaging

in illegal behavior. Research has shown, however, that criminal justice sanctions can either

decrease, increase, or have no effect on behaviors depending upon the context in which the

47


sanction is administered. Toby (19S7) argues that individuals with a significant stake in conformity

may feel that they have more to lose through formal crim in a l justice sanctioning and are thereby

more likely to be deterred than their counterparts.

Research examining the deterrent effect of arrest for domestic violence reveals that arrest

only deters certain individuals and in some communities. Specifically, research suggests that arrest

deters domestic violence for married and/or employed individuals and individuals who live in

communities comprised of a significant proportion of married and employed individuals. A

possible explanation for the inconsistent results lies in examining the formal social control

processes operating at both the individual and aggregate level.

Two alternative theories that could explain some of the relationships that are explored in

the study include informal deterrence theory and feminist theory, however because of data

limitations, the current study is not intended to test concepts derived from these theories directly.

Chapter three outlines the research strategy for this study, which will examine interactions

between different court dispositions and informal social control variables (individual as well as

aggregate contextual measures) to determine the effect of criminal justice sanctions on domestic

violence recidivism. Specific interactions are discussed as well as how the present study builds upon

past research on domestic violence recidivism.

48


CHAPTER THREE

RESEARCH STRATEGY

Introduction

Subsequent to the published findings from the Minneapolis Domestic Violence

Experiment, mandatory arrest policies were extensively adopted across jurisdictions in the United

States (Sherman et al. 1992). Such policies were based on the experiment’s finding that arrest,

compared to separation and counseling, produced the lowest rates of recidivism among domestic

violence offenders (Sherman and Berk 1984). The experiment was replicated six times in five

different cities: Omaha, Charlotte, Milwaukee, Colorado Springs, and Miami. O f the six

replications, only three—Milwaukee, Colorado Springs, and Miami-Dade County-yielded results

consistent with Minneapolis (Sherman et al. 1992). Findings from the remaining studies suggested

a criminogenic effect in which arrested offenders actually had higher rates of repeat offenses

compared to offenders who were either counseled or separated from their partners (Sherman et al

1992).

The diversity in findings prompted social scientists to speculate as to why arrest appears to

"work" for some offenders and in some cities. A consistent finding across studies that conducted

subgroup analyses (Omaha, Milwaukee, and Colorado Springs) is that offenders who are employed

or serving in the military appear to be deterred after arrested for domestic violence (Berk et al.

1992; Dunford et al. 1990; Sherman et al. 1992). Further, a contextual effect for employment has

also been suggested. That is, offenders who reside in communities with high rates of employment

may be more likely to be deterred from future acts of domestic violence because of the stigma of

arrest (Sherman et al. 1992). A deterrent effect has also been found among married individuals

and for individuals residing in cities with a high proportion of married people (Pate et al.

49


The diversity in findings across replication studies also has been attributed to

methodological differences. None of the subsequent studies were exact replicas of the original

experiment. Differences include variations in how offenders were assigned to the treatm ent groups

and in who did the assignment of offenders as well as to differences across jurisdictions in the

amount of time offenders spent in custody (Sherman et al. 1992). Further, researchers (e.g., Berk

et al. 1992; Binder and M eeker 1988; G am er et al. 1995; McCord 1992; Sherman et al. 1992)

argue that the replication studies are fraught with limitations, and that until such limitations are

addressed the true deterrent effect of arrest for domestic violence will remain unknown.

Chapter three will focus on the limitations of prior research examining the deterrent effect

of arrest for domestic violence recidivism and on how the current study helps to overcome some of

these limitations. Although there are several limitations that have been discussed in the literature

(for a complete review see Berk et al. 1992; Binder and Meeker 1988; Gam er et al. 1995; McCord

1992; Sherman e t al. 1992), the only limitations that are addressed in detail are the specific ones

that the current study helps to overcome. It should be noted that the study is not intended as a

replication of the original Minneapolis Domestic Violence Experiment or any of the subsequent

domestic violence studies. This study does attempt, however, to overcome some of the limitations

o f past research studies. Accordingly, it may provide additional insight into the effectiveness of

criminal justice sanctioning on domestic violence recidivism.

Limitations of the Research on Arrest

and Domestic Violence Recidivism

After the published findings from the replication studies on the effectiveness of arrest for

domestic violence recidivism, several social scientists offered plausible explanations as to why arrest

"worked" for some offenders and in some communities. The inconsistent findings have been

attributed to differences both in the research designs across jurisdictions and in the characteristics

o f the study sites. For example, in some o f the jurisdictions individual police officers randomly

50


assigned cases to the treatment groups, while in others, a team of civilians made this decision

(Sherman et al. 1992). It has been suggested that officers may not have been willing to arrest a

"respectful" suspect even if arrest was the next disposition on the list (Binder and M eeker 1988;

Sherman et al. 1992). If so, demeanor of a suspect would become a factor in determining who is

arrested, and the experiment no longer would be truly random.

According to McCord (1992), what exactly constituted "counseling" and "separation" in

each of jurisdictions is unclear, and consequently the true effectiveness of these sanctions remains

unknown. Further, the amount of time an arrested offender actually spent in custody varied from

two hours to over twenty-four hours (Sherman et al. 1992). The research does suggest, however,

that the more time spent in custody upon arrest, the greater the deterrent effect (Sherman et al.

1992).

Differences in the Outcome of Arrest

It has also been suggested that the inconsistent findings across replication studies could be

a result of differences in the outcome of arrest across jurisdictions (Sherman et al. 1992; Gamer et

al. 1995; Zorza 1992). Not all offenders who were arrested were prosecuted. Of those offenders

who were prosecuted, not all were convicted. Further, variation exists in the sentences of those

offenders who were convicted. The Minneapolis Domestic Violence Experiment and subsequent

replications were designed to determine which police intervention—arrest, separation, or

counseling—would produce the largest decrease in domestic violence recidivism. Taken as a whole,

however, these experiments tell nothing about the effectiveness of other, and more punitive,

criminal justice sanctions. In fact, according to Kruttschnitt (1996), there has been no study that

addresses the effects of sentencing options on domestic violence recidivism. It is possible that any

deterrent effect of arrest could hinge upon what happens to an offender subsequent to arrest.

In this light, the current study will explore the effectiveness of criminal justice sanctions on

domestic violence recidivism beyond the initial arrest. Such an exploration is important for two

51


reasons. First, because many jurisdictions (including Cincinnati) operate under a mandatory arrest

policy for cases o f domestic violence, variation in the decision to arrest could be reduced1. Police

are required to arrest one, if not both, individuals once the police have been requested to respond

to a call for assistance.

Second, little is known about the effectiveness of other, and more punitive, criminal justice

sanctions on domestic violence recidivism. As stated previously, the only relationship explored in

the Minneapolis study and subsequent replications is that between arrest and time spent in custody

upon arrest. Little is known about the effectiveness of prosecution o ther than that mandatory

prosecution policies appear to result in higher conviction rates for domestic violence offenders

(Elliott 1989) There is a dearth of research, however, about w hether higher conviction rates result

in lower rates of recidivism and about whether different sentencing options such as incarceration,

probation, and/or treatment result in lower rates of recidivism. (Krutschnitt 1996).

Thus, the current study will examine the effectiveness o f these criminal justice sanctions on

domestic violence recidivism. Specifically, the criminal justice sanctions that are explored are as

follows: whether or not an offender is prosecuted for domestic violence; the effects of a conviction

for domestic violence; whether or not an offender is sentenced to jail and the amount of time

spent in jail; the effects of probation, fines, and/or domestic violence treatment.

Individual Differences in the Deterrent Effect of Arrest

As previously stated, only three of the replication studies—Milwaukee, Colorado Springs,

and Miami-Dade County—yielded results consistent with Minneapolis (Sherman et al. 1992). In the

remaining two jurisdictions—Omaha and Charlotte—arrest appeared to result in an escalation effect

in which offenders who were arrested were more likely to commit further acts of domestic violence

i

Although there is always some discretion involved in the arrest decision, this policy results in an eighty percent arrest rate for all such calls to the police.

52


(Dunford et al. 1990; Hirshel et al. 1990). In Milwaukee, an escalation effect was found for short

arrest (Sherman et al. 1992). Further, studies that conducted subgroup analyses found that

offenders who were either employed and/or married at the time o f arrest were more likely to be

deterred from future acts of domestic violence (Berk et al. 1992; Dunford et al. 1990; Sherman et

al. 1992). The inconsistent findings have led many researchers (e.g., Berk et al. 1992; Dunford et

al. 1990; Sherman et al. 1992) to infer that arrest only works as a deterrent in some jurisdictions

and for some individuals. Specifically, individuals with a "high stake in conformity" who have more

to lose by an arrest may be more likely to be deterred from committing additional acts of domestic

violence subsequent to an arrest for domestic violence.

On its surface, the "stake in conformity" hypothesis appears to offer a plausible

explanation for the inconsistent results stemming from the replication studies. According to Gam er

et al. (1995), however, such an explanation lacks experimental rigor and has only been applied post

hoc to the results of the replication studies. Sherman (1992) himself notes that the "stake in

conformity" hypothesis has yet to be tested directly with the existing data yielded by the replication

studies. Such a study would require pooling all of the raw data from each of the domestic violence

studies and calculating risk factors for different groups of individuals (for example, single and

unemployed) (Gam er et al. 1995; Sherman 1992).

An extensive analysis of risk factors has yet to be published from the domestic violence

studies. Using the data from Colorado Springs, Omaha, and Milwaukee, Berk et al. (1992) created

risk categories for offenders who were either employed or serving in the military. As hypothesized,

the authors found a deterrent effect for "good risk" individuals; that is, for individuals who were

either employed or serving in the military at the time of arrest (Berk et al. 1992). An escalation

effect was found for unemployed offenders. An analysis of the domestic violence data collected in

Milwaukee by Sherman et al. (1992) found that level of education, in addition to employment and

marital status, was significantly related to the incidence of repeat domestic violence. Employed,

married, and high school graduate offenders were all less likely to be involved in repeat offenses as

53


measured by "hotline" reports made by the police to a battered women’s shelter (Sherman et al.

1992).

The current study follows along the lines of the Berk et al. (1992) and Sherman et al.

(1992) studies by examining interactions between an extensive number o f risk factors and criminal

justice sanctions. These risk factors are designed to assess the concepts from a variety of control

theories, especially Toby’s concept of "stake in conformity." For example, Toby’s stake in

conformity idea was a predecessor to Hirschi’s commitment element of the social bond. Individuals

who are married, have children, employed, educated, and live at a permanent address are 1)

believed to have a higher stake in conformity, 2) are thereby hypothesized to have more to lose

through formal criminal justice sanctions than their counterparts, and 3) are more likely to be

deterred through formal criminal justice sanctions than their counterparts. Specifically, risk factors

were created using the following measures: marital status; number of children; family ties; length

at current residence; education; and employment, including the length of employment and type of

employment (see table 4.1 for a complete list of variables).

Further, an argument can be made that the aforementioned risk factors not only assess the

concrete status of employment but also offer a measure of the quality of such a status. Such

measures include the length of employment as well as type of employment. Sampson and Laub

(1993) argue that an important distinction should be made between individuals who are married

and employed and individuals who are happily married and hold down a job defined as meaningful.

The study is not intended as a direct test of Sampson and Laub’s ideas, because such an

investigation would require data on an offender’s attitudes. This study does include, however, a

more extensive list of stake in conformity measures than examined in previous research. Equally

important, the study is intended as a direct test of Toby’s stake in conformity hypothesis in that

the aforementioned measures are specified as research hypotheses prior to data analysis.

54


Contextual Effects on Individual Behavior

The inconsistent findings of the deterrent effect of arrest across cities suggests that the

social structure of a community could play a part in determining who is likely to be deterred

through criminal justice sanctions (Sherman et al. 1992). According to Sampson and Wilson

(1995), explanations of criminal behavior tend to rely solely on individual-level characteristics. Just

as neighborhood characteristics have proved to be strong correlates of crime rates, the structural

characteristics of a neighborhood could prove to be significant correlates of the behavior of

individuals (Sampson and Wilson 1995). Not only may individuals with a high stake in conformity

feel that they have more to lose by committing crime, but also individuals residing in communities

comprised of a significant proportion of high-stake individuals may be deterred because of the

possible stigma faced if caught committing a crime. Sampson (1986a) found, for example, that the

deterrent effect of formal criminal justice sanctions within a community were conditioned by the

type of crime and such offender characteristics as race and age. According to Braithwaite (1989),

criminal sanctions can produce different results depending on individual-level and community-level

characteristics. Individual-level characteristics, such as employment and martial status, influence an

individual’s degree of interdependency. Rates of urbanization and residential mobility influence an

individual’s attachment to their community. Criminal justice sanctions are more likely to deter

future criminal behavior if administered in communities containing significant proportions of

mutually dependent individuals (Braithwaite 1989).

Structural characteristics include, but are not limited to, such factors as the unemployment

rate or proportion of single individuals residing within a community. The research on domestic

violence recidivism lends some support for such an ecological hypothesis. Colorado Springs had a

higher employment rate compared to Milwaukee and a greater deterrent effect was found in

Colorado Springs (Sherman 1992). Further, a strong deterrent effect was found in Miami-Dade

County in which seventy-nine percent of the sample were married (Pate et al. 1991).

55


The current study will test the ecological hypothesis by examining a variety of

neighborhood characteristics and their influences on the behavior of individuals. Specifically,

census tract data have been collected so that the following proportions could be calculated:

proportion of adults consisting of college graduates; proportion of unemployed adults; proportion

in managerial and professional occupations; proportion of the population on welfare; median

household income; proportion non-whites; the proportion of married adults and single adults;

proportion of two-parent households with children; proportion of individuals with the same

residence for the past five years; and proportion of youthful individuals (see table 4.1 for a

complete list of variables).

The aforementioned aggregate-level measures are drawn from a variety of ecological

theories of crime. For example, proportion of the population on welfare and median household

income are offered as measures of poverty; while the proportion of residences who have lived at

their current address for the past five years is offered as a measure of residential stability

(Sampson 1991; Shaw and McKay 1942). All of these measures are derived from social

disorganization theory. Further, the proportion of employed, college educated, and married

individuals as well as the proportion of youthful individuals are measures designed to tap into

Braithwaite’s concept of interdependency (Braithwaite 1989).

This study is also intended to examine the contextual effects of aggregate-level measures

within a single city. According to Sampson (1986a) ecological studies have tended to focus on

explaining differences in crime rates across cities as opposed to studies o f different neighborhoods

within a city. A reason for this level of aggregation in the research is that collecting data that

includes an offender’s address is sometimes difficult. The study was carried out using data

collected from neighborhoods within a single city. Addresses of all offenders arrested for domestic

violence during the study period have been obtained and was sorted according to census tract

boundaries. This allowed for the comparison of offender recidivism across neighborhoods to

determine the possible influence of neighborhood structural characteristics on offender recidivism.

56


Inferring Criminogenic Effects

Although three of the six replication studies—Milwaukee, Colorado Springs and Miami-

Dade County-produced results consistent with the Minneapolis study, fundings from Omaha and

Charlotte revealed that arrest produced a criminogenic effect in that recidivism rates increased

after the initial six m onth follow-up period (Sherman et al. 1992). In Milwaukee, an escalation

effect was found for short arrest (defined as three hours in custody) (Sherman e t al. 1992).

Further, studies that included subgroup analyses found that arrest had a deterrent effect for

employed offenders but an escalation effect for the unemployed (Berk et al. 1992; Dunford et al.

1990; Sherman et al. 1992). It was inferred from these replication studies that over time arrested

offenders were more likely to recidivate compared to offenders who were either separated or

counseled.

Because the sample for the study is comprised solely of offenders who have already been

arrested, escalation effects based on arrest cannot be measured. However, because such escalation

effects have been suggested in prior research examining the effects of criminal justice sanctioning

on domestic violence recidivism, the study will explore the possibility that other, and more

punitive, criminal justice sanctions may escalate the occurrence of domestic violence as well.

Three dependent variables have been measured: whether or not an offender committed an

act of domestic violence during the follow-up period, the number of domestic violence arrests

during the follow-up period, and the number of weeks which pass before an offender is re-arrested

for domestic violence during the follow-up period. Event history analysis with Cox regression is

used to examine the model predicting time to recidivism. Event history analysis is useful in this

case, because both the individual-level stake in conformity variables and the aggregate measures of

informal social control are hypothesized to interact with formal criminal sanctions to determine

who is likely to commit further acts of domestic violence. Event history analysis provides a way to

determine if informal social control processes influence who is likely to recidivate sooner, thus

allowing for an examination of both the short-term and long-term effects of criminal justice

57


sanctions. For example, Dunford (1992) found that a significant proportion of offenders residing

with their partners recidivate between six and twelve months but not within the first six months.

The sample for the study, which is discussed in detail in the next chapter, includes all

offenders arrested during a twenty month period. The follow-up period for each offender began

immediately after their release, whether they are released upon arrest, have their charges

dismissed by the court, receive a suspended sentence, or complete a sentence set forth after

conviction. Follow-up periods vaiy in length from one to five years. Such a lengthy follow-up

period ensures that all recidivists are included in the study.

Conclusion

As a result of the inconsistent findings across replication studies, the deterrent effect of

arrest for domestic violence recidivism remains unknown (Sherman et al. 1992). The inconsistent

results have been attributed to methodological differences and/or problems among the replication

studies preventing definitive conclusions from being reached. Furthermore, the domestic violence

studies tell us little, if anything, about the effectiveness of criminal justice sanctions beyond arrest.

Given that a majority of jurisdictions have adopted mandatory arrest policies, it is possible that

variation in the decision to arrest has been reduced. There is a significant amount of variation,

however, in what happens to offenders subsequent to arrest. The current study explores this issue

by examining the effectiveness of criminal justice sanctions, other than arrest, on domestic violence

recidivism. Both the short-term and long-term effects are assessed.

The inconsistent findings across replication studies have also been attributed to differences

in individual offender characteristics and variations in the social structure of an individual/s

community. A consistent finding in the domestic violence studies is that arrest appears to serve as

a deterrent for individuals who are married, employed and/or have at least a high school education

(Berk et al. 1992; Dunford et al. 1990; Sherman et al. 1992). This finding has been attributed to

Toby’s idea of "stake in conformity"; that is, that individuals who have more to lose if arrested for

58


committing a crime are more likely to be deterred through formal criminal justice sanctioning

(Berk e t al. 1992; Dunford et al. 1990; Sherman et al. 1992). This study explores this issue further

by examining the interaction between formal criminal justice sanctions and an extensive list of

"stake in conformity" measures, otherwise known as informal social control processes.

Further, it is hypothesized that the social structure of a community could influence the

effects of criminal justice sanctioning. Individuals residing in communities comprised of a

significant proportion of married and/or employed individuals are more likely to be deterred

through formal criminal justice sanctioning as well. This hypothesis is derived from a number of

ecological theories, including social disorganization theory, Braithwaite’s theory of reintegrative

shaming, and Hagan’s idea of social capital. Traditional ecological theories were originally put

forth to explain rates of criminal behavior across particular jurisdictions by exploring such factors

as residential mobility, neighborhood poverty levels, and heterogeneity. Sampson (1991) makes the

argument, however, that the structural characteristics of a neighborhood can produce contextual

effects, thereby influencing the behavior of individuals. Neighborhood characteristics can influence

an individual's attachment to community, thereby influencing an individual’s stake in conformity.

Such a direct test of the ecological hypothesis has yet to be published using the current domestic

violence data collected from Minneapolis or any of the replication cities. The current study

attempts to directly test this hypothesis by examining the interactions between criminal justice

sanctions and measures of structural characteristics derived from neighborhoods within a single

city.

Chapter four outlines the research methodology for the study. Included in this section are

the specific research hypotheses that were tested, a description of the selected sample and data,

and an explanation of the statistical tests that were used in the study.

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CHAPTER FOUR

RESEARCH METHODOLOGY

Introduction

Chapter four presents the research methodology for the study. Section one of this chapter

includes the research hypotheses that are examined. Main effects for each of the individual-level

and aggregate-level informal social control variables are presented as well as the hypothesized

main effects for each of the formal social control variables. Also included are the hypothesized

interactions between the informal social control variables and formal criminal justice sanctions.

Section two outlines the time dimension for the study and provides an explanation of how

the time dimension is advantageous for measuring recidivism among domestic violence offenders.

Section three of chapter four provides a detailed description o f the sample for the study,

including a discussion of the target population, sampling frame, sample size, and sampling method.

Section four focuses on data collection. Included in this section is an explanation o f the data

sources, types o f data, and methods of data collection. Section five specifies how each of the

dependent and independent variables in the study are measured as well as a discussion of said

measures. Finally, section five outlines the statistical analyses that were used and provides a

description and rationale for these techniques.

Research Hypotheses

Following are the research hypotheses for the study. Each hypothesis is presented as a

bivariate relationship, but the examination of each relationship includes statistical controls for all

other variables in the complete model. Hypotheses delineating all main effects are presented first,

followed by the hypothesized interactions.

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Individual-Level Formal Social Controls

Hypothesis 1; The likelihood of rearrest is lower and the time to rearrest is longer for

misdemeanants who are subjected to increased levels of formal social control. Specifically:

1.1 offenders who are prosecuted versus offenders who are dismissed;

1.2 offenders who are convicted of a domestic violence offense versus all other offenders

not convicted;

1.3 offenders who are sentenced to probation versus all other offenders;

1.4 offenders who spend more time on probation once convicted;

1.5 offenders who are sentenced to jail versus all other offenders;

1.6 offenders who spend more time in jail once convicted; and

1.7 offenders who are required to participate in domestic violence treatm ent as part of

their sentence versus offenders not required to participate in treatment.

Individual-Level Informal Social Controls

Hypothesis 2: The likelihood of rearrest is lower and the time to rearrest is longer for

misdemeanants who have a higher stake in conformity; this includes:

2.1 offenders who are married;

2.2 offenders with children;

2.3 offenders who reside with their spouse and/or children;

2.4 offenders who have resided longer at their current address;

2.5 offenders who have more education;

2.6 offenders who are employed;

2.7 offenders who have spent more months at their current job; and

2.8 offenders who are skilled blue-collar workers or skilled white-collar workers;

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Aggregate-Level Informal Social Controls

Hypothesis 3; The likelihood of rearrest is lower and the time to rearrest is longer for

misdemeanants residing in neighborhoods with a higher proportion of high stake individuals; this

includes:

3.1 communities with larger proportions of college graduates;

3.2 communities with larger proportions of employed adults;

3.3 communities with higher median household incomes;

3.4 communities with smaller proportions of the population on welfare;

3.5 communities with a high proportion of individuals in managerial or professional

occupations;

3.6 communities with smaller proportions of non-whites;

3.7 communities with a larger ratio of married to single adults;

3.8 communities with larger proportions of two-parent households with

children;

3.9 communities with smaller proportions of female headed households;

3.10 communities with smaller proportions of individuals aged 15-24; and

3.11 communities with larger proportions of residents residing at the same address for the

past five years.

Hypothesized Interactions

Hypothesis 4: The likelihood of rearrest is lower and the time to rearrest is longer for

misdemeanants who have a higher stake in conformity (as measured by the above individual-level

informal social control variables) and/or who live in communities comprised of a higher proportion

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of "high stake" individuals (as measured by the above aggregate-level informal social control

variables) and are subjected to increased levels o f formal social control; this includes:

4.1 offenders who are prosecuted versus low stake offenders who are

prosecuted;

4.2 offenders who are convicted o f a domestic violence offense versus low stake offenders

who are convicted of a domestic violence offense;

4.3 offenders who spend time on probation versus low stake offenders who spend time on

probation;

4.4 offenders who are sentenced to jail versus low stake offenders who are sentenced to

jail; and

4.5 offenders who are required to participate in domestic violence treatm ent as part of

their sentence versus low stake offenders who are required to participate in

domestic violence treatment as part of their sentence.

The interactions that are examined in the study are similar to those examined by Berk et

al. (1992) and Gam er et al. (1995), but includes a much more extensive list of individual-level

informal social control variables and includes interactions between aggregate-level informal social

control variables and formal social control variables.

Time Dimension

The data for the entire project spans a period of five years, including at least a two-year

follow-up period for all arrestees since misdemeanants cannot be sentenced to more than twelve

months in jail. Follow-up periods range from one to five years depending on the date of the

original arrest and the disposition of the arrest. The follow-up period for each misdemeanant

begins immediately after their release, whether they are released outright, have their charges

dismissed by the court, have their sentence suspended, or they have actually completed their

sentence.

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Prior research on recidivism indicates that follow-up periods should be at least one year

long. Significant differences have been found between domestic violence offenders who recidivate

within the first six months and offenders who recidivate between six and twelve months. Dunford

(1992) found that a significant proportion of offenders residing with their partners recidivate

between six and twelve months but not within the first six months. The present study includes

follow-up periods of up to five years, which will allow for the examination of similar types of

patterns. A second advantage of examining data with such lengthy follow-up periods is that the

vast majority of those offenders who will recidivate are included in the study. A third advantage is

that for those offenders who are actually convicted and sentenced to jail, follow-ups are sufficiently

long to examine recidivism after release. The follow-up period is sufficient to control for the

effects of prosecution, conviction, and criminal sanction. Domestic violence offenders who are

convicted and/or sentenced to jail will have longer time periods until re-arrest (if any).

Sample

The target population for the study consists of all suspects arrested for misdemeanant

domestic violence in Hamilton County, Ohio.

The sampling frame consists of all misdemeanant suspects arrested for domestic violence

in Hamilton County, Ohio between August 1, 1993 and October 31, 1993 and January 1, 1995 to

May 31, 1996. Cases from November 1993 through 1994 were unavailable for review because the

county adopted a data base system for record keeping beginning in 1995. Paper documents for

cases prior to 1995 were unavailable. However, whether or not a suspect recidivated between

November 1993 and December 1994 was available, and this information was used in the study.

Copies of all arrest reports with the names of all misdemeanant suspects arrested for

domestic violence between August 1993 and October 1993 and between January 1995 and May

1996 were provided by the Hamilton County Justice Center’s Department of Pretrial Services.

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During these two periods, roughly 5,000 arrests for misdemeanor domestic violence were

made. To provide enough cases for the data set (in order to minimize the intercorrelations among

the predictors), all o f these arrests were selected from the list. D ue to repeat offending during the

two time periods, this procedure provided 3,954 unique individuals (first period = 683; second

period = 3,271 suspects). For persons with multiple arrests during the two time periods, the

earliest arrest was coded as the initial arrest whereas all subsequent arrests were coded as domestic

violence recidivism.

The sample size for the study is large enough to include all o f the independent variables

in the same model. Models with large numbers of variables are moTe likely to have

multicollinearity when smaller samples are used because any extreme categories of a variable will

disproportionately influence the results o f the bivariate correlations. With smaller samples, the

extreme categories of a variable may be represented by only one or a few cases (i.e., they are

"outliers" in the variable’s distribution relative to the other cases in the sample). With larger

samples, extreme categories are more likely to contain more cases because larger samples provide

a more representative picture of the true population. Unless the population correlations between

the predictors are in fact non-zero, larger samples can reduce the possibility of multicollinearity

(Hanushek and Jackson 1977). Multicollinearity was not a problem in the current study. A

correlation matrix of all variables included in the study is attached as appendix one. An inspection

of the correlation matrix reveals that four of the variables—whether or not a suspect was sentenced

to probation or jail and the length of jail or probation sentence were highly correlated with each

other. To determine if the correlations were problematic in the complete model, a separate

analysis was completed excluding the variables measuring length of sentence and the regression

coefficients did not significantly change.

Although the list o f names would constitute the population of domestic violence arrestees

during this period, this pool of individuals would still be treated as a sample because it would

65


represent only twenty months o f arrests. The results of this study are externally valid to domestic

violence offenders in Hamilton County, Ohio. The results should, however, generalize to other

cities with similar macro-level demographic characteristics.

Data Collection

Data for the study were collected from four sources: arrest reports; intake interview forms

court files; and the 1990 U.S. Census of Population and Housing.

Court files and census data are matters of public record, but arrest reports and intake

interview forms are confidential. The arrest reports were used to identify the sample, the dates of

any rearrests during the follow-up period, and the types of offenses involved in the rearrests. The

intake interview forms, on the other hand, contain a much wider variety of information, appendix

two presents a copy of this form so that reviewers can examine the available information.

The macro-level (census tract) data were obtained from the 1990 U.S. Census of

Population and Housing. This includes data for the one hundred and thirty-one census tracts in

Hamilton County Ohio. The U.S. Bureau of the Census provides demographic and

Sociodemographic information aggregated by census tract. Each suspect’s address was known,

allowing for the identification o f each person’s census tract.

Most of the micro-level data was collected from copies of arrest reports and intake

interview forms provided by the Department of Pretrial Services. The remaining micro-level data

were obtained from adult court files. Court files were reviewed in the Hamilton County

Courthouse because these files cannot be copied or removed from the premises.

The micro-level data include the names and addresses of all persons in the sample, the

dates of the original arrest(s) and all subsequent rearrests for domestic violence during the follow-

up period, the disposition of every arrest (including the type and length of sentence if convicted),

the seriousness and numbers of prior arrests, and all of the demographic and Sociodemographic

66


characteristics necessary to operationalize the individual-level informal social controls as well as

the statistical control variables.

Micro-level data were coded and transferred to a code sheet prior to input. A copy o f the

codebook is attached as appendix three and specifies all information taken from the arrest sheet,

intake interview forms, and court files.

Once identified, the existing macro-level data were merged with the suspect’s individual-

level data. The following information was taken from the 1990 U.S. Census of Population and

Housing: number of college graduates; number of unemployed adults; number of individuals in

managerial and professional occupations; number o f female headed households; median household

income; number of welfare recipients; number of non-whites; number of single and married adults;

number of two-parent households with children; the number of individuals aged fifteen to twenty-

four; and the number of individuals residing at the same address for the past five years.

Proportions were calculated using the total populations of interest in each tract. These macro-level

data were used to operationalize the aggregate-level informal social control variables.

A common criticism in previous macro-level research involving the use of census data

centers around the fact that census data fail to provide measures for factors believed to mediate

the relationship between community structure and crime. According to Sampson and Groves

(1989), a lack of friendship networks, community members’ perception of a lack of control over

street com er gangs, and a low prevalence of organizational participation mediate the effects of

community structural characteristics put forth by Shaw and McKay (poverty, residential mobility,

and heterogeneity).

Any analysis involving the possible influences of community characteristics on behavior,

including criminal behavior, should begin by first asking the question of what is a community. The

community characteristics thought to influence crime could exist at various levels, ranging from a

few street blocks to an entire neighborhood. Complicating the problem is the fact that residents

define their neighborhoods differently. According to H unter (1974), people define their

67


communities in terms of their functional needs and interests. Residents with children may define

their community on scale of the "social block," while residents without children may define their

community on a larger neighborhood scale. Furthermore, Sociodemographic characteristics are

also related to an individual's definition of community. In his study of Chicago’s neighborhoods,

H unter (1974) found that individuals who were white, had higher-level occupations, who lived in

an area longer, and participated in local neighborhood organizations tended to define their

neighborhoods on a larger scale than their counterparts.

Focusing on territorial boundaries as a definition o f a community has also been criticized

by Tilly (1973). Advances in transportation and communication have expanded the concept of a

community beyond simple neighborhood boundaries (Tilly 1973). Duffee (1980) argues that

communities contain both a vertical and a horizontal dimension. The vertical dimension refers to

the extent to which a neighborhood is dependent upon some outside system for locally needed

resources. The horizontal dimension refers to the extent to which a community relies upon internal

organizations and groups to supply needed resources and perform necessary functions. According

to Duffee (1980), there has been a major shift over the past fifty years resulting in communities

relying more and more on external resources and fewer internal ones.

What this research suggests is that how residents define their community has obvious

implications for a neighborhood to exercise informal social control over its members. The larger

the social unit that is defined by residents, the less likely residents are to know one another and

the less likely they are to exercise supervision over other members and offer assistance to

neighbors that are unfamiliar to them. Hunter (1974) found that the smaller the area defined by

residents, the more likely those residents are to evaluate their community and its members in

positive terms.

There are two possible area units in which aggregate-level data could have been collected

for the study. The first would have been to collect the aggregate-level measures for each of the

one hundred and thirty-one census tracts in Hamilton County, Ohio. Census tracts are created by

68


local committees appointed by the Census Bureau to approximate what local residents perceive

their neighborhood to be (Gunn et al. 1993). Furthermore, they are created to be as homogeneous

as possible in terms of sociodemographic characteristics. The second method would have been to

aggregate or disaggregate the census data into the fifty-three neighborhoods in Hamilton County.

For Hamilton County, there are two and one-half times as many census tracts as there are

neighborhoods resulting in smaller geographical ares compared to the neighborhoods.

Although census tracts have been criticized by some as not being theoretically meaningful

in terms of providing data on those factors believed to mediate the effects of community structural

characteristics (Sampson and Groves 1989), they have been shown to provide valid measures of the

community structural characteristics put forth by Shaw and McKay which are the focus o f the

study (Gottfredson et al. 1991). Further, a study conducted by Simcha-Fagan and Schwartz (1986)

which compared measures derived from census data with data collected from interviews with

community residents, revealed that the two measures were highly correlated and in the expected

direction. For these reasons, and given the fact that to adequately measure those factors believed

by Sampson and Groves to mediate the effects of community structural characteristics would

require interview data, census tract data were used to provide measures for the aggregate-level

informal social control variables. In addition, it is recognized that aggregating across census tracts

to create neighborhoods would result in a loss of homogeneity that is provided by the census

tracts. The averages for the neighborhoods would not tap into any differences between census

tracts within neighborhoods.

The data set consists of the micro-level and macro-level information for all domestic

violence arrestees in the sample. Data collection began on August 1, 1993 and ceased on May 31,

1996. The data were then coded and entered into the computer from the arrest reports and intake

interview forms. Court files were reviewed for the sample, data collected, coded, and entered;

follow-up data for all rearrests were collected, coded, and entered until the end of the follow-up

period; census tract data was entered to create the aggregate-level data set (units = census tracts).

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Table 4.1 Variables and Frequencies (Means and Standard Deviations Reported for Interval/Ratio Scales)

Variable Categories Category Frequencies

Denendent

Rearrested for d.v.during 2 year 0 = no 2,544follow-up 1 = yes 459

Number of rearrests 0 = none 2,544for d.v. during 1 = one 3622 year follow-up 2 = two 72

3 = three or more 25

Months to rearrest M = 9.77for d.v. during s = 9.29varied follow-up range: 1-35 n = 589

Case Processing and Outcome

No charges 0 = no 3,373filed 1 = yes 241

Charges 0 = no 1,853dropped 1 = yes 1,761

Acquitted at 0 = no 3,364trial 1 = yes 250

Offender 0 = no 3,324program 1 = yes 290

Probation with or 0 = no 3,014without a fine 1 = yes 600

Jail with or 0 = no 3,310without a fine 1 = yes 304

Probation + jail with 0 = no 3,497or without a fine 1 = yes 117

continued.

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Table 4.1 (Continued)


Length of 0 = no probation 2,849probation 1 = < 3 months 23

2 = 6-8 months 1403 = 12 months 4034 = > 18 months 199

Length of 0 = no jail 3,222jail 1 = < 1 month 143

2 = 1-3 months 1613 = 4-12 months 88

Charges pending at time of 0 = no 2,953arrest 1 = yes 257

Stake in Conformity (Individual-level)

Highest level of 1 = some h.s. 476education 2 = h.s. degree 2,207

3 = some college 5494 = B.S./B.A. or more 130

Years at current 1 = 1 year 1,392residence 2 = 2 years 449

3 = 3-5 years 6644 = 6-10 years 2235 = 11-15 years 636 = 16-20 years 667 = 21-25 years 438 = 26+ years 22

Family ties 1 = no family contact 1092 = lives w/other

relative 8613 = lives w/spouse

or child(ren) 1,3764 = lives w/spouse

+ child(ren) 567

Same job at least 0 = no 1,9281 year 1 = yes 984

Employed in 0 = no 3,006skilled job 1 = yes 608

continued..

71




Stake in Conformity (Asereeate-level)

Concentrated dis M = 0.00advantage s = 6.00(factor of...) range:-15.4-185 n = 2,752

Proportion college M = 0.15graduates range: 0.00-0.72 s = 0.13

Proportion civilian M = 0.11pop. unemployed range: 0.00-0.48 s = 0.09

Median household M = 21,869income range: 0-109,242 s = 12,229

Proportion receiving M = 0.18public assistance range: 0.00-0.65 s = 0.15

Proportion M = 0.42nonwhite range: 0.00-0.99 s = 0.36

Proportion female M = 0.22headed households range: 0.02-0.65 s = 0.13

Proportion managerial M = 0.22-(-professional occup. range: 0.00-0.64 s = 0.11

Ratio M = 0.72married:single range: 0.04-3.05 s = 0.50

Proportion two-parent M = 0.17households w/children range: 0.00-0.51 s = 0.10

Proportion of pop. M = 0.15aged 15-24 range: 0.04-0.74 s = 0.06

Proportion same res M = 0.52idence past 5 years range: 0.13-0.75 s = 0.10

continued...

72




O ther Control Variables

Male 0 = no 5461 = yes 3,051

Age 1 = 16 - 24 8122 = 25 - 34 1,4533 = 35+ 1,314

# prior convictions 0 = none 2,374for violent 1 = one 629misdemeanors 2 = two o r more 432

Ever incarcerated 0 = no 2,200for other than d.v. 1 = yes 1,169

Variables and Methods of Analysis

Discussion of Measures

The first dependent variable for the analysis is whether a misdemeanant from the sample

was rearrested for any domestic violence during a two-year fixed follow-up period beginning after

the sentence (if any) had been completed.' The second dependent variable for the analysis is the

number of rearrests for any domestic violence during a two year fixed follow-up period. This

measure was collapsed into four categories due to the heavily skewed distribution o f rearrests (only

twenty-five suspects were rearrested three or more times). The third dependent variable is the

number of weeks that elapse before a rearrest for domestic violence, if any, during a varied follow-

up period.

Recidivism can be operationalized in different ways depending on whether the goal of

criminal justice intervention is to reduce, delay, or eliminate criminal behavior. Further, recidivism

73


can be measured in terms of either arrests or convictions. Examining convictions helps to

overcome the problems with cases failing to meet the standard of legal guilt, however, such an

examination fails to account for an even greater number of cogent cases that are dismissed for any

given reason. Research comparing the reliability of arrests and convictions suggests that for this

reason arrests are a preferable measure of recidivism (Maltz 1984). An alternative dependent

variable would be to examine differences in recidivism for offenses involving the same victim

versus a different victim. The Sherman et al. (1992) study of domestic violence in Milwaukee

provided an analysis of repeat domestic violence involving the same victim as well as any victim.

No significant differences were found, however, between the two samples. Reiss (1985) argues that

any analysis of offender recidivism that focuses solely upon violence against the same victim

ignores possible displacement effects.

Three groups of independent variables are included in the complete model: formal social

control variables; individual-level informal social control variables; and aggregate-level informal

social control variables. The formal social control variables refer to case processing and outcome

measures and include the following: whether or not charges were filed against a suspect; whether

or not charges were subsequently dropped; whether or not the suspect was convicted or acquitted

at trial; as well as the sanction received (if any) by a suspect. The disposition measure labeled

"offender program" refers to a two-day counseling session related to domestic assault. Some of

these offenders may have also served time on probation and/or in jail, but the number of those

who did were too limited to permit a separate and reliable examination of such sentences. The

ordinal categories of time served on probation and time served in jail represent the most common

terms imposed by judges in the jurisdiction. For example, over one-third of those sentenced to jail

served under one month whereas the rest ranged predominantly between one and six months.

W hether or not a suspect had pending charges at the time of arrest is offered as a crude measure

of the speed of case processing.

74


The second group of variables are offered as measures of Toby’s stake in conformity

hypothesis. Individuals who are married, have children, have lived longer at their current

residence, have more education, are employed, have been employed longer at their current job,

and are considered skilled blue-collar or skilled white-collar are hypothesized to 1) have a higher

stake in conformity; and 2) are thereby believed to have more to lose through criminal justice

sanctioning; 3) are hypothesized to recidivate less; and 4) are more likely to have longer time

periods until rearrest for domestic violence than their counterparts.

The individual-level informal social control variables are designed to assess concepts from

control theories, particularly Toby’s concept of "stake in conformity." Toby’s stake in conformity

hypothesis has never really been operationalized and tested directly. Toby’s stake in conformity

hypothesis emulates control theory with the idea that individuals with a significant stake in

conformity (i.e., educated and employed) are insulated from the enticements of illegal behaviors.

Education was coded as an ordinal variable because years of schooling may not indicate how much

education was actually attained. An individual could potentially report fourteen years of school (if

they failed a couple o f years) when in fact they only have a high school diploma. Further,

education was collapsed into four categories due to the highly skewed distribution of cases beyond

a bachelors degree (i.e., some graduate school, Masters degree, and Ph.D.).

The concept of stake in conformity was further expanded upon by Sherman e t al. (1992)

to include marital status. Sherman et al. (1992) attributed the deterrent effect of arrest for married

individuals to these individuals having a "stake in conformity." Marital status has also been used as

a proxy measure of informal social control by Sampson and Laub (1993) in their adult social bond

theory. Marital status can explain why adults, although previously delinquent, do not continue their

illegal behaviors. Further, Sampson and Laub (1993) make a distinction between individuals who

are married and individuals who are happily married. Although a direct test of this distinction

would require data on offender’s attitudes towards their spouse, whether or not an offender

resides with their spouse and/or children can be used to proxy this concept.

75


Given the findings relating to marital status, it can be argued that individuals with children

also have more to lose by committing illegal acts. This idea is most closely related to Hirschi’s

(1969) attachment element of the social bond. Individuals who are emotionally invested in their

family and friends are believed to be less likely to engage in criminal behavior. The measure of

family ties was constructed with consideration of natural groupings. Persons who do not live with a

family member may be less "attached" than those who do. The latter can be further divided into

varying degrees of attachment (Le., living with family members aside from spouse and children

versus spouse or children versus spouse and children). Common law marriages were coded

separately; however, these individuals were treated the same as married individuals in the analysis.

Length at current residence is offered as a proxy measure of attachment to an individual’s

community (Sampson 1992). Data for residential stability were originally coded in months, but the

measure in table 4.1 was derived after consideration of meaningful cut-off points. It seems

reasonable to treat shorter periods of time during the earlier periods of residence as distinct

groups, since, in these early periods, residents can only begin to familiarize themselves with their

neighbors and surroundings. The subsequent categories used in the scale become broader because,

as more time passes at a specific location, each additional year of residence probably brings fewer

gains in community ties.

The number of months at current job and the type of employment (unskilled laborer,

skilled blue-collar worker or skilled white-collar worker) are concepts drawn from Sampson and

Laub’s (1993) adult social bond theory. Sampson and Laub make a distinction between

employment and meaningful employment. Although these measures are not designed as a direct

test of Sampson and Laub’s theory, they do provide the means to examine the effects of

employment versus type of employment as a possible influence on recidivism. Length of

employment was originally coded in weeks, but was collapsed into two categories because of the

scarcity in employment among the offender sample). Type of employment was coded as either

skilled blue collar or skilled white collar or all else. The official data only contain very crude

76


descriptions of employment type making it difficult to break employment type down into more

than two categories.

In addition to examining the correlation matrix to determine if multicollinearity was a

problem for the current study, another possible indicator of multicollinearity is if one particular

variable is a significant linear combination of other variables in the model. If Pearson’s R is equal

to or greater than .7 (or R2 is equal to or greater than .49) multicollinearity may be a problem.

None of the stake in conformity measures approached either cutoff point. Specifically, only five

percent of the variation in education is explained by the other four stake in conformity measures.

Similarly, five percent of the variation in residence length, twelve percent of the variation in

employment length, and nine percent o f the variation in skilled employment is accounted for by

the other predictors.

Aggregate-level measures are offered as a test of the "community contextual effect"

hypothesis derived in part from social disorganization theory, Sampson’s work on community-level

informal social control processes, Braithwaite’s concepts of interdependency and

communitarianism, and finally Hagan’s idea of social capital (for a complete description of these

theories, see chapter two). Not only may individuals with a high stake in conformity feel that they

have more to lose by committing crime, but also individuals residing in communities comprised of

a significant proportion of high-stake individuals may be deterred because of the possible stigma

faced if caught committing a crime. Specifically, offenders residing in neighborhoods with a higher

socioeconomic status (i.e., larger proportions of college graduates, employed adults, adults

employed in managerial and professional occupations, and higher median household incomes),

lower proportions of welfare recipients, lower proportions of non-whites, greater family stability

(i.e., higher proportions of married adults; higher proportions of two-parent households with

children); lower proportions o f individuals aged 15 to 24, and greater residential stability are

hypothesized to have fewer rearrests for domestic violence and longer time periods until rearrest.

77


According to Shaw and McKay (1942), poverty, transiency, and heterogeneity can

contribute to the level o f disorganization within a neighborhood. A consequence of social

disorganization is that the informal social control institutions—family, churches, and schools—lose

their ability to exercise control over their individual members and crime rates can increase. The

proportion of families with incomes above the national median income and the proportion of the

population on welfare are offered as measures of the poverty level within a neighborhood.

Although these measures constitute a measure of absolute deprivation, they can be combined with

other measures (Le., education and employment) to create a factor similar to that used by

Gottfredson et al. (1991) called "affluence and education" to create a more meaningful SES

dimension. Specifically, Gottfredson et al. (1991) create this factor out of the following measures:

proportion of families with incomes above the national median income, proportion of persons

employed in professional and managerial occupations, proportion o f persons who completed high

school, proportion of employed females, and the ratio of families with farm income to families

with earnings from wages and salaries.

Additional proxy measures of social disorganization have been offered by Sampson (1985)

to include family structure (as measured by proportion of two-parent households with children)

and residential stability.

The proportion of college graduates, the proportion of employed adults, the proportion

youthful population, and the ratio of married to divorced individuals are offered as proxy measures

of Braithwaite’s (1989) interdependency concept. According to Braithwaite, the effects of formal

social control can vary according to the level of interdependency or the degree o f individual

attachment to parents, school, neighbors, and employer.

The aggregate-level measures of neighborhood characteristics were all highly correlated,

perhaps a consequence of the units involved (i.e., census tracts in one urban area). These measures

were factor analyzed for data reduction. Two factors emerged from the principle components

analysis which account for 70 percent of the variation in the eleven measures. The first factor

78


weights nine of the measures more heavily except for the proportion aged 15-24 and the

proportion of the population living at the same residence for at least five years. The original intent

was to create a meaningful SES dimension similar to previous research examining contextual

effects (Le., Gottfredson et al. 1991).

Sampson e t al. (1997) have referred to several of these measures (poverty, female-headed

households, unemployment, youthful population and proportion non-white) as indicators of

"concentrated disadvantage" as opposed to simply measuring SES o r social disorganization.

Poverty-stricken neighborhoods contain significantly higher proportions of non-whites, young

people, and single-parent families compared to more affluent neighborhoods. This concentration

of "disadvantaged" individuals is a consequence of large proportions of middle class individuals

moving out of the cities and into the suburbs as well as the shift in industry to areas outside of the

central city (Sampson et al. 1997). Sampson et al. (1997) has found that "concentrated

disadvantage" has a negative impact on collective efficacy or the willingness of residents to

intervene in situations for the common good of the community and is considered to be a

component of informal social control. Although the processes contributing to the degree of

neighborhood disadvantage are somewhat different from the processes described by Shaw and

McKay (1942)—poverty, heterogeneity, and transiency—the end result is the same. Social

disorganization and a lack of collective efficacy both result in a break down in control in that

neighbors are unwilling or unable to solve commonly experienced problems. The first factor that

emerged from the present study combines variables that resemble Sampson et al.’s (1997) concept

of "concentrated disadvantage" and offers a more meaningful measure of contextual effects than

simply examining an SES or a traditional social disorganization factor.

The last two measures (proportion aged 15-24 and the proportion of the population living

at the same residence for at least five years) were weighted more heavily in the second factor. The

second factor (subject to broader interpretation) might tap into population transience. According

to Bellair (1997), these two variables are related in the sense that a youthful population is also

79


more transient. The transience factor, however, was dropped from the analysis because of its weak

interpretation.

In addition to the social demographic variables that are included in the analysis as control

variables (see table 4.1 for a complete list), a suspect’s race was controlled for. However, it was

dropped from the analysis because it was a nonsignificant predictor of rearrest (for all three

outcome measures) once the "concentrated disadvantage1' factor was introduced in the model.

Race has been found to be significantly related to the deterrent effect of arrest in past research on

domestic violence recidivism. The relationship at the individual level disappeared, however, once

the effects of unemployment were controlled for (Sherman et al. 1992). Age of offender is offered

as a control variable because o f the age-crime relationship. Older offenders are less likely to

recidivate compared to younger offenders. An ordinal measure of age was included because it

absorbed a larger portion of variation in the outcome measures compared to an interval age scale.

Finally, two variables designed to measure prior involvement in criminal activities are

included as control variables. Number of criminal convictions for violent misdemeanors and

whether or not a suspect had any institutional commitments for offenses other than domestic

violence were used to determine the extent that prior criminal history influences an individual’s

current involvement in criminal activities. Sixteen different measures were explored in order to

identify the best measure for the model (Le., the one that absorbed the greatest amount of

variation in the outcome measures)2, and these two measures were by far the best predictors.

Multivariate logit analysis was used to estimate the relative effects of each independent

variable on the first dependent variable—whether a misdemeanant from the sample was rearrested

2

The sixteen measures of prior record explored for inclusion in the complete model included the following: number of prior convictions for all misdemeanors (ratio and ordinal), number of prior convictions for violent misdemeanors (ratio and ordinal), number of prior convictions for all felonies (ratio and ordinal, number of prior convictions for all violent crimes (ratio and ordinal), number of prior convictions for all felonies and misdemeanors (ratio and ordinal, and the number of prior convictions for all felonies, misdemeanors, and petty misdemeanors (ratio and ordinal).

80


for any domestic violence during a fixed two year follow-up period. Logit analysis is designed for

dichotomous dependent variables with marginally skewed distributions.

Multivariate ordinary least squares (OLS) regression was used to examine the second

dependent variable—the number of rearrests for domestic violence during the same two-year

follow-up period. The OLS assumption o f normal distributions of the dependent variable around

fixed values of the predictors in the population might have been violated due to the limited scale.

However, the results from the OLS analysis were consistent with those from the logit analysis, so

both sets of results are presented for comparison.

Event history analysis with Cox regression was used to examine the model predicting time

to recidivism during a varied follow-up period (including all suspects). Event history analysis with

Cox regression was used to estimate the relative effects of the predictors in the complete model on

time to rearrest. Each life table was pooled over the categories of the independent variables and

chi-square tests were performed to examine the significance of these variables for predicting time

until recidivism. Life-table analysis was also used to examine time to recidivism for each

disposition group. Both analyses are appropriate for dealing with right-censored data with unequal

follow-up periods (Allison 1984). Life tables were examined for each disposition group so that

comparisons can be made between groups to examine differences in the length of time to

recidivism.

The "life table" is used in demographic research to follow up the experiences of a closed

cohort of persons bom in the same year over time (Keyfitz 1977). It is used in mortality research,

where the death of a cohort member constitutes a "decrement" to the life table. For this study,

recidivists constitute the decrements to the life table and the cohorts being followed over time

include persons receiving identical dispositions. Although the follow-up spanned fifty-eight months

for some offenders, everyone rearrested for domestic violence was rearrested by the thirty-fifth

month.

81


Two types of failure rates were examined because each measure is based on a different

assumption of "failure" (for a discussion of event history analysis, see Kalbfleisch and Prentice

1980; Lawless 1982; Lee 1980). The first measure is an extension of the "age-specific decrement

rate,” defined as the probability that an individual who has survived to a particular age will "exit"

the cohort at any time during that particular age interval. This study examines a "time-specific

recidivism rate", describing the probability of recidivism during time b for an individual who has

not yet recidivated before reaching time b. 'Time" is measured in one-month intervals.

individuals recidivating during time b

Recidivism R ate = __________________________for time b total persons surviving to

time b without recidivating

This equation calculates time-specific recidivism rates for the exposed population (i.e., the

denominator incorporates only persons who have not recidivated by a particular time). The time-

specific recidivism rate assumes that survival to later time periods creates a greater risk of

recidivism during those periods (based on the laws of probability). The second life-table measure is

the "probability density function," defined as the probability that any individual will exit the cohort

during a particular time.

individualsPDFb = recidivating during time b

total persons in cohort

Equation 2 describes the probability that any cohort member will have recidivated during time

b. The probability density function assumes that length of survival is irrelevant for probabilities of

recidivism (decrement), unlike the time-specific recidivism rate. A comparison of the distributions

of each rate examined will determine whether the different assumptions underlying each rate yield

significant differences in the distributions of recidivism for each disposition group.

82


To examine the interactions between court dispositions and stake in conformity, models

including the disposition variables and the statistical control variables were estimated separately for

groups differing in stake in conformity. This was done to determine whether the relationships

involving type of disposition depended on a suspect’s stake in conformity. To permit the analysis,

some of the categories of the ordinal stake in conformity measure were collapsed (to preserve

degrees of freedom). For education, categories one and two were combined and categories three

and four (see table 4.1). For residential stability, categories one and two were combined and three

through eight. Family ties were collapsed into two groups: categories one and two versus three and

four. The "concentrated disadvantage" measure was also collapsed into two groups based on

meaningful distinctions balanced against the numbers included in each group: persons falling in

the bottom thirty-third percentile of the scale and those in the top thirty-third percentile of the

scale (in order to include the extreme categories). The interactions were examined with the

Bonferroni (z-score) test for interactions which is considered to be a more conservative test than

the Kleinbaum and Kupper t-test for differences between regression coefficients. The Bonferonni

z-score test is conservative test for interactions reduces the probability of finding significant

interactions by chance alone, where z is equal to

|bi - b2 / (se,)2 + (se2)2 j

bi = first regression coefficient b2 = second regression coefficient sej=standard error of first coefficient se2=standard error of second coefficient

83


CHAPTER FIVE

RESULTS

Introduction

Roughly fifteen percent of the 3,003 suspects followed for at least two years were

rearrested for domestic violence. O f the 3,614 suspects remaining in the jurisdiction, 589 (16.3

percent) were rearrested. All recidivists were rearrested within three years after sentence

completion. O ther descriptives are also worth noting, some of which cannot be gleaned from table

4.1. One-third of the sample were not even living with the victim at the time o f arrest. Roughly

one third of the sample were also unemployed and 29.8 percent were receiving no financial

support (including welfare) upon arrest. The vast majority of the sample (96.1 percent) did not

have college degrees and only one-third held jobs for at least a year.

A review of the other criminal history measures explored for the study indicated that a

majority of the suspects (63.2 percent) had prior records. A significant percentage of the

sample had been incarcerated previously for an offense other than domestic violence (34.7

percent), suggesting that many domestic violence offenders may violate the law in general. While a

clear majority of the suspects were male, 15.2 percent were female. Over one-third o f those

arrested were 35 years or older a t the time of arrest, whereas 22.7 percent were

under age 25. Finally, African-Americans constituted nearly two-thirds of the sample (60.4

percent).

Zero-Order Relationships

Table 5.1 presents the standardized zero-order relationships between each predictor and

the outcome measures. Results for the court disposition variables revealed that the likelihood of

recidivism was lower, rearrests were fewer, and time until rearrest was longer for suspects who (a)

had charges filed against them (versus those who did not), (b) underwent the domestic violence

84


offender program, (c) were convicted and sentenced to probation and jail, and (d) did not have

old charges pending at the time of arrest. Those who were convicted and served longer sentences

of probation or jail were more likely to be rearrested for domestic violence. The number of

rearrests was also higher for suspects with longer sentences of either probation or jail, but the time

to rearrest was significantly shorter for suspects sentenced to longer jail terms only.

Table 5.1 Zero-order Relationships Between Predictors and Outcome Measures of Domestic Violence Recidivism (Standardized Coefficients Reported)

Denendent Variables

PredictorRearrested(pseudo-r)

# rearrests (r)

M onths to rearrest (Cox reg.)

Case Processing and Outcome

No charges filed 0.060** 0.057** 0.040**

Chargesdropped -0.020 -0.017 0.000

Acquitted at trial 0.000 -0.032 0.000

Offenderprogram -0.042** -0.057* -0.022**

continued.

* statistically significant at the 0.05 probability level.

** statistically significant at the 0.01 probability level.

85



PredictorRearrested(pseudo-r)

Deuendent Variables

# rearrests (r)

Months to rearrest (Cox reg.)

Probation with or without a fine 0.000 0.009 -0.028**

Jail with or without a fine 0.021 0.028 0.013

Probation+jail with or without a fine 0.052** 0.073** 0.016*

Length of probation 0.060** 0.079** -0.005

Length of jail 0.045** 0.050** 0.018*

Charges pending at time of arrest 0.127** 0.137** 0.081**

Stake in Conformity (Individual-level)

Highest level of education -0.055** -0.073** -0.029**

Years at current resid. -0.052** -0.043* -0.032**

Family ties 0.000 0.026 0.000

continued...



86



Denendent Variables

Rearrested Predictor (pseudo-r)

# rearrests (r)


Same job atleast 1 year -0.064** -0.059** -0.041**

Employed in skilled job -0.045** -0.047** -0.029**

Stake in Conformity fAeereaate-Ieveil

Concentrated disadvantage -0.079** -0.077** -0.052**

OtherControl Variables

Male 0.092** 0.075** 0.050**

Age -0.056** -0.070** -0.044**

# prior convictions for violentmisdemeanors 0.173** 0.165** 0.101**

Ever incarcerated for other than d.v. 0.074** 0.075** 0.049**



87


The relationships involving offender program, split sentences o f probation and jail,

sentence length, and pending charges may simply reflect a lack of controls for risk (where

offenders who are more likely to recidivate are also more likely to receive harsher sentences and

to have pending charges whereas the better risks are sent to the offender program). However, the

relationship involving filled charges is consistent with the idea that "doing something" may be better

than "doing nothing."

Except for family ties, the zero-order relationships between all stake in conformity

measures and the three outcome measures were statistically significant. The likelihood of

recidivism was higher, rearrests were greater, and time until rearrest was shorter for suspects who

(1) had a lower level of education in general, (2) lived a shorter amount of time at the same

residence, (3) had not been employed for at least one year, (4) had not been employed in a skilled

occupation, and (5) lived in a census tract with a higher degree of concentrated disadvantage.

These results suggest that controlling for these types of variables may be important in order to

obtain valid estimates of relationships between court dispositions and recidivism likelihoods.

All of the "other" statistical control variables, including the measures of a suspect’s prior

record, were significantly related to the three outcome measures. Those who were younger, were

male, had more prior convictions for violent misdemeanors, and had previously been incarcerated

for an offense other than domestic violence were more likely to be rearrested, had more rearrests

and were rearrested sooner than their counterparts. These results reinforce the importance of

recognizing the possible relevance of these variables in related studies.

Multivariate Analysis of Main Effects

Table 5.2 presents the results from the Multivariate analysis of the three recidivism

measures. Most striking is the finding that, once the statistical controls are introduced, none of the

court disposition variables maintain statistically significant relationships in the predicted directions

with recidivism. However, the number of old charges pending consistently maintains a statistically

significant relationship with recidivism (p < 0.01). Given that this variable is statistically significant

88


even when controlling for prior record of violent misdemeanors and prior incarceration. This

relationship, reinforces the earlier contention that the speed of case processing may be an

important factor in preventing, reducing, and delaying domestic violence recidivism. Although this

measure has never been used in previous domestic violence research, research derived from the

National Pretrial Reporting Program reveals that among offenders who are released after an arrest

and who are then subsequently rearrested, eight percent are rearrested within one week and 37

percent within one month (Walker 1998). This finding related to domestic violence offenders

suggests the possibility that disposing o f these cases sooner may result in less recidivism.

Table 5.2 Multivariate Analyses Predicting Domestic Violence Recidivism (Unstandardized Coefficients Reported; Standard Errors in Parentheses)

PredictorRearrested

(logit)

Denendent Variables

# rearrests (OLS)


Case Processingand Outcome

No charges 0.254 -0.002 0.300filed (0.492) (0.075) (0.391)

Charges 0.244 0.046 0.333dropped (0.439) (0.065) (0.348)

Acquitted at 0.231 0.020 0.160trial (0.496) (0.073) (0.406)

Offender -0.040 -0.008 0.044program (0.478) (0.069) (0.382)

continued...



89



PredictorRearrested

(logit)

Deuendent Variables

# rearrests (OLS)


Probation withor without a -0.746 -0.284* -0.303fine (0.788) (0.126) (0.556)

Jail with or 0.640 0.168 0.367without a fine (0.634) (0.105) (0.482)

Probation+jailwith or 0.005 -0.011 0.352without a fine (0.762) (0.135) (0.560)

Length of 0.512* 0.158* 0.115probation (0.236) (0.041) (0.148)

Length of -0.268 -0.077 -0.093jail (0.249) (0.045) (0.169)

Charges pendingat time of 0.736** 0.179** 0.651**arrest (0.222) (0.042) (0.165)

Stake in Conformityfindividual-level)

Highest level of -0.136 -0.029* -0.129education (0.103) (0.014) (0.085)

Years at -0.081 -0.007 -0.053current resid. (0.051) (0.007) (0.041)

Family ties 0.193* 0.038** 0.103*(0.088) (0.014) (0.071)

continued...




Table 53, (Continued)

Rearrested Predictor (logit)

Denendent Variables

# rearrests (OLS)


Same job at -0.121 -0.005 -0.122least 1 year (0.161) (0.024) (0.128)

Employed in -0.179 -0.023 -0.195skilled job (0.177) (0.026) (0.145)

Stake in Conformity fAacrecate-Ievel)

Concentrated -0.025* -0.003* -0.024**disadvantage (0.011) (0.001) (0.009)

OtherControl Variables

Male 0.883** 0.082** 0.643**(0.240) (0.029) (0.188)

Age -0.210* -0.040** -0.244**(0.093) (0.015) (0.074)

# prior convictionsfor violent 0.332** 0.069** 0.328**misdemeanors (0.093) (0.016) (0.072)

Ever incarcerated for other 0.027 0.016 0.108than d.v. (0.144) (0.023) (0.113)

Modelchi-square 98.656** — 124.558**

Model F 6.889** —

Modelpseudo R .222 — .218

Model R — .261



91


Offenders who received a sentence of probation with or without a fine had fewer rearrests;

however, they were not less likely to be rearrested or have longer periods until rearrest. Offenders

who received longer sentences of probation were more likely to be rearrested and have more

rearrests, but did not have shorter time periods until a rearrest.

Regarding stake in conformity, the significant predictors of rearrest included level of

education (for number of rearrests only), family ties (for all three dependent variables), and the

degree of concentrated disadvantage of a suspect’s census tract (for all three outcomes). The result

for education is consistent with the finding for education in the arrest study by Berk et al. (1992)

and G am er et al. (1995).

Family ties was the only stake in conformity measure that did not maintain a significant

zero-order relationship with any of the recidivism measures. Yet the results for family ties are

inconsistent with the related hypothesis (and previous findings from the domestic violence studies

regarding marital status) in that suspects living with more immediate family members were more

likely to be rearrested, were rearrested more often, and were rearrested faster. Of course, given

the unique offense examined, persons living with significant others have greater opportunities to

engage in domestic violence due to their physical proximity.

A logical question to ask might be why did family ties have a significant effect in the

multivariate model when this variable did not have a significant zero-order effect? Checks for

multicollinearity did not reveal a problem with the family ties variable. A more probable

explanation is that the inconsistency is a result of differences in sample size. The number of cases

included in the analyses varies based on the existence of missing data for each variable. The

number cases included in the multivariate analysis was approximately two hundred cases less than

the number included in the zero-order analysis for the family ties variable. To determine if the

inconsistency was a result of differences in sample size, I did a separate zero-order analysis for

92


family ties based on the same number of cases included in the complete model and the zero-order

relationship was in fact significant as well.

Also, given the inconsistency in findings for family ties compared to prior research on

domestic violence recidivism, I did a separate analysis using a dummy fam ily ties variable that

consisted of whether or not a suspect was simply residing with their spouse o r not (in other words,

I combined the original categories one and two versus categories three and four). The results of

this analysis were congruent with the analysis that is presented. A significant zero-order and

multivariate effect was found for each dependent variable. Suspects who were residing with their

spouse were more likely to recidivate, recidivate more often, and have shorter time periods to

recidivism.

The degree of concentrated disadvantage of a suspect’s census tract was by far the

strongest predictor of rearrest for domestic violence relative to the other statistically significant

stake in conformity measures. The significance of this measure is consistent with studies of other

types of predatory crimes revealing higher violent crime rates as well as higher individual-level

likelihoods of violent crime in more socially "disorganized" neighborhoods and lower

socioeconomic status neighborhoods (e.g., Elliott et al., 1996; Gottfredson et al., 1991; Gunn et al.,

1993; Sampson 1991; Simcha-Fagan and Schwartz 1986). Sampson et al. (1997) has also found a

significant negative relationship between concentrated disadvantage in a neighborhood and low

levels of collective efficacy (the willingness of residents to intervene in situations for the common

good of the community), which in turn was correlated with violent crime. This finding is also

consistent with the criminological literature on the relative importance of aggregate- versus

individual-level predictors of criminal behavior. When including both levels o f predictors in the

same model, the aggregate-level variables not only are stronger but also render nonsignificant

relationships involving most of the individual-level predictors (Simcha-Fagan and Schwartz 1986.)

93


Aside from a suspect’s sex and age, the number of prior convictions for violent

misdemeanors yields one of the strongest relationships in the model. A separate analysis revealed

that this control variable alone rendered a nonsignificant relationship between the recidivism

measures and whether a suspect had ever been incarcerated previously for offenses other

than domestic violence. This is not necessarily surprising, although it does suggest that a history of

less serious criminal behavior may be a better predictor of rearrest for domestic

violence than a history of more serious criminal behavior. This was also suggested in a separate

analysis (see appendix four) of the bivariate relationships involving the different

measures of prior record, where the relationships involving misdemeanors were stronger than those

involving felonies.

Interactions

Tables 5.3, 5.4 and 5.5 present the results from the models predicting rearrests for

domestic violence with the disposition and control variables, specified by categories of stake in

conformity. (The "other" control variables were included in the complete models but their

estimates are not presented.) Table 5.3 presents the results for the first dependent variable—

whether or not a suspect was rearrested during a fixed two-year follow-up period. Table 5.4

presents the results for the second dependent variable—the number of rearrests (if any) during a

fixed two-year follow-up period. Table 5.5 presents the results for the third dependent variable—the

number of weeks until a rearrest (if any) during a varied follow-up period. Tables 5.3, 5.4, and 5.5

are primarily descriptive in that they present the coefficients used in the Bonferroni z-score test of

the interactions. The statistical significance of the coefficients are not particularly insightful

because they are driven in many cases by sub-sample size. It is the coefficient estimates that are

central to the analysis of interactions.

94


Table 53 Empirical Relationships Between Court Dispositions and Recidivism Specified byCategories of Stake in Conformity: Predicting Rearrest for Domestic Violence(Unstandardized Logit Coefficients Reported)

Stake inConformityGroups

No charges filed

Court Disnositions

Dropped Acquitted charges at trial

OffenderProgram

Education

(a) h.s. degree or less .9632 .4377 .2632 .2836

(b) some coU+ 1.1489 .0385 .1200 -1.0955

Residence length

(a) less than 3 years 1.4359 .8464 .8473 .7949(b) 3 years or more -.3074 -1.0522 -1.6458 -2.1885

Family ties

(a) not w/spouse and/or child 1.0296 -.4445 -.0930 -8.2569

(b) w/spouse and/or child 1.0205 .4190 .3104 .1499

Same job > 1 year

(a) no 1.0609 .3855 .1924 .3044(b) yes .4827 .4281 .5134 -.1747

Skilled occupation

(a) no .9991 .3604 .2028 .1214(b) yes .2015 .3190 .3806 -.0542

Concentrated disadvantage

(a) bottom 33 percent .7852 .3166 .1489 .1522(b) top 33 percent .1364 -.0948 -.4176 -.3003

continued...

95


Table S3 (Continued)


Probation

Court Disuositions

Jail Probation+ Jail

PendingCharges

Education

(a) h.s. degree or less .4634 .7184 1.2806 .9520

(b) some coll+ .8911 .3062 .6271 .9773

Residence length

(a) less than 3 years 1.1413 1.1223 1.6954 .8859(b) 3 years or more -.9184 -.7256 -.3075 1.3140

Family ties

(a) not w/spouse and/or child -8.2569 -8.2436 -8.1400 -1.4444

(b) w/spouse and/or child .6611 .7423 1.4700 .9939

Same job > 1 year

(a) no .4421 .6483 1.3474 .9800(b) yes 1.1146 .5999 .1540 .7498

Skilled occupation

(a) no .2686 5509 1.3949 1.0223(b) yes 1.6180 1.2283 .5519 5156


(a) bottom 33 percent .4783 .7009 1.1083 .8695(b) top 33 percent -.4816 .2983 1.4057 1.3895

96


Table 5.4 Empirical Relationships Between Court Dispositions and Recidivism Specified byCategories of Stake in Conformity: Predicting Number of Rearrests for Domestic Violence(Unstandardized OLS Coefficients Reported)


No charges filed

Court Disnositions


OffenderProgram

Education


(b) some coll+ .16667 .03222 .01560 -.06130

Residence length

(a) less than 3 years .22848 .10717 .06938 .06605(b) 3 years or more -.00778 -.10539 -.19369 -.22267

Family ties

(a) not w/spouse and/or child .16071 -.04155 -.01700 -.12500

(b) w/spouse and/or child .19242 .07596 .02495 .00731

Same job > 1 year

(a) no .22210 .06860 .00680 .02246(b) yes .04347 .08105 .04672 -.00451

Skilled occupation

(a) no .20048 .06921 .01028 .00340(b) yes .01610 .06221 .03215 -.00259


(a) bottom 33 percent .13087 .04753 -.01045 -.02148(b) top 33 percent .03593 .02089 -.03552 -.02518

continued...

97




Probation

Court Dispositions


PendingCharges

Education


(b) some coll+ .20069 .06225 .07665 .17631

Residence length

(a) less than 3 years .21618 .15336 .36194 .26993(b) 3 years or more -.13762 -.05151 .05757 .22923

Family ties

(a) not w/spouse and/or child -.12500 -.12441 -.11736 -.06111

(b) w/spouse and/or child .14104 .12896 .38039 .27323

Same job > 1 year

(a) no .13413 .12616 .37708 .29582(b) yes .12080 .05420 .01436 .11280

Skilled occupation

(a) no .04407 .10985 .38153 .29564(b) yes .45240 .14549 .11199 .06960


(a) bottom 33 percent .06634 .07472 .27061 .27517(b) top 33 percent -.03489 .11620 .38262 .28336

98


Table 5.5 Empirical Relationships Between Court Dispositions and Recidivism Specified byCategories of Stake in Conformity: Predicting Time to Rearrest for Domestic Violence(Unstandardized Cox Regression Coefficients Reported)

1111Stake in | Conformity J Groups j

No charges filed

Court Disuositions


OffenderProgram

Education

(a) h.s. degree orless .8957 .5538 .3124 .3068

(b) some coll+ 1.2636 .1789 .1465 -.3147

Residence length

(a) less than 3 years 1.5137 1.0905 1.0076 .9556(b) 3 years or more -.3990 -1.10286 -1.7596 -1.7574

Family ties

(a) not w/spouseand/or child .1907 -.9142 -.8326 -14.3369

(b) w/spouseand/or child 1.1309 .6812 .5023 .4043

Same job > 1 year

(a) no .9865 .4973 .2331 .3536(b) yes .6614 .6067 .6099 .1333

Skilled occupation

(a) no .9577 .4698 .2420 .2231(b) yes .1676 .5533 .5088 .1577


(a) bottom 33 percent .9737 .6162 .4608 .3633(b) top 33 percent .2723 .1729 -.2994 .0143

continued...

99




Probation

Court Disnositions


PendingCharges

Education


(b) some coll+ .4676 .6732 .4439 .9610

Residence length

(a) less than 3 years .7993 1.2210 1.4819 .8210(b) 3 years or more -1.4192 -.7277 -.4729 1.0335

Family ties

(a) not w/spouse and/or child -.1568 -14.3260 -14.0957 -1.4689

(b) w/spouse and/or child .3298 .8750 1.2172 .8969

Same job > 1 year

(a) no .2061 .6545 1.0037 .8752(b) yes .2164 .6312 .5183 .6521

Skilled occupation

(a) no .1286 .5825 .9457 .8966(b) yes .2944 1.0494 .8673 .5649


(a) bottom 33 percent .3968 .7723 .9384 .6553(b) top 33 percent -.1813 .1712 .8416 1.4276

100


The results from the Bonferroni z-score tests of interactions involving court dispositions

and stake in conformity are presented in tables 5.6, 5.7 and 5.8. Table 5.6 presents the results for

the first dependent variable—whether o r not a suspect was rearrested for domestic violence during

a fixed two-year follow-up period. Table 5.7 presents the results for the second dependent variable

—the number of rearrests for domestic violence and table 5.8 presents the results for the model

predicting time to rearrest. The first column of the table presents the models (from tables 5.3, 5.4

and 5.5) being compared within each pair of coefficients. Each subsequent column presents the z-

scores for a specific disposition group. Tables 5.6, 5.7 and 5.8 help to focus on the interactions that

are actually significant, and tables 5.3, 5.4 and 53 can then be examined to more carefully

interpret the meaning of the significant interactions.

Each of the separate components of stake in conformity (education, residential stability,

family ties, stability and type of employment and concentrated disadvantage) were examined for

the each of the case processing and outcome variables. The only interactions that were significant

consistently across all three outcome measures were related to residential stability. Significant

interactions for family ties, employment stability, and type of occupation were found in the model

predicting number of rearrests only and one significant interaction between concentrated

disadvantage was found in the model predicting time to recidivism. The lack of consistency for

these measures of stake in conformity across all models is likely due to chance alone given the

numerous interactions examined in the study, and for this reason, will not be interpreted as being

theoretically meaningful.

101


Table 5.6 Tests of Significant Differences in Relationships Between Court Dispositions andRecidivism Across Stake in Conformity Groups: Predicting Rearrest for Domestic Violence(Bonferroni z-scores Reported)

1111

Stake in |Court Disnositions

Conformity | Groups i

No charges filed


OffenderProgram

Education

h.s. degree or less v. college + .15 .34 .11 1.00

Residence length

less than 3 yearsv. 3 years or more 1.80* 2.15** 2.42** 2.79**

Family ties

not w/spouse +/- child v. w/spouse +/- child .01 .68 .25 .11

Same job > 1 year

no v. yes .47 .04 .26 .39

Skilled occupation

no v. yes .59 .04 .14 .14


bottom 33 percent v. top 33 percent 59 .41 .51 .41

continued.* approaches statistical significance at the 0.05 probability level.


102



1111Stake in | Conformity | Groups |

Probation

Court Disnositions

Ja il Probation+ Jail

PendingCharges

Education

h.s. degree or less v. college + .33 .33 .40 .05

Residence length

less than 3 years v. 3 years or more 1.96** 1.90* 1.63 1.02

Family ties

not w/spouse +/- child v. w/spouse +/- child .09 .15 .17 .77

Same job > 1 year

no v. yes .53 .04 .76 .50

Skilled occupation

no v. yes 1.06 .55 .61 .91


bottom 33 percent v. top 33 percent .74 .38 .25 1.19

* approaches statistical significance at the 0.05 probability level.


103


Table 5.7 Tests of Significant Differences in Relationships Between Court Dispositions andRecidivism Across Stake in Conformity Groups: Predicting Number of Rearrests forDomestic Violence (Bonferroni z-scores Reported)

1111

Stake in | Conformity j Groups |

No charges filed

Court Disnositions


OffenderProgram

Education


Residence length

less than 3 years v. 3 years or more 1.32 1.32 1.51* 1.68*

Family ties

not w/spouse +/- child v. w/spouse + /- child .19 .93 .27 .75

Same job > 1 year

no v. yes 1.13 .09 .26 .18

Skilled occupation

no v. yes .97 .04 .13 .03


bottom 33 percent v. top 33 percent .54 .17 .15 .02

continued...



104



1111Stake in | Conformity ' Groups J

Probation

Court Disnositions


PendingCharges

Education

h.s. degree or less v. college + .48 .38 1.10 1.20

Residence length

less than 3 years v. 3 years or more 1.87* 1.16 1.30 .46

Family ties

not w/spouse +/- child v. w/spouse + /- child 1.29 1.60 2.92** 2.34**

Same job > 1 year

no v. yes .08 .45 1.81* 2.27**

Skilled occupation

no v. yes 2.08** .20 1.28 2.18**





105


Table 5.8 Tests of Significant Differences in Relationships Between Court Dispositions andRecidivism Across Stake in Conformity Groups: Predicting Time to Rearrest forDomestic Violence (Bonferroni z-scores Reported)

1111Stake in \ Conformity [ Groups |

No charges filed

Court Disnositions


OffenderProgram

Education


Residence length

less than 3 years v. 3 years or more 2.40** 2.93** 3.12** 3.15**

Family ties

not w/spouse +/- child v. w/spouse + /- child .86 1.71* 1.00 .01

Same job > 1 year

no v. yes .28 .10 .33 .19

Skilled occupation

no v. yes .61 .08 .23 .06



continued...

* approaches statistical significance at the 0.0S probability level.


106



t111Stake in j Conformity j Groups !

Probation

Court Disoositions


PendingCharges

Education


Residence length

less than 3 years v. 3 years or more 2.87** 2.47** 2.19** .65

Family ties

not w/spouse +/- child v. w/spouse +/- child 0.45 .02 .02 .75

Same job > 1 year

no v. yes .01 .02 .39 .61

Skilled occupation

no v. yes .15 .41 .07 .77


bottom 33 percent v. top 33 percent .60 .61 .09 2.36**



107


Residential stability was the only stake in conformity measure that maintained a significant

interaction effect across all three dependent variables. Specifically, in the model predicting whether

or not an offender was rearrested for domestic violence, offenders who had resided at their

current residence for less than three years and had their charges dropped or were acquitted at trial

were more likely to recidivate. Less stable residents whose cases were ignored approached

statistical significance (0.05 level). Offenders with more stable lengths of residence (3 or more

years) who were sentenced to the offender program or to probation were less likely to recidivate

and more stable residents who were sentenced to jail approached statistical significance. In the

model predicting number o f rearrests, less stable residents who were acquitted at trial approached

statistical significance as did more stable residents who participated in the offender program or

who were sentenced to probation.

Finally, in the model predicting time to recidivism less stable residents whose cases were

either ignored, subsequently dismissed or were acquitted at trial were more likely to be rearrested

sooner. More stable residents sentenced to the offender program, probation, jail or a combination

of probation and jail had longer time periods to rearrest. The consistent results regarding

residential stability suggest, that in the context of stake in conformity, residents who have lived

longer at their current address may be more likely deterred through formal criminal justice

sanctioning because they have more to lose. According to Sampson (1992) length at current

residence can be used as a proxy measure of an individual’s attachment to their community which

in turn is correlated with crime. Braithwaite (1989) argues that the effects of criminal sanctions can

either be "stigmatizing" or "reintegrative" depending on individual offender characteristics and

community-level characteristics. Sanctions that stigmatize can increase crime; while reintegrative

sanctions can decrease crime. Although the current study is not offered as a direct test of

Braithwaite’s theory, sanctions that reduce criminal behavior occur within a context of

"interdependency", which includes an individual’s attachment to their neighborhood (Braithwaite

1989).

108


Despite the finding that less stable residents were more likely to recidivate if their cases

were ignored, dropped, or were acquitted a t trial, the exact opposite was not true. No relationship

was found between more stable residents and "doing nothing". What this suggests is, that for low

stake individuals only, doing nothing could possibly increase recidivism. Also, while more stable

residents were less likely to recidivate when formally sanctioned by the criminal justice system, less

stable residents were neither more or less likely to recidivate when formally sanctioned. This

finding suggests that formal social control can not be used as an effective substitute for informal

social control. Criminal justice sanctions do not appear to have any positive (or negative) benefits

for individuals who do not have a significant stake in conformity. While previous studies of arrest

and stake in conformity have found escalation effects for low stake offenders. The escalation

effects could have been a product of 'bad risk" offenders being more likely to be arrested or the

lack of escalation effects found in the current study could be product of the more conservative

Bonferroni z-score test.3

As stated previously, other components of stake in conformity maintained significant

interaction effects (or approached statistical significance). In the model predicting number of

rearrests for domestic violence, offenders who did not live with their spouse and/or children had

fewer rearrests if they received a sentence of probation and jail and had pending charges against

them. In addition, offenders with less stable employment (less than one year at current job) who

had pending charges against them had more rearrests for domestic violence. Offenders not

employed in a skilled profession who had pending charges against them also had more rearrests

for domestic violence. A second interaction effect was found for skilled workers, but not in the

direction predicted. Skilled workers who received probation actually had more rearrests. Finally, in

the model predicting time to recidivism, offenders with pending charges who reside in

neighborhoods with less concentrated disadvantage had shorter time periods until rearrest. This

A previous analysis of interactions using the Kleinbaum and Kupper (1978) t-test for differences in regression coefficients, a less conservative test, did find several escalation effects that disappeared with the Bonferroni z-score test (see appendix five for these results).

109


finding was also not in the direction predicted. Given the lack of consistency across dependent

variables and within each stake in conformity group, however, the interactions for family ties,

employment, and concentrated disadvantage were probably due to chance alone and will be not

interpreted as theoretically meaningful.

The observations listed above provide support for the idea that greater degrees of doing

"something" (whether that involved more severe dispositions or forced participation in the

offender program) were more effective for reducing rearrests for persons with greater stakes in

conformity (in terms o f residential stability). Specifically, greater effectiveness in reducing

recidivism was achieved with (a) the offender program for more stable residents, (b) probation for

more stable residents, and (c) probation and jail for more stable residents.

Life Tables of Time to Rearrest for Domestic Violence

Table 5.9 presents the length of time to rearrest during the follow-up period for each

disposition group and the pooled sample. The follow-up period spanned 58 months for some

offenders even though everyone rearrested for domestic violence during the follow-up period was

rearrested by month 35. In short, the survival rate for month 35 was the same as for month 58.

This is interesting in and of itself because it suggests that three year follow-ups of these offenders

may include 100 percent of all "failures." The standard errors are presented in table 5.10.

T a b l e 5 . 9 S u r v i v a l A n a l y s i s o f T i m e t o R e a r r e s t A f t e r S e n t e n c e C o m p l e t i o n : C u m u l a t i v e P r o p o r t i o n s H o t R e c i d i v a t i n g-----------1-------------1111 C o u r t D i s D o s i t i o n

1j P o o l e d O f f . P r o b a - P r o b . C h r g s

M o n t h !S a m p le I g n o r e D i s m i s s A c q u i t P r o g , t i o n J a i l + J a i l P e n d .

0 1 . 0 0 0 1 . 0 0 0 1 . 0 0 0 1 . 0 0 0 1 . 0 0 0 1 . 0 0 0 1 . 0 0 0 1 . 0 0 0 1 . 0 0 01 . 9 6 5 . 9 8 3 . 9 8 5 . 9 7 9 . 9 8 6 . 9 1 8 . 9 4 4 . 8 3 6 . 9 0 22 .9 5 4 . 9 5 4 . 9 7 3 . 9 6 3 . 9 7 5 . 9 1 3 . 9 3 7 . 8 3 6 . 8 7 13 .9 3 8 . 9 1 7 . 9 6 2 . 9 5 9 . 9 5 5 . 9 0 9 . 9 0 4 . 8 0 1 . 8 3 94 . 9 2 8 . 8 9 2 . 9 5 0 . 9 4 7 . 9 5 1 .9 0 8 . 8 9 1 .8 0 1 . 8 1 25 . 9 1 9 . 8 8 8 . 9 3 6 . 9 3 9 . 9 4 4 .9 0 3 . 8 8 4 .8 0 1 . 7 9 3

continued...

110



Mon th| P o o l e d j S a m p l e

C o u r t D i s D o s i t i o n

O f f . P r o b a - I g n o r e D i s m i s s A c q u i t P r o g , t i o n J a i l

P r o b .+ J a i l

C h r g sP e n d .

" <5 . 9 1 1 .8 7 5 . 9 2 7 .9 3 5 . 9 3 0 . 9 0 1 . 8 7 1 . 8 0 1 . I d 97 . 9 0 6 . 8 6 3 . 9 2 1 .9 3 1 . 9 2 7 .8 9 9 . 8 6 8 . 8 0 1 .7 8 58 . 9 0 3 . 8 5 8 . 9 1 8 .9 2 3 . 9 2 7 .8 9 9 . 8 6 8 . 7 8 4 .7 8 19 . 8 9 8 . 8 5 8 . 9 1 2 .9 1 9 . 9 2 7 .8 9 8 . 8 5 5 . 7 8 4 . 7 6 9

10 . 8 9 6 . 8 5 4 .9 0 9 . 9 1 9 .9 2 3 .8 9 6 .8 5 2 . 7 8 4 . 7 6 511 . 8 9 2 . 8 5 0 .9 0 3 . 9 1 9 .9 2 3 .8 9 6 . 8 4 8 . 7 8 4 . 7 5 712 . 8 8 9 . 8 4 2 .8 9 9 . 9 1 9 . 9 2 3 . 8 9 6 . 8 4 2 . 7 8 4 . 7 5 013 . 8 8 6 . 8 2 9 . 8 9 4 . 9 1 9 . 9 2 3 . 8 9 6 . 8 3 8 . 7 8 4 . 7 3 814 .8 8 3 . 8 2 9 . 8 9 0 .9 1 5 . 9 2 0 .8 9 6 . 8 3 5 .7 8 4 . 7 3 015 . 8 7 9 . 8 0 9 . 8 8 7 .9 1 1 . 9 2 0 .8 8 9 . 8 3 5 . 7 8 4 .7 2 616 . 8 7 6 . 8 0 0 . 8 8 4 .9 1 1 . 9 2 0 . 8 8 8 .8 2 8 .7 8 4 .7 2 217 . 8 7 4 . 8 0 0 . 8 8 2 . 9 0 7 . 9 1 7 .8 8 8 . 8 2 8 . 7 8 4 . 7 1 818 . 8 7 0 . 7 9 6 . 8 7 7 . 8 9 1 . 9 1 7 . 8 8 8 .8 2 8 . 7 8 4 . 7 1 019 . 8 6 8 . 7 9 2 .8 7 4 . 8 8 7 . 9 1 3 . 8 8 8 . 8 2 8 . 7 8 4 . 7 0 720 . 8 6 6 . 7 9 2 .8 7 1 . 8 8 3 .9 1 3 . 8 8 8 . 8 2 8 . 7 8 4 . 7 0 321 . 8 6 4 . 7 8 4 .8 6 9 .8 8 3 .9 1 3 . 8 8 4 . 8 2 8 . 7 8 4 . 7 0 322 . 8 6 2 . 7 8 0 . 8 6 7 . 8 7 9 . 9 1 3 .8 8 4 . 8 2 5 . 7 7 5 . 7 0 323 . 8 6 0 . 7 7 5 . 8 6 4 . 8 7 5 . 9 1 3 .8 8 4 . 8 1 5 . 7 7 5 .7 0 324 . 8 5 6 . 7 7 1 . 8 6 0 . 8 7 1 . 9 0 6 .8 8 3 . 8 1 5 . 7 7 5 .6 9 125 . 8 5 1 . 7 6 3 . 8 5 4 .8 6 7 . 8 9 9 .88 3 . 8 1 2 . 7 7 5 . 6 8 326 . 8 4 9 . 7 5 5 . 8 5 0 . 8 6 3 . 8 9 9 .883 . 8 1 2 . 7 7 5 . 6 7 927 . 8 4 5 . 7 5 1 .8 4 6 . 8 5 9 . 8 9 6 . 8 8 3 .8 0 5 . 7 7 5 . 6 7 528 . 8 4 3 . 7 4 6 . 8 4 3 . 8 5 9 . 8 9 6 . 8 8 3 .8 0 2 .7 7 5 . 6 7 129 . 8 4 1 .7 4 6 .8 4 0 . 8 5 5 . 8 9 2 . 8 8 3 .8 0 2 .7 7 5 . 6 6 830 . 8 3 9 . 7 4 6 . 8 3 7 . 8 5 1 . 8 9 2 . 8 8 3 .7 9 9 .7 7 5 . 6 6 431 . 8 3 7 . 7 3 8 . 8 3 5 . 8 5 1 . 8 9 2 . 8 8 3 . 7 9 9 .7 7 5 . 6 6 432 . 8 3 7 . 7 3 8 . 8 3 4 . 8 5 1 . 8 9 2 . 8 8 3 . 7 9 9 .7 7 5 . 6 6 033 . 8 3 6 .7 3 8 . 8 3 3 . 8 5 1 . 8 9 2 .8 83 . 7 9 9 . 7 7 5 . 6 6 034 . 8 3 6 .7 3 8 . 8 3 3 .8 5 1 .8 9 2 .8 83 .7 9 9 . 7 7 5 . 6 6 035 . 8 3 6 .7 3 8 . 8 3 3 .8 5 1 .8 9 2 . 8 8 3 . 7 9 9 . 7 7 5 . 6 6 0

N 3 , 6 0 3 241 1 , 7 5 4 24 9 289 599 304 116 256

T a b l e 5 . 1 0 S u r v i v a l A n a l y s i s o f T ime t o R e a r r e s t A f t e r S e n t e n c e C o m p l e t i o n i S t a n d a r d E r r o r s o f C u m u l a t i v e P r o p o r t i o n s N o t R e c i d i v a t i n g

i i i i i i i i i i i ii P o o l e d |

Mo nt h j S a m p l e j I g n o r e D i s m i s s


O f f . P r o b a - A c q u i t P r o g . t i o n J a i l

P r o b . ■f J a i l

C h r g sP e n d .

0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 .0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 .0 0 0 01 . 0 0 2 9 . 0 0 8 8 .0 0 2 9 .0 0 9 4 .0 0 8 1 .0 1 1 7 .0 1 2 9 .0 3 5 8 .0 1 8 02 . 0 0 3 4 .0 1 4 4 .0 0 4 0 . 0 1 1 8 .0 1 0 6 . 0 1 2 1 .0 1 3 7 .0 3 5 8 . 0 2 0 43 . 0 0 3 9 .0 1 9 1 .0 0 4 7 . 0 1 1 8 .0 1 4 3 .0 1 2 3 .0 1 7 2 . 0 3 8 5 . 0 2 2 34 . 0 0 4 1 .0 2 1 0 .0 0 5 4 . 0 1 3 8 . 0 1 4 8 .0 1 2 3 .0 1 8 3 . 0 3 8 5 . 0 2 3 55 . 0 0 4 4 . 0 2 1 4 .0 0 6 1 .0 1 4 9 . 0 1 5 7 .0 1 2 6 .0 1 8 8 . 0 3 8 5 .0 2 4 5

continued...



Mo nt hP o o l e dS a m p le

C o u r t D i s p o s i t i o n

I g n o r e D i s m i s s A c q u i tO f f .P r o g .

P r o b a t i o n J a i l

P r o b .+ J a i l

C h r g sP e n d .

6 .0 0 4 6 . 0 2 2 4 . 0 0 6 5 . 0 1 5 4 . 0 1 7 0 . 0 1 2 7 . 0 1 9 8 . 0 3 8 5 . 0 2 4 77 .0 0 4 7 . 0 2 3 4 . 0 0 6 7 . 0 1 5 9 . 0 1 7 4 . 0 1 2 8 . 0 2 0 0 .0 3 8 5 . 0 2 4 88 .0 0 4 8 . 0 2 3 7 . 0 0 6 8 .0 1 6 9 . 0 1 7 4 . 0 1 2 8 . 0 2 0 0 .0 3 9 6 . 0 2 5 09 . 0 0 4 9 .0 2 3 7 . 0 0 7 1 . 0 1 7 3 . 0 1 7 4 . 0 1 2 9 . 0 2 0 9 .0 3 9 6 . 0 2 5 5

10 .0 0 4 9 . 0 2 4 0 . 0 0 7 1 . 0 1 7 3 . 0 1 7 8 . 0 1 3 0 .0 2 1 1 .0 3 9 6 . 0 2 5 611 .0 0 5 0 .0 2 4 3 . 0 0 7 4 . 0 1 7 3 . 0 1 7 8 . 0 1 3 0 .0 2 1 3 .0 3 9 6 . 0 2 5 912 . 0 0 5 1 . 0 2 4 8 . 0 0 7 5 .0 1 7 3 . 0 1 7 8 . 0 1 3 0 .0 2 1 7 .0 3 9 6 . 0 2 6 213 . 0 0 5 1 . 0 2 5 6 . 0 0 7 7 . 0 1 7 3 . 0 1 7 8 . 0 1 3 0 .0 2 1 7 .0 3 9 6 . 0 2 6 714 .0 0 5 2 .0 2 5 6 . 0 0 7 8 .0 1 7 8 . 0 1 8 2 . 0 1 3 0 .0 2 1 9 .0 3 9 6 . 0 2 6 915 .0 0 5 3 .0 2 6 8 . 0 0 7 9 . 0 1 8 2 . 0 1 8 2 . 0 1 3 4 .0 2 1 9 .0 3 9 6 . 0 2 7 016 . 0 0 5 3 . 0 2 7 2 . 0 0 8 0 .0 1 8 2 . 0 1 8 2 . 0 1 3 5 .0 2 2 3 .0 3 9 6 . 0 2 7 217 .0 0 5 4 . 0 2 7 2 . 0 0 8 1 .0 1 8 6 . 0 1 8 6 . 0 1 3 5 .0 2 2 3 . 0 3 9 6 . 0 2 7 318 .0 0 5 4 . 0 2 7 4 . 0 0 8 2 .0 2 0 1 . 0 1 8 6 . 0 1 3 5 .0 2 2 3 .0 3 9 6 . 0 2 7 519 .0 0 5 5 .0 2 7 6 . 0 0 8 3 .0 2 0 5 . 0 1 8 9 . 0 1 3 5 .0 2 2 3 .0 3 9 6 . 0 2 7 620 .0 0 5 5 .0 2 7 6 . 0 0 8 4 .0 2 0 8 . 0 1 8 9 . 0 1 3 5 . 0 2 2 3 . 0 3 9 6 .0 2 7 821 .0 0 5 6 . 0 2 8 0 . 0 0 8 4 .0 2 0 8 . 0 1 8 9 . 0 1 3 7 . 0 2 2 3 .0 3 9 6 .0 2 7 822 .0 0 5 6 . 0 2 8 2 . 0 0 8 5 .0 2 1 1 . 0 1 8 9 . 0 1 3 7 . 0 2 2 5 .0 4 0 2 .0 2 7 823 .0 0 5 6 . 0 2 8 4 . 0 0 8 5 .0 2 1 5 . 0 1 8 9 . 0 1 3 7 . 0 2 3 0 .0 4 0 2 .0 2 7 824 .0 0 5 7 . 0 2 8 6 . 0 0 8 6 .0 2 1 8 . 0 1 9 6 . 0 1 3 8 . 0 2 3 0 .0 4 0 2 .0 2 8 125 .0 0 5 8 .0 2 8 9 . 0 0 8 7 .0 2 1 8 . 0 2 0 2 . 0 1 3 8 . 0 2 3 2 .0 4 0 2 .0 2 8 326 . 0 0 5 8 . 0 2 9 1 . 0 0 8 8 .0 2 2 1 . 0 2 0 2 . 0 1 3 8 .0 2 3 2 .0 4 0 2 .0 2 8 427 .0 0 5 8 .0 2 9 1 . 0 0 8 9 .0 2 2 1 .0 2 0 6 . 0 1 3 8 .0 2 3 5 .0 4 0 2 . 0 2 8 528 .0 0 5 9 .0 2 9 3 . 0 0 9 0 .0 2 2 1 . 0206 . 0 1 3 8 .0 2 3 7 .0 4 0 2 .0 2 8 629 .0 0 5 9 .0 2 9 3 . 0 0 9 1 .0 2 2 4 . 0 2 0 9 . 0 1 3 8 . 0 2 3 7 .0 4 0 2 .0 2 8 730 .0 0 5 9 . 0 2 9 3 . 0 0 9 2 .0 2 2 7 . 0 2 0 9 . 0 1 3 8 .0 2 3 8 .0 4 0 2 .0 2 8 831 .0 0 6 0 .0 2 9 4 . 0 0 9 2 .0 2 2 7 . 0 2 0 9 . 0 1 3 8 .0 2 3 8 .0 4 0 2 .0 2 8 832 .0 0 6 0 . 0 2 9 4 .0 0 9 2 .0 2 2 7 . 0209 . 0 1 3 8 .0 2 3 8 .0 4 0 2 . 0 2 8 833 . 0 0 6 0 .0 2 9 4 . 0 0 9 2 .0 2 2 7 . 0 2 0 9 . 0 1 3 8 .0 2 3 8 .0 4 0 2 .0 2 8 834 .0 0 6 0 .0 2 9 4 . 0 0 9 2 . 0 2 2 7 . 0 2 0 9 . 0 1 3 8 .0 2 3 8 .0 4 0 2 .0 2 8 835 .0 0 6 0 .0 2 9 4 . 0 0 9 2 .0 2 2 7 . 0209 . 0 1 3 8 .0 2 3 8 .0 4 0 2 .0 2 8 8

T a b l e 5 . 1 1 S u r v i v a l A n a l y s i s o f T ime t o R e a r r e s t A f t e r S e n t e n c e C o m p l e t i o n i P r o p o r t i o n s E x p o s e d t o R i s k R e c i d i v a t i n g D u r i n g S p e c i f i c Mon th ( H a z a r d R a t e s )

Month

C o u r t D i s p o s i t i o n

S a m p l e | I g n o r e D i s m i s s A c q u i tO f f .P r o g .


P r o b .- t - J a i l

C h r g sP e n d .

0 .0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 .0 0 0 0 . 0 0 0 0 . 0 0 0 01 . 0 3 4 7 .0 1 6 7 . 0 1 4 4 .0 2 0 3 . 0 1 3 9 . 0 8 5 3 . 0 5 7 5 . 1 7 8 4 .1 0 2 72 .0 1 2 1 . 0 3 0 0 .0 1 2 2 .0 1 6 5 . 0 1 0 6 . 0 0 5 5 . 0 0 7 0 . 0 0 0 0 .0 3 5 23 .0 1 6 1 .0 3 9 9 . 0 1 1 8 .0 0 4 2 . 0 2 1 5 . 0 0 3 7 .0 3 5 7 .0 4 2 1 . 0 3 6 54 .0 1 1 0 . 0 2 7 5 .0 1 2 5 . 0 1 2 6 . 0 0 3 6 .0 0 1 8 .0 1 4 7 . 0 0 0 0 . 0 3 3 15 .0 1 0 2 . 0 0 4 7 .0 1 4 5 .0 0 8 5 . 0 0 7 3 .0 0 5 5 .0 0 7 4 . 0 0 0 0 . 0 2 4 3

c o n t i n u e d . . .

112




Mo nthP o o l e dS a m p l e I g n o r e D i s m i s s A c q u i t

O f f .P r o g .


P r o b . +J a i l

C h r g sP e n d .

6 . 0 0 8 8 .0 1 4 1 .0 1 0 4 . 0 0 4 3 . 0 1 4 8 . 0 0 1 9 .0 1 5 0 . 0 0 0 0 .0 0 4 97 . 0 0 4 9 .0 1 4 3 .0 0 5 6 . 0 0 4 3 . 0 0 3 7 . 0 0 1 9 .0 0 3 8 . 0 0 0 0 . 0 0 5 08 . 0 0 3 4 .0 0 4 8 .0 0 3 7 . 0 0 8 7 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 .0 2 1 7 . 0 0 5 09 . 0 0 5 5 .0 0 0 0 .0 0 6 9 . 0 0 4 4 . 0 0 0 0 . 0 0 1 9 .0 1 5 3 . 0 0 0 0 . 0 1 5 1

10 . 0 0 2 5 . 0 0 4 8 .0 0 2 5 . 0 0 0 0 . 0 0 3 7 . 0 0 1 9 .0 0 3 9 .0 0 0 0 . 0 0 5 111 . 0 0 4 0 .0 0 4 9 .0 0 6 9 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 3 9 .0 0 0 0 .0 1 0 312 . 0 0 3 4 .0 0 9 8 .0 0 4 4 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 .0 0 7 8 .0 0 0 0 .0 1 0 413 . 0 0 4 1 . 0 1 4 9 .0 0 5 7 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 3 9 .0 0 0 0 .0 1 5 714 . 0 0 3 1 .0 0 0 0 .0 0 4 5 . 0 0 4 4 . 0 0 3 8 . 0 0 0 0 . 0 0 3 9 . 0 0 0 0 .0 1 0 615 . 0 0 4 7 . 0 2 5 3 .0 0 3 2 . 0 0 4 4 . 0 0 0 0 . 0 0 7 5 . 0 0 0 0 . 0 0 0 0 . 0 0 5 416 . 0 0 3 2 .0 1 0 3 .0 0 3 2 . 0 0 0 0 . 0 0 0 0 . 0 0 1 9 .0 0 7 9 . 0 0 0 0 . 0 0 5 417 . 0 0 2 2 .0 0 0 0 .0 0 3 2 . 0 0 4 4 . 0 0 3 8 . 0 0 0 0 .0 0 0 0 .0 0 0 0 . 0 0 5 418 . 0 0 4 1 .0 0 5 2 .0 0 5 2 . 0 1 7 9 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 .0 0 0 0 . 0 1 0 919 . 0 0 2 9 .0 0 5 2 .0 0 3 9 . 0 0 4 5 . 0 0 3 8 . 0 0 0 0 .0 0 0 0 . 0 0 0 0 . 0 0 5 520 . 0 0 1 9 . 0 0 0 0 .0 0 3 3 . 0 0 4 5 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 5 521 . 0 0 2 2 .0 1 0 5 . 0 0 2 0 . 0 0 0 0 . 0 0 0 0 . 0 0 3 8 . 0 0 0 0 . 0 0 0 0 . 0 0 0 022 . 0 0 2 2 . 0 0 5 3 .0 0 2 0 . 0 0 4 6 . 0 0 0 0 . 0 0 0 0 .0 0 4 0 . 0 1 1 0 . 0 0 0 023 . 0 0 3 2 .0 0 5 3 .0 0 3 3 . 0 0 4 6 . 0 0 0 0 . 0 0 0 0 . 0 1 2 0 . 0 0 0 0 . 0 0 0 024 . 0 0 4 5 .0 0 5 4 .0 0 5 3 . 0 0 4 6 . 0 0 7 6 . 0 0 1 9 . 0 0 0 0 . 0 0 0 0 . 0 1 6 825 . 0 0 5 2 .0 1 0 8 .0 0 6 6 . 0 0 4 6 . 0 0 7 7 . 0 0 0 0 .0 0 4 0 . 0 0 0 0 . 0 1 1 426 . 0 0 3 3 .0 1 0 9 .0 0 4 7 . 0 0 4 6 . 0 0 0 0 . 0 0 0 0 .0 0 0 0 .0 0 0 0 .0 0 5 727 . 0 0 3 9 . 0 0 5 5 .0 0 4 7 . 0 0 4 7 . 0 0 3 9 . 0 0 0 0 .0 0 8 1 .0 0 0 0 . 0 0 5 828 . 0 0 2 6 . 0 0 5 5 . 0 0 4 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 .0 0 4 1 .0 0 0 0 . 0 0 5 829 . 0 0 2 3 .0 0 0 0 .0 0 2 7 . 0 0 4 7 . 0 0 3 9 . 0 0 0 0 . 0 0 0 0 .0 0 0 0 . 0 0 5 830 . 0 0 2 6 .0 0 0 0 .0 0 4 1 . 0 0 4 7 . 0 0 0 0 . 0 0 0 0 .0 0 4 1 . 0 0 0 0 . 0 0 5 931 . 0 0 1 7 . 0 1 1 2 .0 0 2 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 032 . 0 0 1 0 . 0 0 0 0 .0 0 2 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 .0 0 5 933 . 0 0 0 3 . 0 0 0 0 .0 0 0 7 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 034 . 0 0 0 0 . 0 0 0 0 .0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 .0 0 0 0 . 0 0 0 035 . 0 0 0 3 . 0 0 0 0 .0 0 0 7 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 .0 0 0 0 . 0 0 0 0

N 3 , 6 0 3 241 1 , 7 5 4 249 289 599 304 116 256

Table 5.12 Survival Analysis of Time to Rearrest After Sentence Completion: Standard Errors of Proportions Exposed to Risk Recidivating During Specific Month (Hazard Rates)

i ii i

{ j Court DispositionI II I

|Pooled! Off. Proba- Prob. ChrgsMonth j Sample {Ignore Dismiss Acquit Prog, tion Jail + Jail Pend.

0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 .0 0 0 0 . 0 0 0 0 . 0 0 0 01 . 0 0 3 0 . 0 0 9 0 . 0 0 3 0 . 0 0 9 6 . 0 0 8 2 . 0 1 2 7 .0 1 3 6 .0 4 2 8 . 0 1 9 92 . 0 0 1 8 . 0 1 2 2 .0 0 2 8 . 0 0 7 6 . 0 0 7 2 . 0 0 3 4 .0 0 5 3 .0 0 0 0 . 0 1 2 03 . 0 0 2 1 . 0 1 4 4 . 0 0 2 7 . 0 0 0 0 . 0 1 0 4 . 0028 .0 1 2 1 . 0 2 2 2 .0 1 2 54 . 0 0 1 7 . 0 1 1 1 . 0 0 2 8 . 0 0 7 7 . 0 0 4 3 . 0 0 0 0 .0 0 7 8 . 0 0 0 0 . 0 1 1 1

continued...

113


Table 5.12 (Continued)---------- 1-------------1-----------------------------

1 1 1 1 1 1 1 j| P o o l e d 1

M o n t h | S a m p l e | I g n o r e D i s m i s s

C o u r t

A c q u i t

D i s p o s i t i o n

O f f . P r o b a - P r o g . t i o n J a i l

P r o b . ■f J a i l

C h r g sP e n d .

5 . 0 0 1 7 . 0 0 5 0 . 0 0 3 2 . 0 0 6 3 .0 0 6 1 .0 0 3 5 . 0 0 5 6 . 0 0 0 0 .0 1 0 46 . 0 0 1 6 .0 0 8 8 . 0 0 2 6 . 0 0 4 5 .0 0 7 6 .0 0 2 0 . 0 0 8 0 . 0 0 0 0 .0 0 4 77 . 0 0 1 2 .0 0 9 0 . 0 0 2 0 . 0 0 4 5 .0 0 4 4 .0 0 2 0 . 0 0 4 1 . 0 0 0 0 .0 0 4 88 . 0 0 1 0 .0 0 5 2 . 0 0 1 6 . 0 0 6 5 .0 0 0 0 .0 0 0 0 . 0 0 0 0 . 0 1 6 3 .0 0 4 89 . 0 0 1 3 .0 0 0 0 .0 0 2 2 . 0 0 4 6 .0 0 0 0 . 0 0 2 0 . 0 0 8 2 . 0 0 0 0 .0 0 8 3

10 . 0 0 0 9 .0 0 5 2 . 0 0 1 3 . 0 0 0 0 .0 0 4 4 .0 0 2 0 . 0 0 4 1 . 0 0 0 0 .0 0 4 911 . 0 0 1 1 .0 0 5 3 . 0 0 2 2 . 0 0 0 0 .0 0 0 0 .0 0 0 0 . 0 0 4 2 . 0 0 0 0 .0 0 6 912 . 0 0 1 0 .0 0 7 5 . 0 0 1 8 . 0 0 0 0 . 0 0 0 0 .0 0 0 0 . 0 0 5 9 . 0 0 0 0 .0 0 7 013 . 0 0 1 0 .0 0 9 3 . 0 0 1 9 . 0 0 0 0 . 0 0 0 0 .0 0 0 0 . 0 0 0 0 . 0 0 0 0 .0 0 8 714 . 0 0 1 0 . 0 0 0 0 . 0 0 1 8 . 0 0 4 6 .0 0 4 5 .0 0 0 0 . 0 0 4 2 . 0 0 0 0 .0 0 7 215 . 0 0 1 2 .0 1 2 3 . 0 0 1 5 . 0 0 4 6 .0 0 0 0 . 0 0 4 1 . 0 0 0 0 . 0 0 0 0 .0 0 5 116 . 0 0 1 0 .0 0 7 9 . 0 0 1 5 . 0 0 0 0 .0 0 0 0 .0 0 2 1 . 0 0 6 0 . 0 0 0 0 .0 0 5 117 . 0 0 0 8 .0 0 0 0 . 0 0 1 5 . 0 0 4 7 .0 0 4 5 .0 0 0 0 . 0 0 0 0 . 0 0 0 0 .0 0 5 218 . 0 0 1 1 .0 0 5 7 . 0 0 2 0 . 0 0 9 4 .0 0 0 0 .0 0 0 0 . 0 0 0 0 . 0 0 0 0 .0 0 7 419 . 0 0 0 9 .0 0 5 7 . 0 0 1 6 . 0 0 4 8 .0 0 4 5 .0 0 0 0 . 0 0 0 0 . 0 0 0 0 .0 0 5 220 . 0 0 0 8 .0 0 0 0 . 0 0 1 6 . 0 0 4 8 .0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 .0 0 5 321 . 0 0 0 8 .0 0 8 1 . 0 0 1 2 . 0 0 0 0 .0 0 0 0 . 0 0 2 9 . 0 0 0 0 . 0 0 0 0 . 0 0 0 022 . 0 0 0 8 .0 0 5 8 . 0 0 1 2 . 0 0 4 8 . 0 0 0 0 .0 0 0 0 . 0 0 4 3 . 0 1 1 7 . 0 0 0 023 . 0 0 0 9 . 0 0 5 8 . 0 0 1 2 . 0 0 4 8 . 0 0 0 0 .0 0 0 0 . 0 0 7 4 . 0 0 0 0 .0 0 0 024 . 0 0 1 1 .0 0 5 9 . 0 0 1 7 . 0 0 4 9 .0 0 6 4 . 0 0 2 1 . 0 0 0 0 . 0 0 0 0 .0 0 9 225 . 0 0 1 2 .0 0 8 4 . 0 0 2 1 . 0 0 0 0 .0 0 6 5 .0 0 0 0 . 0 0 4 3 . 0 0 0 0 .0 0 7 626 . 0 0 1 0 .0 0 6 0 . 0 0 1 9 . 0 0 4 9 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 .0 0 5 427 . 0 0 1 0 .0 0 0 0 . 0 0 1 9 . 0 0 0 0 .0 0 4 6 .0 0 0 0 . 0 0 6 2 . 0 0 0 0 . 0 0 5 528 . 0 0 0 9 . 0 0 6 0 . 0 0 1 8 . 0 0 0 0 .0 0 0 0 .0 0 0 0 . 0 0 4 4 . 0 0 0 0 . 0 0 5 529 . 0 0 0 8 .0 0 0 0 . 0 0 1 4 . 0 0 4 9 .0 0 4 6 .0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 5 530 . 0 0 0 9 .0 0 0 0 . 0 0 1 8 . 0 0 4 9 . 0 0 0 0 .0 0 0 0 . 0 0 4 4 . 0 0 0 0 .0 0 5 631 . 0 0 0 6 . 0 0 6 0 . 0 0 1 3 . 0 0 0 0 .0 0 0 0 .0 0 0 0 . 0 0 0 0 . 0 0 0 0 .0 0 0 032 .0 0 0 5 . 0 0 0 0 . 0 0 1 0 . 0 0 0 0 .0 0 0 0 .0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 5 633 .0 0 0 3 .0 0 0 0 .0 0 0 7 . 0 0 0 0 . 0 0 0 0 .0 0 0 0 . 0 0 0 0 . 0 0 0 0 .0 0 0 034 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 .0 0 0 0 . 0 0 0 0 . 0 0 0 0 .0 0 0 035 . 0 0 0 3 . 0 0 0 0 .0 0 0 7 . 0 0 0 0 .0 0 0 0 .0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0

Table 5.9 presents the cumulative survival rates (i.e., cumulative proportions not

recidivating throughout the follow-up) for each group, and table 5.11 displays the hazard rates

(i.e., proportions at risk recidivating during specific months). The standard errors are presented in

table 5.12. The number of persons falling into each group is presented at the bottom of each table.

If a person recidivated before a sentence was served completely, (s)he was included in the

percentage of recidivists falling into the first month of the follow-up period. This permits an

examination of the incapacitative effects of some dispositions versus others. The discussion that

114


follows steois from careful examination of these rates in conjunction with their standard errors in

order to determine significantly different estimates of recidivism across disposition groups.

The cumulative survival rates presented in the last row o f table 5.9 in conjunction with

their standard errors indicate that offenders with pending charges had the highest recidivism rate

(the lowest cumulative survival rate) of any group, with 34 percent recidivating by the last time

period. This is consistent with the significant main effect for pending charges discussed

previously. In contrast, none of the remaining survival rates were significantly different from all of

the other groups (which is also consistent with the multivariate analysis of main

effects). These indicate that anywhere from 11 to 26 percent of a particular cohort recidivated

overall.

Offenders whose charges were pending and those receiving split sentences of probation

and jail were the quickest to recidivate, with 20 percent or more being rearrested by the end

of month 7. The hazard rates in table 5.11 also indicate much higher risks of recidivism occurring

during the early months of follow-up for these groups. Although suspects sentenced to jail

alone were typically worse risks compared to most others in the sample, sentences of jail alone

served to at least delay recidivism longer through its incapacitative effect. Table 5.11

reveals a ten percent difference in risk during the first month between those sentenced to jail

versus probation and jail.

Suspects whose cases were ignored ended up having some of the highest recidivism

likelihoods in the sample, but recidivists from this group actually had longer delays to rearrest

compared to those receiving probation, jail, and split sentences. The hazard rates for this group

are significantly higher for later periods (e.g., months 15, 21, 26, and 31) compared to all other

groups. It is possible that the factors leading to inaction on the part of prosecutors were related to

this delay, but the fact remains that a very high proportion of the group ultimately

recidivated for domestic violence (26 percent), and risk was significantly greater in later periods for

this group alone.

115


Similar patterns of delay can be observed for suspects whose cases were dismissed,

acquitted, and sentenced to either the offender program or probation. However, unlike those

whose cases were ignored, the offender program and probation groups had among the lowest

recidivism likelihoods in the sample. In short, the risk evaluations of these individuals made by

prosecutors and judges seemed to be fairly well-informed. N ote the significantly higher hazard

rates for the more severe disposition groups during the early periods of follow-up. M onth by

month, these more severe disposition groups have consistently lower cumulative survival rates

compared to the "better risks." Note the 11 percent difference in survival rates by the end of

month 24 for probationers versus those sentenced to probation and jail. This difference was

established after only two years and was maintained throughout the remaining three years of the

follow-up.

116


CHAPTER SIX

DISCUSSION

Introduction

As a result of the inconsistent findings across replication studies, the deterrent effect of

arrest for domestic violence recidivism remains unknown (Sherman et al. 1992). The inconsistent

results have been attributed to methodological differences and/or problems among the replication

studies preventing definitive conclusions from being reached. Furthermore, the domestic violence

studies tell us little, if anything, about the effectiveness of criminal justice sanctions beyond arrest.

Given that a majority of jurisdictions have adopted mandatory arrest policies, it is possible that

variation in the decision to arrest has been reduced. There is a significant amount of variation,

however, in what happens to offenders subsequent to arrest. Not all offenders who are arrested are

prosecuted, convicted, and sentenced. Further, variation exists in the sentences of those offenders

who are convicted. The current study explored this issue by examining the effectiveness of criminal

justice sanctions, other than arrest, on domestic violence recidivism.

The inconsistent findings across replication studies have also been attributed to differences

in individual offender characteristics and variations in the social structure of an individual’s

community. A consistent finding in the domestic violence studies is that arrest appears to serve as

a deterrent for individuals who are married, employed and/or have at least a high school education

(Berk et al. 1992; Dunford et al. 1990; Sherman et al. 1992). This finding has been attributed to

Toby’s idea of "stake in conformity"; that is, individuals who have more to lose if arrested for

committing a crime are more likely to be deterred through formal criminal justice sanctioning

(Berk et al. 1992; Dunford et al. 1990; Sherman et al. 1992). This study explored this issue further

by first examining the direct effects of stake in conformity on recidivism as well as the interaction

between formal criminal justice sanctions and an extensive list of stake in conformity measures,

otherwise known as informal social control processes.

117


I t has also been suggested that the social structure of a community could influence the

likelihood of recidivism. According to Braithwaite (1989), criminal sanctions can produce different

results depending on individual-level and community-level characteristics. Rates of urbanization

and residential mobility influence an individual’s attachment to their community. Criminal justice

sanctions are more likely to deter future criminal behavior if administered in communities

containing significant proportions of mutually dependent individuals (Braithwaite 1989).

Structural characteristics include, but are not limited to, such factors as the unemployment rate o r

proportion of single individuals residing within a community. The current study tested this

hypothesis by examining the direct effects and interactions between criminal justice sanctions and

measures of structural characteristics derived from neighborhoods within a single city.

This hypothesis is derived from a number of ecological theories, including social disorganization

theory, Braithwaite’s theory of reintegrative shaming, and Hagan’s idea of social capital.

Traditional ecological theories were originally put forth to explain rates of criminal behavior across

particular jurisdictions by examining the structural characteristics of neighborhoods. Sampson

(1991) makes the argument, however, that the structural characteristics of a neighborhood can

produce contextual effects, thereby influencing the behavior of individuals.

The Influence of Formal Social Control

It was hypothesized that more severe dispositions would result in lower likelihoods of

recidivism. Specifically, the disposition variables that were examined included whether or not a

suspect was prosecuted, convicted, and sentenced as well as the type of sentenced received. Both

the short-term and long-term effects were assessed. Aspects of this hypothesis were supported

when examining the bivariate relationships between case processing and outcome. Suspects who

had charges filed against them, participated in an offender treatment program, were convicted and

sentenced to probation and jail, and did not have old charges pending at the time of arrest were

less likely to recidivate, had fewer rearrests for domestic violence, and longer time periods until

rearrest. Once the stake in conformity and the control variables were introduced into the model,

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however, the only significant relationship remaining was whether o r not a suspect had pending

charges. The finding that suspects were more likely to recidivate for domestic violence if charges

from previous arrests were still pending suggests that the speed of case processing may be

important for reducing the likelihood o f domestic violence recidivism. According to deterrence

theory, celerity of punishment is an important, and often times missing, component of c rim in al

justice sanctioning (Gibbs 1975). If an offender has been arrested for an offense, but has yet to be

brought to trial, the association between the punishment and the offense may not occur for the

offender. A potential consequence of an offender failing to make such an association is that he or

she is less likely to be deterred from future offending and would be more likely to recidivate

compared to an offender with no pending charges. Perhaps a preoccupation with the quality and

length of sentences has take attention away from the possible deterrent effects of efficiency in case

processing. Although using the measure of whether or not a suspect had pending charges at the

time o f arrest was only offered as a crude indicator of case processing, this finding suggests that

future research exploring this issue is warranted.

The results from the multivariate models confirms the importance of including measures

of informal social control in any study examining the impact of formal criminal justice sanctioning.

In the context o f deterrence theory, these findings are also consistent with previous studies

examining the effect of arrest on domestic violence in which no deterrent effect was found (e.g.,

Dunford et al. 1990; Hirschel et al. 1990; Sherman et al. 1992). The relative consistency in findings

across models predicting each of the three dependent variables also suggests that any study

examining the impact of criminal justice sanctioning should explore the effects on preventing

recidivism altogether, reducing the occurrence of rearrest, and delaying recidivism.

The Influence of Informal Social Control

It was also hypothesized that the likelihood of rearrest would be lower, the number of

rearrests fewer, and the time to rearrest would be longer for suspects with a significant stake in

conformity (as indicated by the presence of informal social control measures). Although all of the

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stake in conformity measures (except family ties) were significantly related to recidivism in the

bivariate model, once the controls were introduced, family ties was the only stake in conformity

measure at the individual level that maintained a significant relationship, however, this relationship

was not in the direction as hypothesized. Suspects who were residing with their spouse and/or

children were more likely to be rearrested, had more rearrests and shorter time periods to rearrest.

This finding is inconsistent with previous research on the deterrent effect of arrest in that married

suspects were less likely to recidivate (Sherman et al. 1992). The inconsistency could be a product

of examining the difference between marital status and whether or not the suspect was actually

residing with their spouse.

The significant main effect of family ties is not trivial, since the idea of "opportunity" is

central to many criminological theories (Cohen and Felson 1979; Gottfredson and Hirschi 1991).

In the context of understanding domestic violence recidivism, an offender’s family ties may not

adequately reflect his or her attachment to immediate family members. Nonetheless, this

operational concept applied to domestic violence offenders may tap into an interaction between

impulsivity and opportunity that is more reflective of Gottfredson and Hirschi’s concept of low

self-control (1990. From a routine activities perspective, residing with your spouse could create an

opportunity for domestic violence to occur in that the spouse becomes an "attractive target" due to

a lack of guardianship (Cohen and Felson 1979). Of course, further research is needed to test

these ex-post facto hypotheses.

Drawing from the outcome of the present study, the results for the main effects do not

reject the possible importance of informal social controls for predicting domestic violence in the

population of all adults, since this was a study of domestic violence offenders only. Consistent

support has been found in previous research supporting the importance of informal social control

to explain criminal versus non-criminal behavior (e.g., Bainbridge 1989; Burkett and W ard 1993;

Evans et al. 1995; Sampson and Laub 1993). However, for the population of offenders only,

individual-level informal social controls may do little to influence recidivism by themselves.

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The results from the current study render additional support for the idea that the

structural characteristics of a community may have a contextual effect on the behavior of

individuals residing in those communities. Suspects residing in neighborhoods with a significant

proportion of "high stake" individuals (measured in terms of having low levels of concentrated

neighborhood disadvantage) were less likely to be rearrested, had fewer rearrests and had longer

time periods until rearrest compared to suspects residing in neighborhoods with fewer "high stake"

individuals. According to Sampson et al. (1997), "concentrated disadvantage" has a negative impact

on collective efficacy or the willingness of residents to intervene in situations for the common good

of the community and is considered to be a component of informal social control. A possible

explanation for the significant effect of neighborhood concentrated disadvantage on recidivism

could be that offenders who return to communities characterized by a breakdown in informal

social control continue their criminal behavior because they still have little to lose by engaging in

criminal behavior.

Although previous studies examining the contextual effects of neighborhood characteristics

on individual behavior have focused primarily upon delinquency, support has been found for the

influence of aggregate factors upon rates of domestic violence. Miles-Doan (1998) found that

neighborhoods characterized by "resource deprivation" have significantly higher rates of domestic

violence compared to middle and upper class neighborhoods.

It should also be noted that the effects o f neighborhood concentrated disadvantaged in the

present study were consistent for male and female offenders. This finding lends support for the

idea that structural characteristics do not appear to be sex specific. Although the present study

could have benefited from a larger sample of female offenders, the findings suggest a possible rival

hypothesis to traditional theories of female violence against male partners. It has been argued that

a significant amount of violence by females originates from a prior abusive relationship. Females

who abuse their partners do so out of fear or retaliation (Dobash and Dobash 1992). Straus et al.

(1980) argues, however, that the motivations underlying much female violence are similar to those

for males who abuse their female partners.

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Interactions between Formal and Informal Social Control

Although neighborhood concentrated disadvantage maintained a significant direct effect

with recidivism, inconsistent with prior research, no consistent significant interaction effect was

found. None of the criminal justice sanctions "worked" any better when administered in a

community context o f "high stake" individuals o r resulted in an escalation effect in communities

without a significant number of individuals with a stake in conformity (with the one exception

found in table 5.8). One possible explanation might be that the current study was conducted in a

single jurisdiction only. A second explanation is that because the direct effect was significant, the

effect is not mediated by court disposition.

Also, no consistent interaction effects were found for family ties (with the few exceptions

found in table 5.7) suggesting that "opportunity" may be an influence regardless o f the type of

sanction imposed. A t the very least this finding may also render support for a greater use and

enforcement of restraining orders in cases o f domestic violence.

In contrast to the findings for main effects, support was found for the idea that certain

elements of stake in conformity (specifically residential stability) interact with court dispositions to

produce significant effects on recidivism. Offenders who lived at their current address for at least

three years who were formally sanctioned by the criminal justice system were less likely to

recidivate, had fewer rearrests, and longer time periods to rearrest. Inconsistent with previous

research, no significant interaction effects were found for education and employment (with the

exception found in table 5.7). Prior research examining interactions between arrest and

employment, marital status, and education did not include a measure of residential stability,

suggesting that residential stability may be a more important indicator of stake in conformity. This

finding supports research by Sampson (1992) regarding length of residence and an individual’s

attachment to their community, which in turn is correlated with crime. According to Braithwaite

(1989), sanctions can produce deterrent effects when administered within a context of

interdependency that includes an individual’s attachment to their neighborhood. Despite the fact

that the present study does not offer a direct measure o f community attachment, residential

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stability can be used as a proxy of this concept. In contrast to particular sentencing schemes that

generate more lenient dispositions for offenders with more community ties, the findings for the

interactions suggest tha t it may not be worthwhile to "go easier" on offenders with a higher stake

in conformity (with respect to residential stability) since these appear to be the types of individuals

most likely deterred by more severe sanctions. It is possible that offenders who have lived longer

at their current address may feel stigma and shame because they are more likely to be known to

their neighbors than offenders who have only resided at their current address for a short period o f

time.

Conclusions and Directions for Future Research

The focus on significant interaction effects is not meant to trivialize the few significant

main effects presented here. From a theoretical standpoint, the significant result for concentrated

neighborhood disadvantage extends previous criminological research yielding support for the

importance of neighborhood characteristics on crime in general to an understanding of recidivism

among a fairly large population of offenders in society (i.e., domestic violence offenders). It is also

insightful that the significant influence of concentrated neighborhood disadvantage on recidivism

does not appear to be mediated by type of court disposition.

The significance of concentrated neighborhood disadvantage is also consistent with studies

of other predatory crimes revealing higher violent crime rates as well as higher individual-level

likelihoods of violent crime in more socially "disorganized" neighborhoods. Further inspection of

geographic maps of the distribution of residences for persons arrested and rearrested for domestic

violence (attached as appendices six and seven) reveals a preponderance of arrests closer to the

central business district of Cincinnati. The heavy concentration in census tracts closest to the city’s

major thoroughfares (1-71,1-74 and 1-75) is also consistent with the idea that poorer

neighborhoods with more transient populations and high amounts of family disruption often

correspond with higher rates of predatory crime.

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Regarding the statistical control variables, although whether or not a suspect was ever

incarcerated for an offense other than domestic violence was used as an indicator of prior record,

this variable may be more substantially meaningful than anticipated. Domestic violence offenders

who are eclectic in their offending may be more criminogenic in general, and as a consequence

may be unlikely to be deterred through formal criminal justice sanctioning.

Finally, the results derived bom the life table analysis suggest the importance o f lengthy

follow-up periods when examining the impact of criminal justice sanctioning, however, a three-year

follow-up period should be sufficient if the goal is to "catch" the vast majority of recidivists.

Again, offenders with pending charges were the poorest "risks" in terms of recidivating sooner.

Despite the finding that offenders sentenced to jail were also poor risks, jail does provide an

incapacitative effect. Given that suspects whose cases were ignored had high rates of failure

(particularly during the later months) also reinforces the idea that "doing something" may be better

than "doing nothing" in the long run. This idea is also reinforced by the finding regarding

offenders sentenced to the offender program or to probation had the lowest recidivism likelihoods

in the sample. Although the offender program was only a two day voluntary counseling session

paid for by the suspect, this finding does suggest that it may be worthwhile for jurisdictions to

consider expanding treatment options for offenders.

Although the present study helps to overcome some of the limitations in previous research

examining the effects of arrest on domestic violence by exploring sanctions beyond the arrest stage

and including individual- and aggregate-level measures of informal social control (including an

exploration of the interactions between such), it is necessary to recognize the limitations of the

study and to suggest directions for future research. First, it is recognized that the current study was

a non-experimental design (unlike the original Minneapolis Domestic Violence Experiment and

subsequent replications). As a consequence, there was no random assignment of cases to each of

the disposition groups. If future research could incorporate a procedure by which judges were

willing to allocate sanctions randomly, m ore insight may be gained into the effectiveness of court

dispositions on domestic violence recidivism.

124


Second, although the sample for the current study was significantly large enough to

include all of predictors in the models, the study would have benefited from having even more

cases. This is especially true given the sheer number of suspects whose cases were either ignored,

never prosecuted, dismissed, and acquitted (and because of such were never sanctioned).

Third, despite the large number of informal social control measures tapping into Toby’s

stake in conformity hypothesis, the study would have benefited from more direct measures derived

from the offender’s actual attitudes about whether or not they feel "attached" to their communities

and felt stigmatized as a consequence of criminal justice sanctioning. A related limitation of the

current study is that only official measures of arrest and rearrest were used. It is possible that

within the "dark figure" of domestic violence (overcome with victimization data) variation exists in

the importance of informal and formal social controls.

Finally, it would be worth pursuing in future research, interactions between the individual-

and aggregate-level stake in conformity measures to determine if informal social control operates

within specific community contexts. In other words, would high stake offenders be less likely to

recidivate only if they reside in high stake communities?

Despite the aforementioned limitations, the current study offers a contribution to the

existing body of literature examining the influence of criminal justice sanctioning on misdemeanant

domestic violence. The inconsistent findings across the domestic violence replication studies

questioned the effectiveness of arrest in deterring domestic violence, and the results from the

current study suggest that sanctioning beyond the arrest stage appears to do little to reduce

domestic violence as well. In contrast to the lack o f influence of criminal justice sanctioning, it

would be worthwhile to explore the possibility of increasing the efficiency with which these types of

cases are handled by the courts.

The current study also builds upon a growing body of research suggesting that

neighborhood characteristics have an influence on individual behavior. This finding has been well

documented with offender and non-offender populations (e.g., Elliott et al., 1996; Gottfredson et

al., 1991; Gunn et al., 1993; Sampson 1991; Simcha-Fagan and Schwartz 1986) and the current

125


study suggests that the same may be true for recidivists. Specifically, offenders residing in

neighborhoods with high degrees o f "concentrated disadvantage" appear to be more likely to be

rearrested than offenders residing in neighborhoods with less "concentrated disadvantage".

Offenders who return to an environment lacking in informal social control may be more likely to

continue their behavior because they still have little to lose by engaging in crime.

Finally, the current study expands the existing body of literature examining the interactions

between formal and informal social control to determine if criminal justice sanctioning "works"

better for individual’s with a stake in conformity. Previous research failed to include residential

stability as an indicator of stake in conformity and the results from the present study suggest that

this may be a more important factor (compared to education, employment, and family ties) in

determining the deterrent effect of criminal justice sanctioning.

126


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134


APPENDIX ONE

CORRELATION MATRIX

Correlations: VI V2 V3 V4 V5 V6 V7 V8 V9 VI0 VI1 V12VI 1.00V2 • 88* 1.00V3 - . 9 4 * -. 84* 1.00V4 . 0 6 * • 06* -. 06* 1.00V5 - . 0 3 -.02 .02 — .26* 1.00V6 - . 0 2 -.03 .02 — . 0 7 * -.26* 1.00V7 -.03 - . 0 5 * .02 - . 0 7 * -.27* -.07* 1.00V8 .01 . 0 1 .02 -.11* -.43* -.12* -.12* 1.00V9 .02 . 0 2 -.03 -.08* -.29* -.08* -.08* -.13* 1.00V10 • 05* . 0 6 * -. 06* -.04* -.18* -.05* -.05* -.08* -.05* 1.00VI1 .06* . 0 8 * -.01 -.13* -.48* -.13* -.13* .84* -.06* .35* 1.00V12 . 0 4 * . 0 4 * -. 05* -.08* -.31* -.08* -.09* -.14* .76* .40* .16* 1.00V13 . 1 2 * . 1 3 * -. 13* .00 .01 .01 -.03 -.03 .02 .01 -.02 .03V14 - . 0 5 * - . 0 7 * . 04* -.02 -.00 .02 .04* .05* -.02 -.07* .01 -.05*V15 - . 0 5 * - . 0 4 .03 -.03 .00 .02 .03 .03 -.02 -.04* -.00 -.04*VI fi . 0 1 .02 .01 -.01 -.02 -.01 .02 .07* .00 -.05* .04* -.01V17 - . 0 9 * -. 08* • 09* -.05* .01 .00 .07* .03 -.07* -.02 .01 -.07*VI8 - . 0 5 * -. 04* .06+ -.05* .01 .01 .,03 .00 -.03 .01 .00 -.02V19 — . 0 6 * - . 0 6 * .06* -.03 -.04* .04* .08* .04* -.05* -.01 .03 -.05*V20 . 0 9 * . 0 7 * -. 08* .05* -.06* -.00 .00 .00 .05* .04* .02 .06*V21 - . 0 6 * - . 0 6 * . 07* -.01 -.01 .04* .02 .01 -.01 -.01 .00 -.00V22 . 1 4 * . 1 6 * -. 15* .16* -.07* -.03 -.04* -.02 .08* .02 -.00 . 08*V23 . 0 7 * . 0 7 * - . 0 7 * .03 -.00 -.03 -.05* -.04* .08* .05* -.00 . 11*

Correlations: VI3 V14 V15 V16 V17 . V18 V19 V20 V21 V22 V23V13 1.00V14 -.02 1.00V15 .00 .07* 1.00VI6 -.05* .05* .17* 1.00V17 -.06* .12* .13* .09* 1.00V18 -.04* .09* .02 .01 .32* 1.00V19 -.00 .15* .06* .09* .17* .11* 1.00V20 .01 -.05* .00 -.06* .07* .11* .02 1.00V21 -.06* .11* .19* .03 .17* .06* .05* .01 1.00V22 .06* -.07* -.05* -.04* -.11* -.05* -.07* .12* .10* 1.00V23 .05* -.12* -.13* -.06* -.14* -.05* -.13* .16* -.00 .20* 1.00

♦significant at the 0.01 probability level.Legend:

Vl=whether or not a suspect was rearrested for domestic violence during a two year fixed follow-up period.

V2“number of rearrests for domestic violence during a fixed two year follow-up period. V3=number of months to rearrest for domestic violence during a varied follow-up period. V4=no charges filed against a suspect.V5“charges filed, but subsequently dropped against a suspect.V6=suspect acquitted at trial.V7=whether or not an offender was sentenced to offender treatment program.V8=whether or not an offender was sentenced to probation.V9=whether or not an offender was sentenced to jail.V10=whether or not an offender was sentenced to jail and probation.Vll“length of probation sentence.V12=length of jail sentence.V13=whether or not a suspect had pending charges at the time of arrest for domestic

violence.V14=leval of education.V15=length of residence.V16“family ties.V17=stability of employment.V18“type of employment.V19=degree of concentrated disadvantage within a suspect's census tract.V20=gender of suspect.V21=age of suspect.V22=numbar of prior convictions for violent misdemeanors.V23*=whether or not a suspect had prior institutional commitments for an offense other than

domestic violence.

135


APPENDIX TWO

DEPARTMENT OF PRETRIAL SERVICESINTERVIEW

a.m.I n t a r v l f l w a r D a ta : T i m a : p .m .

CENTRAL INTAKE ONLYN a m a *

B.P.

S n n la l S e c u r i t y N n .: I n t a k e I D . N o . :

A g e : R a c e : B W O S e x : M F C a s h O n P a r s n n :

R e m a r k s :

Outstanding warrants, holders, standard bonds:

VERIFICATIONR E S ID E N C E & F A M IL Y T IE S YES NO

d u rren l AIL A ddress: Address:

If n o n eLenoth: Telephone: con tac t:

R esides with: Relationship:

Lived in C ounty la s t five y ea rs? Yes No If no. explain:

P rior A ddress: Lenoth:

O W M C ontact with: Relationship:

Marital S ta tus: S M 0 S ep W CL No. ol Children: Supported by:

E M P L O Y M E N T & E D U C A T IO Nb urren tEm ployer: Position:

If unem ployed,Lenoth: FT PT S easons! m e a n s of support:

S p o u se 's Employer: FT PT S easo n a lPrior If unem ployed, Em ployer: Lenoth: last em ploym ent date :L ast G rade If currently enrolled in Com pleted: school, oive details:

H E A L T H S T A T U SC urrently trea tedb y Physician? Y es No If yes. w here? Frequency:

Condition: If chronic, explain:

D I S C L O S U R E O F C R IM IN A L H IS T O R YPrior Convictions: Probation? Parole?

Prior S ta te or F ederal Commitments? P.O .:

___________________________________ R E F E R E N C E S A N D B O N D IN F O R M A T IO N ________R eference N am e R elationship Telephone Work Number DPI R ide Ver. By

Verified Bond or Cash

INT. VER. P O IN T S C H E D U L E REMARKS:3 3 OVER ONE YEAR AT PRESENT RESIDENCE2 2 OVER 6 MONTHS AT PRESENT RESID EN C E! OVER ONE YEAR AT PRESENT AND PRIOR RESIDENCE

RESIDENCE 1 1 BETWEEN 6-12 MONTHS AT PRESENT AND PRIOR RESIDENCE / 4-6 MONTHS AT PRESENT RESIDENCE1 1 ' LIVEO IN COUNTY LAST FIVE YEARS0 0 LESS THAN 4 MONTHS AT PRESENT RESIDENCE OR 6 MONTHS AT PRESENT & PRIOR RESIOENCE3 3 LIVES WITH SPO U SE AND CHILDREN

FAMILY 2 2 LIVES WITH SPO U SE OR CHILDREN OR PARENT/GUARDIAN

TIES 1 1 LIVES WITH OTHER RELATIVE / HAS WEEKLY CONTACT WITH FAMILY MEMBER0 0 LIVES ALONE OR WITH NON-RELATIVE O R NO FAMILY CONTACT3 3 PRESENT JOB ONE YEAR OR MORE

MEANS 2 2 PRESENT JO B 6-12 MONTHS / HOMEMAKER WITH CHILDREN OR FULL TIME STUDENTO F 1 1 PRESENT JO B 3-6 MONTHS / CURRENT AND PRIOR JO B OVER 6 MONTHS

SUPPO RT 1 1 UNEMPLOYMENT COMPENSATION. W ELFARE. DISABILITY. FAMILY SUPPORT0 0 NOT EMPLOYED OR OTHERWISE SU PPO R TED OR UNDER 3 MONTHS AT CURRENT JOB

HEALTH 1 1 POOR HEALTH — AT LEAST MONTHLY CONTACT WITH M.D. OR CLINIC / PREGNANCY OR OLD AGE (65-*-)2 2 NO PREVIOUS CONVICTIONS0 0 ONE MISDEMEANOR CONVICTION

PRIOR *1 •1 TWO OR THREE MISOEMEANOR CONVICTIONSCRIMINAL •1 •1 ONE FELONY CONVICTIONHISTORY •2 •2 FOUR OR FIVE MISDEMEANOR CONVICTIONS

•2 •2 TWO OR THREE FELONY CONVICTIONS*3 *3 SIX OR MORE MISDEMEANOR CONVICTIONS•3 -3 FOUR OR MORE FELONY CONVICTIONS

TOTAL (VIOL. TOTAL (VIOL MINOR ______MISD. CONV. _______ MISD.) _______F E L CONV. _______FEL.) _______MISD.

PENDING ______CHARGE

TOTAL POINTS AND ELIGIBILITY

□ ELIGIBLE OWN RECOGNIZANCE □ NOT ELIGIBLE:. □ ELIGIBLE REPORTING RELEASE □ SCREENEO ELECTRONIC MONITORINCB O N D IN F O R M A T IO N Telephone

Charge 1: Charge 2: Charge 3: Charge 4:

. C ase

.C a s e #_

_ # _

Bond AmL/Type Bond AmL/Type Bond Amt/Type Bond AmL/Type

TB Room A Room B / BRDATETIMEJUDGEPOLICE PREFERENCEDISPOSITION / REMARKS:

CHIP □

136Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

APPENDIX THREE

CODEBOOK DOMESTIC VIOLENCE DATA

Information taken from intake interview formatVariableDate

(month, day, year)S.S.#D.O.B.

(month, day, year)Race

B = 1 W = 2 0 = 3

SexM = 1 F = 2

B.P.I.D.Intake I.D.#Address

(write out as is)(zip code)

Length of Residence (months)Lived in County Last 5 Years

Yes = 1 No = 2

Marital StatusS = 1 M = 2 D = 3 Sep = 4 W = 5 CL = 6

# Children (actual #)

Employer(write out as is)

Record #1

1

1

1

1

1

2

2

Column #1 - 6

8 - 1 6 18 - 23

25

27

29 - 33 35 - 42 44 - 78

79 - 80 1 - 3

5

9 - 1 0

12 - 40

137


Variable Record # Column #Length of Employment (weeks) 2 42-45

Unemployed = 0000Full-Time/Part-Time/Seasonal 2 47

FT = 1PT = 2Seasonal = 3

Last Grade Completed 2 49Some H.S. = 1H.S. Degree = 2 Some College = 3 Bachelors = 4Some Graduate = 5 M.S. or M.A. = 6 Ph.D. = 7

Currently Treated by Phys. 2 51Yes = 1 No = 2

Prior Inst. Committ. 2 53Yes = 1 No = 2

Family Ties 2 570 = lives alone or with non-relative or no family contact1 = lives with other relative/has weekly contact with family

member2 = lives with spouse or children or parent/guardian3 = lives with spouse and children

Means of Support 2 5 8 - 5 90 = not employed or otherwise supported or under 3

months at current job -1 = unemployment compensation, welfare, disability,

family support1 = present job 3-6 months/current and prior job over 6 months2 = present job 6- 12 months/homemaker with children or full

time student3 = present job one year or more

Health 2 61(not coded)

Total Misdemeanor Convictions 2 63-64(coded as actual #)

138


Variable Record #Violent Misdemeanors 2

(coded as actual #)Total Felony Convictions 2

(coded as actual #)Violent Felonies 2

(coded as actual #)Minor Misdemeanors 2

(coded as actual #)Pending Charges 3

(coded as actual #)Pretrial Release 3

FOR = 1 (eligible own recognizance)NE = 2 (not eligible)ERR = 3 (eligible reporting release)SCM = 4 (screened electronic monitoring)

# Current Charges 3(coded as actual #)

I n f o r m a t i o n t a k e n fro m a r r e s t r e p o r t s a n d c o u r t f i l e s :

V a r i a b l e R e c o rd #

Rearrested for domestic violence duringthe follow-up period 3

Column66 - 67

69 - 70

72 - 73

75 - 76

1 - 2

Colum n #

0 = no1 = yes

Length of time to rearrest

(# weeks)Length of pretrial detention

(# days)Convicted

0 = no1 = yes

1 0 - 1 2

14-16

139


Variable Record # Column #Jail 3 18

0 = not sentenced to jail1 = sentenced to jail

Time spent in jail 3 20-21(# weeks)

Probation 3 230 = not sentenced to probation1 = sentenced to probation

Time spent on probation 3 24-26(# weeks)

Offender Program 3 280 = no1 = yes

140


APPENDIX FOUR

Zero-order Relationships Between Measures of Prior Record and Likelihood of Rearrest andNumber of Rearrests for Domestic Violence Recidivism (Standardized Coefficients Reported)

Measures of Prior Record

Rearrested(pseudo-r)

# rearrests (r)

Total violent misdemeanors (ordinal scale) .173 .165

Total minor misdemeanors (ordinal scale) .108 .099

Total misdemeanors (ordinal scale) .123 .118

Total felonies (ordinal scale) .096 .088

Total misdemeanors + felonies (ordinal scale) .121 .117

Total petty+ misdemeanors+ felonies (ordinal scale) .120 .114

Total violent felonies (ordinal scale) .081 .080

Total violent priors(ordinal scale) .151 .156

continued..

141


Appendix Four (Continued)

Measures of P rior Record

Rearrested(pseudo-r)

# rearrests (r)

Total violent misdemeanors (ratio scale) .144 .154

Total minor misdemeanors (ratio scale) .098 .098

Total misdemeanors (ratio scale) .115 .118

Total felonies (ratio scale) .085 .067

Total misdemeanors + felonies (ratio scale) .122 .119

Total petty-t- misdemeanors+ felonies (ratio scale) .123 .121

Total violent felonies (ratio scale) .079 .080

Total violent priors (ratio scale) .138 .149

142


APPENDIX FIVE

Tests of Significant Differences in Relationships Between Court Dispositions and RecidivismAcross Stake in Conformity Groups: Predicting Rearrest for Domestic Violence(t-tests Reported)

1111 Court DisuositionsStake in jConformity | No charges Dropped Acquitted OffenderGroups j Hied charges at trial Program

Educationsome hs v. hs degree -1.48 -2.41* -2.58* -0.69

some hs v. some coll+ -0.20 -0.31 -0.79 2.23*

hs degree v. some coll-t- 1.11 1.80 1.50 3.36*

Residence length 1 yr v. 2 yrs -0.11 -1.55 -0.42 -0.661 yr v. 3-5 yrs 0.53 1.10 2.27* 1.761 yr v. 6+ yrs -0.29 -0.29 -0.29 0.322 yrs v. 3-5 yrs 0.37 1.76 1.54 1.452 yrs v. 6+ yrs -0.19 -0.15 -0.18 0.243-5 yrs v. 6+ yrs -0.24 -0.25 -0.29 0.19

Family tiesnot w/spouse +/- child v. w/spouse +/- child 1.03 -1.18 0.37 -0.54

Same job > 1 yearno v. yes -0.31 -0.77 -0.95 -0.12

Skilled occupationno v. yes 1.46 0.55 0.20 0.44

Concentrated disadvantage first quarter v. second quarter 1.39 1.24 1.60 0.81

first quarter v. last half 0.86 0.78 0.92 0.52

second quarter v. last half -0.72 -0.62 -1.05 -0.43

continued...* statistically significant at the 0.05 probability level.

143


Appendix Five (Continued)

111 Court DisnositionsStake in |Conformity | Probation Jail Probation+ PendingGroups j Jail Charges

Educationsome hs v. hs degree -1.64 -1.21 -1.83 -0.82

some hs v. some coll+ -1.58 0.06 0.06 -0.88

hs degree v. some coll+ -0.29 1.14 1.49 -0.62

Residence length1 yr v. 2 yrs 0.46 -1.30 -1.05 -3.23*1 yr v. 3-5 yrs 2.94* 1.82 0.86 -2.00*1 yr v. 6+ yrs -0.29 -0.25 0.05 -3.41*2 yrs v. 3-5 yrs 1.35 1.98* 1.28 1.212 yrs v. 6+ yrs -0.21 -0.13 0.04 -0.263-5 yrs v. 6+ yrs -0.31 -0.24 0.03 -1.43

Family tiesnot w/spouse +/- child v. w/spouse +/- child 0.07 -0.04 -1.21 -0.27

Same job > 1 yearno v. yes -1.47 -0.41 1.10 -0.18

Skilled occupationno v. yes -2.14* -0.85 1.74 1.87

Concentrated disadvantage first quarter v. second quarter 0.21 1.08 0.65 -0.42

first quarter v. last half 0.26 0.75 0.25 -0.89

second quarter v. last half 0.02 -0.46 -0.52 -0.39

continued.

* statistically significant a t the 0.05 probability level.

144



Tests of Significant Differences in Relationships Between Court Dispositions and RecidivismAcross Stake in Conformity Groups: Predicting Number of Rearrests forDomestic Violence (t-tests Reported)

1111 Court DisnositionsStake in |Conformity | No charges Dropped Acquitted OffenderGroups | filed charges a t trial Program

Educationsome hs v. hs degree -1.51 -1.55 -1.93* 0.11

some hs v. some coll+ 0.13 0.36 -0.24 1.68

hs degree v. some coll+ 1.91 2.17* 1.89 2.66*

Residence length1 yr v. 2 yrs -0.20 -1.84 -0.49 -0.881 yr v. 3-5 yrs -0.73 -0.02 1.10 0.601 yr v. 6+ yrs -0.36 -0.35 -0.16 0.232 yrs v. 3-5 yrs -0.31 1.33 1.03 1.022 yrs v. 6+ yrs -0.10 0.84 0.20 0.663-5 yrs v. 6+ yrs 0.18 -0.24 -0.81 -0.20


Same job > 1 yearno v. yes -0.13 -0.89 -0.71 -0.54

Skilled occupationno v. yes 2.38* 0.76 0.54 0.65

Concentrated disadvantagefirst quarter v. second quarter 1.07 0.34 0.85 0.11


second quarter v. last half -0.38 -0.31 -0.72 -0.44


145



1111 Court Disnositions

Stake in jConformity | Probation Jail Probation+ PendingGroups | JaU Charges

Educationsome hs v. hs degree -1.44 -0.61 -0.55 0.52

some hs v. some coll 4- -1.41 0.52 1.38 0.79

hs degree v. some co 11+ -0.38 1.45 2.35* 0.82

Residence length 1 yr v. 2 yrs 1.20 -1.71 -1.72 -5.73*1 yr v. 3-5 yrs 2.94* 1.30 -0.06 -3.05*1 yr v. 6+ yrs 0.91 -0.43 1.87 -2.18*2 yrs v. 3-5 yrs 1.00 2.04* 1.17 2.58*2 yrs v. 6+ yrs -0.20 0.76 2.03* 2.83*3-5 yrs v. 6+ yrs -1.26 -1.11 1.37 0.65

Family tiesnot w/spouse +/- child v. w/spouse +/- child -0.33 0.32 -0.33 -2.82*

Same job > 1 yearno v. yes -0.46 0.37 2.71* 1.45

Skilled occupationno v. yes -2.41* 0.17 2.94* 4.05*

Concentrated disadvantagefirst quarter v. second quarter -0.73 0.32 -0.59 1.68

first quarter v. last half -0.44 -0.43 -1.15 1.22

second quarter v. last half 0.49 -0.87 -0.41 -1.10

continued.


146



Tests of Significant Differences in Relationships Between Court Dispositions and RecidivismAcross Stake in Conformity Groups: Predicting Time to Rearrest forDomestic Violence (t-tests Reported)

1111 Court Disnositions

Stake in |Conformity ] No charges Dropped Acquitted OffenderGroups | filed charges at trial Program

Educationsome hs v. hs degree -1.28 -2.24* -2.44* 0.43

some hs v. some coll+ -0.48 -0.17 -0.69 1.76

hs degree v. some coll+ 0.50 1.72 1.34 1.95:

Residence length 1 yr v. 2 yrs 0.61 -0.80 0.59 -0.351 yr v. 3-5 yrs 1.77 2.22* 3.15* 2.25’1 yr v. 6+ yrs -0.26 0.08 0.11 1.052 yrs v. 3-5 yrs 0.55 1.95 1.42 1.682 yrs v. 6+ yrs -0.40 0.33 -0.16 0.823-5 yrs v. 6+ yrs -0.80 -0.61 -1.08 0.04


Same job > 1 year no v. yes -0.43 -0.99 -1.39 -0.91

Skilled occupationno v. yes 1.03 -0.37 -0.35 -0.70

Concentrated disadvantagefirst quarter v. second quarter 0.33 0.26 0.25 1.01

first quarter v. last half 1.37 1.27 1.66 1.29

second quarter v. last half 1.03 0.99 1.38 0.06


147



1111 Court DisnositionsStake in |Conformity j Probation Jail Probation+ PendingGroups i Jail Charges

Educationsome hs v. hs degree -0.88 -0.87 -1.53 -0.46

some hs v. some coll+ -0.42 -0.19 0.69 -1.17

hs degree v. some co 114- 0.24 0.51 2.02* -1.39

Residence length1 yr v. 2 yrs 0.54 -0.18 0.81 -2.41*1 yr v. 3-5 yrs 2.82* 2.91* 1.81 -1.341 yr v. 64- yrs 0.45 0.26 0.16 -2.37*2 yrs v. 3-5 yrs 1.27 1.92 0.44 0.972 yrs v. 64- yrs 0.10 0.24 -0.20 -0.153-5 yrs v. 64- yrs -0.55 -0.79 -0.48 -1.03

Family tiesnot w/spouse 47- child v. w/spouse 4-/- child 0.56 -0.64 -1.09 0.49

Same job > 1 year no v. yes -0.70 -0.60 0.63 -0.01

Skilled occupationno v. yes -1.04 -1.16 -0.21 1.91

Concentrated disadvantage first quarter v. second quarter -0.38 -0.29 1.04 -0.20


second quarter v. last half 2.04* 1.72 0.28 -1.88


148


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Distribution of Domestic Violence A rrestees in Hamilton County, Ohio

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DISTRIBUTION OF DOMESTIC VIOLENCE ARRESTEES IN CINCINNATI, OH

Map Layers jClty of CincinnatiB |W a t e r Area

jCounty Boundary H P "interstate Highway

• Arrestee Residence 0 1 2 3

7 r ' %' • ■ » . • • .

rers y

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