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This is to certify that the

thesis entitled

FACTORS RELATED TO PROBATION RECIDIVISM AMONG PROBATIONERS
WITH DRUG PROBLEM IN MICHIGAN

presented by

Byongook Moon

has been accepted towards fulfillment
of the requirements for

 

 

MASTER OF SCIENCE degree in CRIMINAL JUSTICE

 

sz<

/

4' /Major professor
Date SEPTEMBER 21. 2000 /

0-7639 MS U is an Affirmative Action/Equal Opportunity Institution

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FACTORS RELATED TO PROBATION RECIDIVISM AMONG
PROBATIONERS WITH DRUG PROBLEM IN MICHIGAN

By

Byongook Moon

A THESIS

Submitted to
Michigan State University
In partial fulfillment of the requirements
for the degree of

MASTERS OF SCIENCE
School of Criminal Justice

2000

ABSTRACT

FACTORS RELATED TO PROBATION RECIDIVISM AMONG
PROBATIONERS WITH DRUG PROBLEM IN MICHIGAN

By

Byongook Moon

While several studies have shown the association between drug use and
recidivism, few studies have specifically examined the mechanism by which drug
users Violate and recidivate when compared to non-drug users on probation.

Therefore, the focus of this study is to compare drug users to non—drug users
on probation in relationship to; a) the individual traits; b) the probation conditions
imposed; c) the predictors of probation violations; and d) the predictors of recidivism.

Significant differences in the individual traits were found between drug users
and non-drug users. Drug users were more likely to be single, to have lower level of
education, to be unemployed at the time of offense, to have alcohol history, and to
have previous criminal records. In the predictors of violation, some conditions such
as drug testing and alcohol treatment were significamly related to violations for both
drug users and non-drug users. For predictors of revocation, the study found that
probationers who committed more violations were more likely to fail their probation
whether they were drug users or not. This finding suggests that probation conditions
may facilitate opportunities for probationers to violate and recidivate whether they use

drug or not.

ACKNOWLEDGMENTS

I would like to thank my thesis chair, Dr. Sheila Maxwell for her help, advice,
and patience. I would also like to thank the other three members of my committee for
their time and participation in the study, Dr. Charles Corley, Dr. Mahesh Nalla, and
Dr. Vincent Hoffman.

Finally, I would like to thank my parents, my wife, Eunji Youn, and my

daughter, Sandra Moon for their unfailing love and support.

iii

TABLE OF CONTENTS

LIST OF TABLES
CHAPTER
I. INTRODUCTION

Introduction . .
Purpose of the Study .
Relevance

II. METHODOLOGY

Research Questions

Sample

Description of Measures
Independent Variable
Dependent Variable

Analytic Technique

HI. RESULTS

Characteristics of Probatloners .

Conditions of Probation Imposed on Drug Users and Non-drug Users .

The Predictors of Conditions Imposed on Probationers

Factors Influencing Violations

Factors Influencing Violations for Drug Users and Non-drug Users.
Factors Influencing Revocations

VI. DISCUSSION

References

iv

Page
vi

001—1

11

11
11
12
12
13
14

15

15

.18

.21
.25
.29
.33

.38

.42

Table

1.1

1.2
1.3

1.4

1.5

1.6

List of Tables

Characteristics of Probationers with Drug Problem and
Non-drug Problem

Conditions Imposed on Drug Users and Non-drug Users .
Characteristics of Probationers Regressed on the Number .
of Conditions

Predictors of Violations Using OLS Regression for the
Entire Sample

Predictors of Violations for Drug Users and Non-drug User .
Using OLS Regression

Predictors of Successful Discharge Using Logistic Regression .

Page
1 7

20
22

27
31

35

Chapter I

INTRODUCTION

In 1995, more than 1.5 million persons were incarcerated in the nation’s jails
and prisons and these populations are increasing (BJ S, 1995). As a way to alleviate
overcrowding in prison and jails, probation has been used as the most common
sentencing alternative (Petersilia, 1997). In the last decade, increasing numbers of
offenders have been sentenced to probation. The Bureau of Justice Statistics (1996)
reported that almost 60 percent of all convicted ofienders were supervised on
probation.

Despite its rapid use and popularity, probation has been criticized for high
rates of recidivism (Petersilia, 1997). The Rand (1985) study found that
approximately 65 percent of probationers were arrested before their probation period
expired. Langan and Cunniff (1992) found that among 79,000 felony probationers in
state courts in 32 US. counties, 43 percent were rearrested for a felony within three
years of sentencing (Geerken & Hayes, 1993). Some researchers have argued that
high recidivism rates are likely to threaten the public’s safety and may have the
unexpected effect of increasing the problem of prison and jail overcrowding (Irish
1989; Petersilia, 1985; Benedict & Corzine, 1997). Recent budget limitations and
increased caseloads per probation officer make it almost impossible for probation
officers to supervise probationers carefully. However, even in this situation, more
serious offenders had been continuously sentenced to probation to make enough space
in prisons and jails for habitual offenders (Petersilia, 1997).

Recidivism studies have found many factors that influence a probationer’s

likelihood of violating probation conditions (Morgan, 1993). These factors have
ranged from the probationer’s age, prior criminal records, employment history, and
several others (Morgan, 1993). Most consistent among factors that had been
identified as precursors of recidivism had been the offenders’ history of drug use
(Chaiken and Chaiken, 1982; Turner, Petersilia, and Deschenes, 1992). Specifically,
drug use had been found in many studies to be predictive of recidivism among
probationers and parolees. In Pritchard’s (1979) review of studies on the relationship
between drug use and recidivism, pre-prison drug use was found to be positively
related to criminal recidivism in all nine studies that investigated this factor.
Similarly, Gottfiedson (1967) noted that drug use had been consistently useful in
identifying subgroups having a higher probability of recidivism among convicted
offenders generally (Bradshaw, 198 7). Researches have shown that offenders have
extensively used illegal drugs and there have been extensive illegal behaviors among
users of all types of illegal drugs (Bradshaw, 1987). Studies have also shown that
offenders who have used illicit drugs prior to arrest are at high risk of failing to appear
in court (McBride & McCoy, 1993).

In explaining the relationship between drugs and crime, researchers. have
generally referred to two main rationales. One is the pharmacological effects of
drugs, which reduce inhibitions against aggression or other criminal activities
(McGlothlin, 1979). The other often cited rational is economic motivation (Goldstein,
1992). Using drugs continuously is very expensive, often leading the drug addicts to
commit crime to obtain money to sustain their expensive habits.

The pharmacological effects suggest that some individuals may become

excitable and irrational and may commit crime as a result of short or long-term drug

2

use (McGlothlin, 1979). There are illegal drugs that have often been perceived as
producing aggressive and violent behaviors.» These have been the hallucinogenic
drugs such as stimulants, barbiturates, LSD, PCP, and amphetamines and aggressive
and violent behavior have been seen as possible effects of these drugs. Some
evidence shows that the hallucinogenic drugs may so strongly affect perceptions. As
a result, aggressive and violent behaviors may occur (McBride and Swartx, 1990).

Barbiturates appear most likely to lead to violence. Tinklenberg et al.,
examined incarcerated delinquents and found that a barbiturate was identified as the
single substance most likely to enhance assaultiveness. Also, Collins and other
researchers (1982) studied self-reported of aggravated assaults and robberies by
nearly 8,000 drug treatment program and found that the highest proportions of
persons committing aggravated assaults or robberies were those who identified their
primary drug problem as barbiturate use.

The irritability associated with withdrawal experienced fiom using heroin may
lead to violence. Goldstein (1979) examined heroin-using prostitutes and found that
they often linked robbing and/or assaulting clients with the withdrawal experience.
These women reported that they preferred to talk a “trick” out of clients’ money, but
if they were experiencing withdrawal symptoms, they might attack the client, take his
money, purchase sufficient heroin to “get straight”. A similar process has been
reported with regard to cocaine. Cocaine users characterize being high on cocaine as
a positive experience but the cocaine “crash”, which is withdrawal symptom, has been
described as a period of anxiety and depression in which external stimuli may be
reacted to in a violent fashion.

Studies that have examined the effects of amphetamine use have consistently

concluded that regular amphetamine use produces paranoia delusions that produce
violence. Ellinwood (1971) examined thirteen offenders who had committed
homicides while under the influence of amphetamines. He found that the use of
amphetamine led to paranoid thought patterns and delusions, consequently to commit
murder. Asnis and Smith (197 8) also examined the effect of amphetamines on
producing violence among amphetamine users who had committed homicides. They
found that the development of delusional paranoia and social isolation might result in
extreme violence. Studies have shown that individuals who are narcotic addicts and
heavy cocaine users are also fi'equently involved in criminal activities (Harrison &
Gfroerer, 1992). Research has shown that narcotic addicts commit crimes more often
during periods of elevated narcotic use. Though the impact of hallucinogenic drugs
and amphetamines is an important factor of the drug-violence relationship, many
experts agree that such violence and criminal behaviors are usually more dependent
on other factors such as personality, environment, culture than drug itself. Further,
the violence did not appear to be a systematic, reflexive result induced directly by the
drug, but rather a very idiosyncratic and complex response that seemed to occur in
particular individuals at unpredictable time (Burns & Lerner, 1976; McBride &
Swartz, 1990).

Another explanation for the association between drug use and crime is the
economic motivation due to the high cost of illicit drugs (Harrison & Gfi'oerer, 1992).
Economic motivation model suggests that drug users engage in economically oriented
crime in order to support drug habits (Goldstein, 1992). There are very few legitimate
ways in which most drug addicts can afford to buy illegal drugs. Faced with the high

cost of drug use above what the drug users can afford legitimately, drug users are

4

forced to become involved in criminal activities such as robberies, shoplifting, and
prostitution as a means of supporting their drug habit.

Several self-report studies found the types and frequency of crimes committed
primarily to buy drugs (McGlothlin, Anglin, and Wilson, 1978). Ball et al.,
interviewed 243 Baltimore heroin addicts arrested or identified between 1952 and
1971. One of their main findings was that heroin addicts commit a staggering amount
of crime, and that the impact of illegal drug use on criminality was pervasive and
long-lasting. They were also convinced that criminality not only coinCided with
heroin use, but that the need to purchase drugs was actually the cause of some crime
(Jarvis & Parker, 1989;175). Johnson et a1 (1983)., interviewed 201 street opiate
users in East Harlem (Jarvis & parker, 1989). The interviews questioned drug users’
previous day’s income from crime and legitimate sources, drug purchased, sold, or
distributed, and any involvement with methadone or other forms of treatment. The
study found most of their subjects derived most of their income from crime and that
the level of drug consumption was related to the nature and seriousness of the drug
addicts’ criminality.

One of the ways to understand the effect of economic motivation of drug users
on crime is to examine the types of crimes committed by drug users and non-drug
users. Eckerman, Bates, Rachell, and Poole (1971) examined 1,889 arrestees from 6
metropolitan cities and found that types of crimes committed by drug users were
different from those committed by non-drug users; non-drug users were more likely to
be arrested for crimes against the person than drug users; drug users were more often
charged with property crime than were non-drug users. Researches show that most

heroin addicts do not commit violent acquisitive crimes if nonviolent alternatives

exist (Goldstein, 1992). Research has shown that income fi'om property crime
escalates with increasing narcotic use and non-property crime does not covary with
levels of narcotic use (McGlothlin, 1979; Harrison and Gfroerer, 1992). Especially,
researches have consistently found that criminal acts increases following drug use,
and that arrrests for drug offenses and property offenses decline with decreasing drug
use (Dembo and Getreu, 1993). This suggests that the association between crime and
drug is attributable to economic motivations which support drug users commit
property crimes to support drug addictions (Harrison and Gfi’oerer, 1992).

Petersilia, Greenwood, and Lavin (197 8) analyzed 49 incarcerated male who
were convicted for armed robberies in California. These robbers committed a total of
855 robberies. More than 50 percent of the robbers reported regular use of drugs and
almost 60 percent of them reported that they were under the influence of drugs while
committing crimes. The most fiequently cited reason for committing crimes was to
obtain money to buy drugs. Harlow (1991) interviewed jail inmates and reported that
13 percent among them interviewed acknowledged that they committed current
offenses to secure money to buy illegal drugs (Walters, 1998). Longitudinal studies
conducted in California, and Great Britain tend to confirm that heroin use
corresponded with periods of increased economically oriented crime (Walters, 1998).

Other explanation often mentioned for relationship between drug use and
crime is that in response to the widespread increase in the use of drugs during the
1980s, criminal justice system has responded with an unprecedented emphasis on
apprehending drug dealers and users in record numbers. New laws increased the
likelihood and length of incarceration for offenders charged with drug offense (F agan,

1994). Drug offenders have also received harsher treatment at all stages of case

6

processing compared with non-drug offenders. In New York City, drug offenders
have a higher probability of felony charges at arrest, are less likely to make bail, and
are more likely to be held in pretrial detention without bail (F agan, 1994). The result
was unprecedented increases in the rates of arrest, incarceration, and legal supervision
of drug users (Longshore, Anglin, and Hser, 1994). Arrests for drug offenders
increased by more than 125% during the 1980s As a result, drug cases in court
increased by 56% between 1983 and 1987 in a sample of 26 cities nationwide. Also,
drug offenders have inundated community corrections. Not only the number of
offenders on probation and paroles reached record level, but also officers in the field
of community correction think that the composition of caseloads has become more
serious, particularly with respect to drug involvement (Turner, Petersilia, and
Deschenes, 1994).

Over the years, special conditions such as alcohol/drug test and substance
abuse treatment have been increasingly imposed on many of probationers who use
drugs (Petersilia, 1997). With the public’s more punitive mood, the availability of
inexpensive drug test, and many probationers with substance abuse problems, the
number of conditions imposed have increased (Petersilia, 1997). The public and the
criminal justice system believe that harsher conditions will prevent probationers from
committing crimes and produce higher compliance levels for probationers. The
Bureau of Justice Statistics (1997) examined the first national survey of adults on
probation under the supervision of State and local agencies. It found that almost all
probationers (99%) had one or more conditions to their sentence required by the court.
Among such conditions imposed on probationers were supervision fees, employment,

boot camp, electronic monitoring, mandatory drug testing, and substance abuse

7

treatment. Almost half of probationers had more than 5 conditions. Also, it found
that almost 41 percent of adults on probation got drug or alcohol treatments as a
sentencing condition and 32 percent of probationers were subjected to mandatory
drug testing.

But interestingly, studies focusing on conditions to probation have found that
their imposition may lead to frequent violation of conditions and higher recidivism
rates among probationers (Langan & Cunniff, 1992). Several studies found that more
stringent conditions of probation may increase the chances of failure. Langan and
Cunniff (1992) examined felons on probation and found that 55 percent of those
probationers had some special condition such as drug testing and many probationers
failed to follow their conditions of probation. Langan (1994) also found that almost
50 percent of probationers simply did not comply with the conditions of probation,
and only 50 percent of known violators went to jail or prison for non-compliance. In
1997, Sims and Jones examined 2,850 North Carolina felony probationers and found
that a total of 26 percent of felony probationers were revoked because of technical
violations such as testing positive for drugs and only 13 percent of probationers were
terminated because of new crimes. Further evaluation on felony probation shows that
the more stringently programs enforce their punitive conditions, the more they are
likely to exacerbate prison overcrowding (Tonry and Hamilton, 1995). It concluded
that stringently requiring conditions of probation might not be having the desired
effects of reducing recidivism rates and decreasing prison overcrowding.

Obviously, efforts to deal with drug crime with harsh punishment have‘been
increased (Fagan, 1994). Arrests, prosecutions, convictions, prison sentences, and

parole revocations of drug offenders all have increased sharply in a short time (Goerdt

& Martin, 1989; Fagan, 1994). But the politics of dealing with drug cases increased
the likelihood that drug offenders will be incarcerated, regardless of their criminal

histories or the comparative risks they posed to public safety (Belenko et al., 1991).

Purpose of the Study

While several studies have shown the association between drug use and
recidivism, few studies have specifically focused on the mechanism by which drug
users recidivate when compared to non-drug users. The focus of this study, therefore,
is in comparing the recidivism of probationers who regularly used drugs and those
who didn’t. Specifically, these two groups will be compared in terms of; 3) their rates
and patterns of probation violations; b) the probation conditions imposed; and, c) the
predictors of recidivism between these two groups. By comparing the patterns and
predictors of recidivism between drug users and non-drug users, we are better able to
delineate those factors that may be specifically unique to drug users and the reasons

for their higher likelihood of recidivism that had been identified in previous research.

Releyance

The United State has been confi'onted with an epidemic of illicit drug use and
drug related crimes (MacKenzie, 1994). The tremendous increasing number of drug-
involved offenders has presented enormous problems such as drug-related crimes,
drug use by youth, and prison overcrowding for the criminal justice system (Wish,
1992). As a result, the criminal justice system has struggled to search for the effective
ways to reduce the rate of drug use and drug-related crimes. Since “get tou ” era,

there had been less emphasis on treatment and an increased emphasis on the use of

9

prison to deter drug use and drug crimes (Mackenzie and Uchida, 1994). Since then,
law and policy have increasingly relied on sanctions to deter drug use and drug selling
(Fagan, 1994; Mackenzie and Uchida, 1994).

Although there have been numerous studies about probation, few have
specifically examined recidivism of probationers with drug problems, the
effectiveness of conditions commonly imposed on drug users such as drug testing and
drug treatment, and their effects on recidivism of probationers. By clarifying these
issues, the present study seeks to inform policy makers on the specific links between

probation conditions imposed on drug users and recidivism.

10

Chapter H

h/[ETHODOLOGY

The study uses a combination of quantitative research techniques to look at
drug users who are on probation, using a sample of probationers in Michigan.

Following is a discussion of the research question and the related hypotheses.

Research Quesgons

Based on the theories and research evidence discussed above, the research
questions the study seeks to answer are: 1) what are characteristics of probationers
with drug problem compared to non-drug probationers? 2) what are types of
conditions imposed on probationers with drug problem compared those with no drug
problem? 3) which factors are related to probation violation for drug users compared
to non-drug users? 4) which factors are related to probation recidivism for drug users

compared to non-drug users?

Sample

The data was collected by Dr. Sheila Maxwell and Dr. Timothy Bynum from
the School of Criminal Justice at Michigan State University with collaboration of the
Michigan Department of Corrections. The Department of Corrections in Michigan
provided access to files and databases to collect information about each probationer
included in the sample.

The sample of 1,500 probationers was randomly selected from the population

of 4,021 probationers who were sentenced to probation in February and March of

11

1996 in Michigan. But due to expected missing files, 200 additional probationers
were sampled. A total of 126 cases could not be located. In many cases, the files
were simply not in the offices they were supposed to be, in others, probationers
originally sentenced in one county would move and their probation file would be
transferred to a different county. Therefore, the total number of cases collected is
1,574 and it represents almost 40 percent of the total number of offenders sentenced
to probation for these months. This sample size will provide an adequate number of
cases for analysis. Also the lapse of 3 years was given to assess recidivism rates of

probationers.

Description of Measures
This section provides detailed descriptions of the data elements used in this

research.

Indemdent Variabls

Drug use of probationers is obtained from a dichotomous variable (yes-no)
that indicated the drug use of the probationer. Also, variables such as age, gender,
race, marital status, education level, employment history, juvenile records, the
numbers of misdemeanor and felony convictions, and type of offense of probationers
with drug problems are used as control variables. Age was in computed using the
probationers’ date of birth available. Race distinguishes among Caucasian, African-
American, Hispanics, and Asians. Due to the overall low fiequencies of Asian and
Hispanics among the probationers in the state of Michigan, these race categories were

recoded into a new category as “other races”. Marital status was dichotomized into

12

married and not married. Education level was coded including categories for up to 11
th grade, 12th grade / GED, some college or more. Employment history is obtained
from dichotomous variable (yes-no), which indicated the employment status of the
offender at the time of arrest. Information of offenders’ prior offenses is obtained and
coded as prior felony convictions, prior misdemeanor convictions, and previous
juvenile records. Type of current offense is coded in three categories including
nonassaultive, drug, and assaultive offenses.

The specific conditions imposed on drug users are analyzed to examine their
overall effect on violations of probation and recidivism. These conditions include
punitive conditions such as alcohol and drug testing, electronic monitoring, fines, and
community services. Treatment conditions such as alcohol and substance abuse
treatment, vocational/employment requirements are also included. These conditions
imposed on probationers with drug problem are all coded into dichotomous (yes-no)

variables.

Dependent Va_riables

In this study, probation recidivism was analyzed using probationer’ discharge
type. Recidivism was conceived as the probationers’ failure to successfully complete
their probation terms. Recidivism took place when one of the following discharge
types was present: jail pending violation, revocation, discharge without improvement,
and jail. Probation discharge type was recoded into a dichotomous variable where
only two outcomes for this dependent variable were “failure” as “O” and “successfirl”
“1”.

as Also, number of violation up to eight was used as dependent variable.

13

Analytic Technique

Chi-square test is used to address the question 1, that is, “what are
characteristics of probationers with drug problem compared to non-drug using
probationers?” Specifically, this study will consider gender, race, age, alcohol abuse,
employment status, marital status, and highest grade completed.

Chi-square test is also used to address the question 2, that is, ‘Vvhat are types
of conditions imposed on probationers with drug problem compared those with no
drug problem?” Chi-square tests are used to identify significant differences in
probation orders imposed on between drug users and non-drug users.

OLS is used to address the question 3, that is, “what factors are related to
probation violation for drug users compared to non-drug users?” OLS regression tests
are used to identify statistically significant factors of violation for drug users
compared to non-drug users.

The logistic regression models are used to address the question 4, that is,
“what factors are related to probation recidivism for drug users compared to non-drug
users?” Because the logistic regression model is the most reasonable statistical tool
given the binary nature of the dependent variable used in the study, which is

probationers’ discharge type.

14

Chapter III

RESULTS

This chapter presents the results of the study. It presents information on the
characteristics of probationers and the effects of these characteristics on probation
outcomes. Also, it presents information on the types of conditions imposed on drug
users and their impact on violations and the outcome of discharge. This chapter
includes results of bivariate examinations, OLS regression, as well as multivariate

models using logistic regression.

Meteri stics of Probationers

Table 1.1 presents baseline information on the probationers sampled for this
study, subdivided by drug use. The probationers in the sample were predominantly
male (80%), the majority were White (54%), most were single (68%), and a little over
half had up to a 12th grade or some college education (52%). Almost half of
probationers (51%) were employed at the time of offense and one third (31%) had a
history of alcohol abuse. Regarding criminal history, most of probationers had no
previous juvenile record (82%), 46 percent had previous misdemeanor records, and 24
percent had previous felony records.

As shown in Table 1.1, some characteristics are vastly different between drug
users and non-drug users. Marital status, highest grade completed, employment
status, alcohol abuse, offense category, prior juvenile record, prior misdemeanor
record, and previous felony record were significantly related to probationers’ drug

use.

15

The measure of marital status indicated that 71 percent of drug users were
single compared to 66 percent of non-drug users. The contingency table for highest
grade completed showed that a little over 90 percent of drug users had less than 12th
or GED. Only 8 percent of drug users had some college experience compared to 16
percent of non-drug users. Employment status at the time of ofl’ense showed that 46
percent of drug users were employed compared to 55 percent of non-drug users. The
contingency table for alcohol abuse showed that probationers using drugs were more
likely to have histories of alcohol abuse. Almost half of probationers using drugs had
alcohol abuse history compared to only 21 percent of those who had no drug
problems. Regarding offense category, drug users committed more drug offenses
compared to non-drug using probationers. Forty two percent of drug users committed
drug offense compared to 13 percent of those probationers with non-drug abuse
history. More than 60 percent of non-drug using probationers committed non-
assaultive offenses while less than half of drug using probationers committed non-
assaultive offenses. When examining previous juvenile record, it was found that
those probationers with drug abuse history were more likely to have juvenile record
than those with no drug abuse history. Twenty two percent of drug users had juvenile
records compared to 15 percent of non-drug users. Table 1.1 also shows that more
than 50 percent of those probationers with drug abuse history had previous
misdemeanor records while only 39 percent of those with no drug abuse history had
previous misdemeanor records.

Table 1.1 also shows that those probationers with a drug abuse history were
more likely to have previous felony record than those with no drug abuse history.

Thirty two percent of those probationers with drug abuse history had one or more

16

Table 1.1 - Characteristics of Probationers with Drug Problem and Non-drug Problem

 

 

Non-drug Users Drug Users Total
% N % N % N
Sex
Male 78 687 82 549 80 1 236
Female 22 191 19 125 20 316
Race
White 56 487 53 355 54 842
Black 41 362 45 305 43 667
Other 3 29 2 14 3 43
Marital Status
Single 66 581 71 474 68 1055
Married 16 144 11 75 14 219
Divorced/separated/widowed 17 152 18 122 18 274
Age
Mean 30 30 30
Median 27 29 28
Highest Grade Completed
Up to 11th grade 46 404 51 340 48 744
12th grade/GED 38 337 41 272 40 609
Some college or more 16 136 8 54 12 190
Employment History
No 45 387 54 359 49 746
Yes 55 477 46 303 51 780
Alcohol Abuse
No 79 692 56 375 69 1067
Yes 21 186 44 299 31 485
Offese Category
Non-assaultive 66 576 45 304 57 880
Drug 1 3 1 13 42 286 26 399
Assaultive 22 1 89 1 3 84 18 273
Previous Juvenile Record
No 85 732 78 514 82 1246
Yes 1 5 127 22 146 1 8 273
Previous Misdemeanor Record
No 61 520 45 299 54 81 9
Yes 39 338 55 362 46 700
Previous Felony Record
No 82 720 68 460 76 1 180
Yes 18 157 32 214 24 371
Notes: * = p<.05 ** = p<.01 “* = p<.001

17

previous felony history compared to 18 percent of those with no drug abuse history.
In summary, it seems that there are some differences of characteristics
between drug users and non-drug users. Probationers with drug problem were more
likely to be single, to have lower level of education, to be unemployed at the time of
offense, to have alcohol abuse history, and to be charged with non-assaultive crimes.
Regarding previous crime records, drug users were more likely to have previous

involvement with the criminal justice system.

C(mditions of Probation Imposed on Drug Users a_r_1_d Non-drum

Having shown the general characteristics of probationers, according to drug
use, the next contingency tables examine conditions imposed on drug users and non-
drug users. As previously mentioned, studies focusing on the effect of conditions on
probation revocation have found that their imposition may lead to frequent violation
of conditions and higher recidivism rates among probationers. More restrictive
conditions may increase the likelihood of failure. This section examines conditions
imposed on drug users and non—drug users. The overall effects of conditions imposed
on drug user and non-drug users on violation and revocation will be discussed in the
next sections.

Table 1.2 presents information on the conditions imposed on the probationers,
subdivided by drug use. The most frequently imposed condition of probation for the
total sample was drug testing (59%), followed by alcohol testing (48%),
vocational/employment requirement (65%), and supervision fee (79%).

In order to assess how many conditions were imposed on all probationers as

well as on drug users compared to non-drug users, number of conditions were

18

grouped into 5 categories, as shown in table 1-2. The data indicated that probationers
could be imposed a total of twenty conditions. Two-thirds of the probationers in the
total sample were ordered to 3 to 6 conditions and 23 percent were ordered to 7 or
more conditions.

Table 1.2 shows that there are differences in conditions imposed between drug
users and non-drug users. The bivariate analysis of the conditions imposed on drug
users and non-drug users indicated that alcohol treatment, drug treatment, drug
testing, alcohol testing, educational requirement, restitution, other treatment,
community service, and numbers of conditions were significantly related to
probationers’ drug abuse history. These relationships were all significant at p<.05
level. The results also indicated that those probationers with a drug abuse history
were more likely to be ordered such conditions as alcohol treatment, drug treatment,
drug testing, alcohol testing, educational requirement, restitution, other treatment, and
community service. Drug users were also ordered more conditions than were non-
drug users.

Table 1-2 shows that 27 percent of drug users got alcohol treatment compared
to 16 percent of non-drug users. The test indicated that half of drug users were
ordered drug treatment while only 13 percent of non-drug users were ordered.
Regarding drug testing, almost 85 percent of probationers with a drug abuse history
were ordered drug tests compared to 40 percent of those probationers with no drug
abuse history. When examining alcohol tests, 62 percent of drug users were ordered
alcohol testing compared to 37 percent of non-drug users. Unlike other variables,
non-drug users were more likely to receive restitution than drug users. Only 31

percent of drug users were ordered restitution compared to 42 percent of those with no

19

Table 1.2 - Conditions Imposed on Drug Users and Non-drug Users

 

Non-drug Users Drug Users Total
% N % N % N
Alcohol Treatment
No 84 736 73 483 79 1219
Yes 16 1 36 27 182 21 318
Drug Treatment
No 88 763 51 339 72 1 102
Yes 1 3 109 49 327 28 436
Mental Health Treatment
No . 86 746 86 572 86 1318
Yes 14 126 14 93 14 219
Drug Testing
No 60 524 16 105 41 629
Yes 40 348 84 562 59 909
Alcohol Testing
No 63 551 38 252 52 803
Yes 37 321 62 414 48 735
Eduwfional Requirement
No 74 642 69 460 72 1 102
Yes 26 229 31 205 28 434
Vocational/Employment
No 33 291 36 241 35 532
Yes 67 581 64 424 65 1 005
Restitution
No 58 506 69 456 63 962
Yes 42 366 31 207 37 573
Electronic monitoring
No 88 766 85 565 87 1331
Yes 12 105 15 101 13 206
Supervision Fee
No 22 191 20 132 21 323
Yes 78 680 80 534 79 1214
Other Treatment
No 84 703 77 488 81 1 191
Yes 16 133 23 145 19 278
Community Service
No 65 564 72 480 68 1044
Yes 35 308 28 185 32 493
Number of Conditions
No Condition 2 17 2 1 3 2 30
1-2 conditions 17 152 7 46 13 198
3-4 conditions 38 335 23 155 32 490
5—6 conditions 26 228 37 248 31 476
7 or more conditions 17 146 32 212 23 358

 

Note: *=p<.0 , “=<.0 , *“=<.001

20

drug abuse history. Drug users were also more likely to be ordered other forms of
treatment than were non-drug users. Twenty three percent of drug users received an
other forms of treatment as a condition of probation as compared to 16 percent of
non-drug users. Non-drug users were more likely to receive community service as a
condition. Thirty five percent of non-drug users were ordered community service
compared to 28 percent of drug users. The total number of conditions imposed on
probationers was also significantly related to drug abuse history. Drug users were
more likely to be ordered more conditions of probation than non-drug users. Only 9
percent of drug users were ordered less than 2 conditions but almost one quarter of
non-drug users were ordered less than 2 conditions of probation. Almost 70 percent
of drug users were ordered more than 5 conditions compared to 43 percent of non-
drug users.

In summary, the results indicate that drug users were more likely to be
imposed alcohol treatment, drug treatment, drug testing, alcohol testing, and other
treatment. Overall, the table also shows that drug users were more likely to be

ordered more conditions of probation.

The Predictors of Conditions Imposed on Probationers

 

After examining the difi‘erence of conditions imposed on between drug users
and non-drug users showing that drug users were more likely to be ordered more
conditions (e. g. alcohol treatment, drug treatment), the following section examines the
predictors of conditions imposed on all probationers using OLS regression models
(Refer to table 1.3). The bivariate models used above do not statistically control for

the effects of multiple variables on conditions, OLS regression models are used to

21

Table 1.3 - Characteristics of Probationers Regressed on the Number of Conditions

 

Predictors

Age

Male

Single

White

Highest Grade Completed
(High School and above)

Drugs Use

Alcohols Use

Employed

Offense Category
Assultive

Drugs
Non-Assaultive
Prior Juvenile Record

Prior Felony

Prior Misdemeanor

Model A

Total Conditions

b s.e

-0.02 0.01 m

-0.07 0.13

0.42 0.11 ***

-0.32 0.10 m

0.76 0.10 m

0.99 0.12 **"’

0.00 0.10
-0.31 0.13 *
0.15 0.12
Reference Category
0.33 0.14
-0.02 0.12

0.38 0.11 m

R Square = 0.18

Model B

Punitive Conditions

b s.e
-0.01 0.00 **
-0.01 0.08
0.03 0.09
0.16 0.07 *
-0.07 0.07 **
0.41 0.07 ***
0.45 0.08 “1'
0.11 0.07
-0.33 0.09 “*
0.00 0.08
Reference Category
0.18 0.09 t
-0.08 0.08
0.23 0.07 t“

R Square = 0.12

ModelC
Treatment Conditions
b s.e
-0.01 0.00 “*
-0.06 0.08
-0.07 0.08
0.26 0.06 m
-0.25 0.06 m
0.35 0.07 *“
0.53 0.07 ***
-0.11 0.06
0.02 0.08
0.17 0.07 *
Reference Category
0.15 0.08
0.06 0.07
0.15 0.07 *

R Square = 0.15

 

Notes: * = p<.05

** = p<.01

m = p<.001

The number of total conditions are twelve. The number of treatment conditions are six which are
drug treatment, alcohol treatment, other treatment ordered, mental health treatment, vocational]

employment requirements, and educational requirements. Also, the number of punitive conditions are
six which are restitution, supervision fees, electronic monitoring, alcohol testing, drug testing, and

community service.

22

examine the effects of probationers’ socio-demographic and criminal history on the
conditions imposed on probationers, controlling for other variables. This allows a
clearer examination of the factors that may contribute to probationers being imposed
more conditions, besides having a drug history. The aim is to see whether the
imposition of more probation conditions is contingent upon drug use itself, or other
characteristics of probationers.

As previously mentioned, probationers could be imposed a total of twelve
conditions. In order to better assess the effects of socio-demographic and criminal
history on the conditions imposed on probationers, the total conditions were also
recoded into two different variables. Six conditions were more related to treatment
conditions and these were added as a single variable. The new variable includes such
conditions as drug treatment, alcohol treatment, other treatment ordered, mental
health treatment, vocational/employment requirements, and educational requirements.
The remaining six conditions were more related to punitive conditions and include
restitution, supervision fees, electronic monitoring, alcohol testing, drug testing, and
community service.

Because there are three dependent variables; which are total conditions,
treatment conditions, and punitive conditions are continuous variables, three OLS
regression models are used to examine the effect of individual characteristics on
conditions. Model A examines the effects of socio-demographic and criminal history
on the total number of conditions. Model B tests the relationship between socio-
demographic and criminal history on the number of punitive conditions. Model C
examines the effects of socio-demographic and criminal history on the number of

treatment conditions.

23

The table1.3 shows that in the three multivariate models, six predictors are
statistically significant. These are age, ethnicity, highest grade completed, drug
abuse, alcohol abuse, and prior a misdemeanor record at the p<.05 level.
Probationers’ age was significant in the all three models at the p<.001 level. The
tendency was for younger probationers to be imposed more conditions, both punitive
and treatment. Ethnicity was significant in the three models as well. Caucasian
probationers were more likely than non-White to get more conditions whether
punitive or treatment conditions. The measure of highest grade completed was also
significant in the three models. Probationers with higher levels of education were
more likely to get fewer conditions than were those with lower levels of education.
Drug abuse and alcohol abuse by probationers were also both significant in three
models at the p<.001 level. Probationers who had drug and alcohol problems were
more likely to get more conditions, both punitive and treatment, than those
probationers with no drug and alcohol problems. Assultive offenses were also
significant predictors in models A and B. Probationers who committed assaultive
offenses were more likely to be imposed fewer punitive and treatment conditions and
fewer punitive conditions. Probationers charged with drug offense were more likely
to be ordered treatment conditions than those charged with non-assaultive offenses.
When examining the effects of probationers’ criminal histories on conditions, prior
juvenile record was a significant predictor. That is, probationers with prior juvenile
records were more likely to receive more punitive conditions than those probationers
with no prior juvenile record. Prior misdemeanor was also significant in all three
models. Probationers who had prior misdemeanor record were more likely receive

both punitive and treatment conditions.

24

The R square of the models indicates that 18 percent of the observed
variability in total conditions can be explained by the independent variables, as well
as 12 percent of the observed variability in punitive conditions and 15 percent of the
observed variability in treatment conditions. Although the variance explained in these
models are fairly small, it is a step toward understanding those factors that influence
judicial considerations in determining what conditions to impose on probationers.

In summary, most of the factors that were identified as significant predictors
of total conditions were also significant predictors of punitive and treatment
conditions. These variables include age, ethnicity, highest grade completed, drug
abuse, alcohol abuse, and prior misdemeanor. The tendency was that probationers
who were older and had lower level of education were more likely to be ordered more
conditions, both punitive and treatment conditions. Probationers who were White,
used drugs and alcohol, and had prior misdemeanor records were more likely to be
ordered more conditions, both punitive and treatment conditions. Also, the results
clearly show that drug users were ordered more punitive conditions as well as
treatment conditions, even when other factors, like prior criminal records and
demographics are controlled.

Based on these findings, the next section examines the overall effect of

probation conditions and other factors on probation violation and revocation.

Factors Influencing Violations

Having discussed the predictors of probation conditions, the following
sections examine the results of OLS regression models used to examine the predictors

of violations for the entire sample of probationers subdivided by drug use. To better

25

understand the predictors of violations for drug users and non-drug users, the
predictors of violations for the entire sample of probationers are examined first and
the next section analyzes the predictors of violations of drug users and non-drug users.
OLS regression techniques are used to examine the relationships because the
dependent variable is the number of violations that is continuous variable. Also, it is
important to mention that all independent variables that were not continuous were
dummy variables with only possible outcomes of 0 and 1.

The table 1.4 shows the predictors of violations of the entire sample using two
OLS regression models. Model A predicts the likelihood of violations for all
probationers. The number of violations was recorded up to eight from the
probationers’ files. The independent variables are socio-demographic and criminal
history of all probationers. Model B also predicts the likelihood of violations for all
probationers. Besides the variables of socio-demographic and criminal history, some
of conditions such as alcohol treatment, drug treatment, drug test, and educational
requirement were included in the independent variables to examine what effects these
variables have on violations, controlling other variables.

In examining the effects of conditions of probation on violations using OLS
regression that were not presented in the study, alcohol treatment, drug treatment,
drug test, and educational requirement were found to be significantly related to
violations of probation conditions. These variables are consistent with what has been
found to be important predictors of violations in prior research. Therefore, these four
conditions are added to model B in order to assess whether these variables are
significantly related to violations, controlling for other variables.

The results show that several variables such as age, ethnicity, highest grade

26

 

 

Table 1.4 - Predictors of Violations Using OLS Regression for the Entire Sample

 

 

Model A Model B
(includes significant conditions)

Predictors b s.e b s.e
Age -0.03 0.01 ** -0.02 0.01 **
Male -0.06 0.16 -0.07 0.16
Single -0.23 0.17 -0.24 0.17
White -0.10 0.14 '** -1.03 0.14 “*
Highest Grade Completed -0.70 0.14 m -0.64 0.13 ***
(High School and above)
Abused Drugs 0.46 0.14 m 0.14 0.15
Abused Alcohols 0.13 0.15 O. 00 0.15
Employed -O.27 0.13 * -O.27 0.13 *
Offense Category

Assultive -O.19 0.17 -0.16 0.17

Drugs -0.09 0.16 -0.24 0.16

Non-Assaultive Reference Category Reference Category
Prior Juvenile Record 0.26 0.18 0.23 0.18
Prior Felony 0.30 0.16 0.27 0.16
Prior Misdemeanor 0.15 0.14 0.06 0.14
Alcohol Treatment as Condition 0.05 0.02 *
Drug Treatment as Condition 0.21 0.16
Drug Testing as Condition 0.75 0.15 m
Educational Requirement -0.01 0.02
as Condition

R Square = .11 R Square = .13

Notes: * = p<.05 ** = p<.01 m = p<.001

27

completed, and employment at the time of offense were significant in the both
models.

Probationers’ age was significant in the two models and younger probationers
were more likely to commit more violations. The measure of ethnicity was significant
in both models. White probationers were more likely to commit fewer violations than
were non-White probationers. Highest grade completed was also significant in both
models. Probationers with high levels of education committed fewer violations than
were those with lower levels of education. Probationers’ employment status at the
time of offense was significant in the both models. Probationers who were employed
were more likely to commit fewer violations than were those probationers who were
not employed at the time of ofl‘ense.

As previously mentioned, model B included four conditions, which are alcohol
treatment, drug treatment, drug testing, and educational requirement. In this model,
alcohol treatment and drug testing were significantly related to violations of probation
conditions, controlling for other variables. Probationers who were ordered to alcohol
treatment and drug testing were more likely to commit violations.

When four conditions are added in model B, the results indicate that all
variables except for drug use, significant in model A were also significantly related to
violations in model B. The results suggest that drug abuse is not necessarily a
significant predictor of violation of probation when number of conditions such as
alcohol treatment and drug testing were combined. It seems that conditions such as
drug testing and alcohol treatment create opportunities for probationers to violate their
probation orders whether they use drug or not.

The R square of model A indicates that 11 percent of the observed variability

28

in violation of probation orders were explained by the independent variables. Also,
the R square of model B indicates that 13 percent of the observed variability in
violation of probation orders were explained by the same independent variables used
in the model A and an additional four variables that were alcohol treatment, drug
treatment, drug test, and educational requirement. The results indicate that even
though 2 percent of observed variability in violation of probation orders is explained
more when four conditions were added to the independent variables, still the majority
percent of variability in violation of probation orders is not explained by the model.
In summary, age, ethnicity, highest grade completed, and employment status
were identified as significant predictors of violation of probation conditions in the
both models. When four conditions are added in model B, it shows that four
significant predictors of violations fiom model A are also significant in the model B
except for drug abuse. It is important to mention that the relationship between drug
use and violation of probation disappeared when several conditions such as alcohol
treatment and drug testing were added. Thus, the results suggest conditions such as
drug testing and alcohol treatment facilitate opportunities for probationers to violate

their probation orders whether they use drug or not.

Factors Influencing Violations For DrugUsers and Non-drug Users

 

Having examined some of the factors that influence violations of probation
conditions for the entire sample of probationers, the following section examines
factors influencing violations of conditions committed by drug users compared to
non-drug users. In previous models, although we know which factors were

significantly related to probation violation for the entire sample of probationers, we

29

do not know which factors are significantly related to violation of probation orders for
drug users or non-drug users. Because one of the goals of this study is to examine the
differences of the predictors of violation between drug users and non-drug users, this
section examines predictors of probation violation committed by drug using
probationers compared to non-drug using probationers.

The table 1.5 shows the predictors of violations for drug users and non-drug
users using two OLS regression models. Model A predicts the likelihood of
violations for drug users and model B predicts the likelihood of violations for non-
drug users on probation. Independent variables in the both of models are socio-
demographic, criminal history variables, and some of conditions imposed on
probationers that are alcohol treatment, drug treatment, drug testing, and educational
requirement.

The table shows that three predictors are statistically significant in both
models. These variables are ethnicity, alcohol treatment, and drug testing. There are
several variables, which are significant in one model but not significant in other
model. The measure of highest grade completed is significant in model A but is not
significant in model B. Alcohol abuse, employment status at the time of offense, and
educational requirement are significant in model B but not significant in model A.

For ethnicity, Whites were less likely to violate the conditions of their
probation compared to non-Whites in the both models, controlling for many other
predictors. The other significant predictors are measures of two probation orders that
are alcohol treatment and drug testing in both models. It shows that probationers who
had drug problem and were ordered alcohol treatment were more likely to incur

violation whereas probationers who had non-drug problem and were ordered alcohol

30

Table 1.5 - Predictors of Violations for Drug Users and Non-drug Users Using OLS Regression

 

 

Model A Model B
Drug Users Non-drug Users

Predictors b s.e b s.e
Age -0.03 0.02 -0.01 0.01
Male -0. 13 0.26 -0.10 0.21
Single -0.37 0.27 -0.12 0.22
White —0.60 0.22 ** -1.26 0.17
Highest Grade Completed -0.91 0.22 *“ -0.26 O. 18
(High School and above)
Abused Alcohols -0.22 0.21 0.79 0.25
Employed -0.11 0.21 -0.36 0.17
Offense Category

Assultive 0.23 0.31 -0.31 0.20

Drugs -O.21 0.22 -0.3 0.26

Non-Assaultive Reference Category Reference Category
Prior Juvenile Record -0.05 0.26 0.44 0.23
Prior Felony 0.29 0.23 0.26 0.22
Prior Misdemeanor -0.04 0.21 0.13 0.18
Alcohol Treatment as Condition 0.06 0.02 " -1 . 17 0.3
Drug Treatment as Condition 0.35 0.21 0.55 0.30
Drug Testing as Condition 0.60 0.29 ‘ 0.68 0.19
Educational Requirement -0.02 0.02 0.61 0.21
As Condition

R Square = .09 R Square = .19

Notes: " = p<.05 ** = p<.01 *** = p<.001

31

*i‘i

m

treatment were less likely to incur violations, controlling other variables. An order of
drug testing predicts the likelihood of violating their conditions of probation whether
probationers are drug users or not. Both drug users and non-drug users ordered drug
testing were more likely to violate the conditions of their probation. The results
suggest that the order itself has an impact on the likelihood of violation of the
conditions, independent of the probationers’ drug abuse problem.

Highest grade completed predicts the likelihood of violations in model A but
not in model B. Among drug users, it shows that the higher the probationer’s
education, the less likely the probationer will commit a violation. Although education
is not significant among non-drug users, it is important to mention that probationers
with high level of education are less likely to incur Violations than are probationers
with lower level of education.

Significant predictors for drug users but not for non-drug users are alcohol
abuse, employment status, and educational requirement. Alcohol abuse among non-
drug users was a significant predictor of violation. Non-drug users with an alcohol
abuse history were more likely to violate the conditions of their probation.
Employment status at the time of offense was a significant predictor of violation
among non-drug users. Non-drug users who were employed at the time of offenses
were less likely to violate their conditions of probation.

Another variable, which was significant in model B but not in model A, is educational
requirement. Probationers given an educational requirement were more likely to
violate the conditions of probation.

The R square of model A indicates that 9 percent of the observed variability in

violation of probation orders were explained by the independent variables (e. g. age,

32

sex, marital status, highest grade completed). Also, the R square of model B indicates
that 19 percent of the observed variability in violation of probation orders were
explained by the same variables used in the model A. These results tell that the
independent variables explained only 9 percent of observed variability in probation
violation of drug users but explained almost 20 percent of observed variability in
probation violation of non-drug users.

In summary, some variables were identified above that were significant
predictors of violations in both models. Other variables were not consistent predictors
across models. Probationers who were White and were not imposed drug testing were
less likely to violate their probation order whether they were drug users or not. Drug
using probationers who have high level of education were less likely to violate their
probation order and probationers with no-drug problem who were employed and were
ordered alcohol treatment were less likely to violate. The results show that alcohol
treatment increased the likelihood of violation for probationers with drug problems
but decreased the likelihood of violation for those without a drug problem. Drug
testing increased the likelihood of violation for drug users as well as non-drug users.
Therefore, the results suggest that these conditions should be ordered selectively to
achieve the expected goals of reducing probation violations and increasing success
rate of probation terms. Otherwise, it is likely that the conditions imposed could

create conditions for probationers to violate.

Factors Influencing Revocations
Having examined some of the factors that predict violations of probation

conditions, this section examines factors that predict revocations using the logistic

33

 

regression technique. The difference between violation and revocation is that
violation is incurred by probationers, however probation officers make the choice to
revoke. The factors that were significant predictors of violation may or may not be
significantly related to revocation. Therefore, this section examines the factors that
predict revocation. Logistic regression is the most reasonable statistical technique
when the dependent variable is a dichotomous measure, which is probationers’
discharge type.

Table 1.6 includes two logistic regression models. Model A predicts the
likelihood of a revocation for probationers who had drug problems and model B
predicts the likelihood of a revocation for probationers who had non-drug problems in
the sample. The independent variables in both models are socio-demographic
variables, criminal history, the number of conditions imposed on probationers, drug
testing, drug treatment, and number of violations.

Model A indicates that Caucasian probationers are more likely to successfirlly
complete their terms while non-White were more likely to fail. The odds ratio in table
1.6 shows that ethnicity has a large effect. Caucasian probationers were almost twice
as likely to be successful, controlling for other variables in the model.

Probationers’ employment status at the time of offense was also statistically
significant. Drug using probationers who were employed at the time of their current
offense were more likely to be successfully discharged. In this case, the odds ratio
showed a large effect where probationers who were employed were more than twice
as likely to be successfully discharged compared to those who were not employed.

Another variable that was also significantly related to discharge type was prior

felony record. Probationers with prior felony convictions were more likely to fail

34

Table 1.6 - Predictors of Successful Discharge Using Logistic Regression

 

 

Model A Model B
Drug Users Non-Drug Users

Predictors b s.e odds ratio b s.e Odds Ratio
Age 0.02 0.19 1.02 0.01 0.01 1.01
Male -0.22 0.32 0.80 -0.41 0.29 0.67
Single -0.38 0.32 0.68 -0.29 0.30 0.75
White 0.56 0.26 1.74 * 0.41 0.24 1.51
Highest Grade Completed 0.17 0.26 1.20 0.25 0.22 1.29
(more than HS, 1)
Abused Alcohol 0.06 0.26 1.06 -0.06 0.29 0.95
Employed 0.87 0.25 2.38 *‘* 0.54 0.21 1.72 *
Offense Category

Assaultive 0.10 0.40 1.11 0.16 0.29 1.17

Drugs 0.39 0.25 1.48 0.52 0.33 1.67

Non-Assaultive Reference Category Reference Category
Prior Juvenile Record 0.02 0.31 1.02 -0.44 0.31 0.64
Prior Felony -0.73 0.28 0.48 ** -0.46 0.28 0.63
Prior Misdeameanor 0.01 0.25 1.01 -0.91 0.25 0.40 ***
Number of Conditions Imposed -0.04 0.08 0.96 -0. 15 0.08 0.86
Drug testing as Condition -0.09 0.38 0.91 0.51 0.31 1.67
Drug Treatment as Condition -0.2 0.28 0.80 0.04 0.36 1.04
Number of Violations -0.50 0.06 0.61 m -0.50 0.06 0.60 ***
- 2Log Likelihood 451.83 547.17
Model Chi-Square 165.15*** 197. 01*“
Notes: * =p<.05 ** = p<.01 *“' =p<.001

35

probation. Also as expected, the number of violations was a significant factor of
predicting revocation. Probationers who commit more violations were more likely to
fail their terms.

Model B indicates that three variables, which are employment status, prior
misdemeanor record, and number of violation, were significant predictors of
revocation. Ethnicity was a significant predictor for drug users but was not
significantly related to discharge type for non-drug users.

Unlike model A where prior felony record was significant, prior misdemeanor
record was significantly related to discharge type in model B. It indicates that
probationers with prior misdemeanor convictions were more likely to fail probation.
Consistent with the model A, number of violations committed by non-drug users was
also significantly related to discharge type. It shows that probationers who commit
more violations were more likely to fail their probation terms, while holding all other
variable. Based on this, it shows that whether probationers had drug problem or not,
probationers who commit more violations would be to fail their terms. Also, it shows
that whether probationers had drug problem or not, number of prior violations
increases the likelihood of a revocation by approximately 60 percent.

In summary, the results indicate that employment status and number of
violations are statistically related to the discharge type in both models, controlling for
other variables. Ethnicity and prior felony are significant for drug users but these
variables are no longer significant for non-drug users. Comparing the predictors of
discharge type to the predictors of violations, some differences are appeared. For
drug users, highest grade completed, alcohol treatment, and drug testing that were

significantly related to violations were no longer significantly related to the type of

36

discharge. Employment status and prior felony records that were not significantly
related to violations were significantly related to the type of discharge for drug users.
Interestingly, ethnicity was significantly related to the type of discharge as well as
violations for drug users. The results show that White were less likely to incur
violations and more likely to be successfully discharged. For non-drug users, the
variables of ethnicity, alcohol abuse, alcohol treatment, drug testing, and educational
requirement that were significantly related to violations were no longer significantly
related to the type of discharge. Prior misdemeanor record that was not significantly
related to violations was significantly related to the discharge type. Employment
status was significantly related to both the discharge type and violations. The results
indicate that probationers with higher level of education were more likely to succeed
their probation and less likely to violate the conditions of their probation.

It is also important to mention that the number of violations was a strong
predictor of a revocation in both models, controlling for other variables. It shows that
probationers who commit more violations were more likely to fail their probation
terms whether they were drug users or non-drug users. Therefore, this finding
indicates that since number of violations is strong predictor of revocation, it is
important to understand which factors are significantly related to violations. The
previous section found that conditions such as drug testing and educational

requirement were significantly related to violation of conditions.

37

Chapter VI

DISCUSSION

The criminal justice system has been confronted with the widespread use of
illegal drugs and drug-related crimes. The increasing number of drug-involved
offenders has presented huge problems like drug-related crimes and prison
overcrowding. As a result, the criminal justice system has struggled to search for
effective ways to deal with drug-related crimes and overcrowding in prisons and jails.
As a way to alleviate overcrowding, probation has been used as the most common
form of community supervision in the criminal justice system. Although there have
been numerous studies about probation, few have specifically examined the
mechanism by which drug users violate and recidivate when compared to non-drug
users on probation.

Therefore, the focus of this study was to compare drug users to non-drug users
on probation in relationship to; a) the individual traits; b) the probation conditions
imposed; c) the predictors of probation violations; and d) the predictors of revocation.

Significant differences were found between drug users and non-drug users.
Specifically, the study found that some characteristics of drug users such as marital
status, education, employment status, alcohol abuse, offense category, criminal
records were significantly different from those of non-drug users. Drug users were
more likely to be single, to have lower level of education, to be unemployed at the
time of offense, to have alcohol abuse history, and to be charged with non-assaultive
crimes. Drug users were also more likely to have previous juvenile, misdemeanor,

and felony records.

38

With regards the differences of probation conditions imposed, the study found
that drug users were more likely to be imposed alcohol treatment, drug treatment,
drug testing, alcohol testing, and other treatment. Regarding the total number of
conditions imposed, the results show that drug users were more likely to be ordered
more conditions.

In the predictors of probation violations, the study shows that some factors
such as ethnicity, alcohol treatment, drug testing were significantly related to
violations for both drug users and non-drug users. Probationers who were non-White
and were ordered drug testing were more likely to violate the conditions of their
probation. Interestingly, the alcohol treatment condition is a positive predictor of
violations for non-drug users but a negative predictor for drug users. Drug users who
were ordered alcohol treatment were more likely to violate their conditions of
probation while non-drug users who were ordered alcohol treatment were less likely
to violate the conditions of their probation. Also, it should be mentioned that drug
testing is independently related to probation violation, regardless of probationers’
drug abuse history. Because drug testing is frequently ordered, it may be useful to
reexamine its effectiveness to deal with drug use by probationers.

For predictors of revocation, this study found that ethnicity, employment
status, and prior felony record were significantly related to discharge type in drug
users. These findings are consistent with prior researches. Whites were more likely
to success their probation than were non-White and drug users who had prior felony
record were more likely to fail their probation. Even though ethnicity was not
significant in non-drug users, the test also shows that Whites were more likely to

successfirlly complete their probation than were non-White. But it should be

39

mentioned that this finding does not mean that being non-White in itself is a problem
because this study did not control for other factors that are likely associated with
ethnicity. Non-drug users who had prior misdemeanor record were more likely to fail
their probation. Employment status was significantly related to revocation in both
drug users and non-drug users. Probationers who were employed at the time of
offense were more likely to be successfully discharged. Also, the number of
violations was significantly related to revocation in both models. Probationers who
committed more violations were more likely to fail their probation. Therefore, this
finding tells that since violations is strong predictor of discharge type, it is important
to know which factors are significantly related to violations. This study found that
conditions such as drug testing and educational requirement and ethnicity were
significantly related to violation of conditions.

Although a few factors considered in revocations were congruent with those
that influenced violations, some were not. For example, in the case of drug users,
highest grade completed and drug test were significant predictors of violations but not
of a revocation. Employment status and prior felony were significantly related to a
revocation but not to violations. For non-drug users, ethnicity, alcohol abuse, and
drug test were significant predictors of violations but not of a revocation. Prior
misdemeanor was predictor of a revocation but not of violations.

The limitation of this study is that because this study examines the factors of
probation violation and revocations of drug users and non-drug users, the study did
not intend to examine the type of violations nor did it look at probation agents’
responses to violations. Because this study found that number of violations are strong

predictors of a revocation, firrther research should consider examining probation

4o

agents’ responses to violations and their effects on discharge types. Other limitations
of this study is that the data used were originally collected for administrative rather
than research purpose and are subject to the short-comings of such information.
Although there are limitations in the study, the results of this study provide
important directions for firrther research, particularly in examining the factors

influencing probation violations and responses of probation officers to violations.

41

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