1 PERSONALITY AND CRIMINAL BEHAVIOR: RECONSIDERING THE INDIVIDUAL 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 2001 by Shelley Johnson Listwan B.S., Wright State University, 1995 M.S., University of Cincinnati, 1996 Committee Chair: Patricia Van Voorhis
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Personality and Criminal Behavior: Reconsidering the Individual
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PERSONALITY AND CRIMINAL BEHAVIOR:
RECONSIDERING THE INDIVIDUAL
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
2001
by
Shelley Johnson Listwan
B.S., Wright State University, 1995 M.S., University of Cincinnati, 1996
FINDING VALUE IN INDIVIDUAL THEORIES ................................................................... .11 Definition of an Elusive Concept ............................................................................. .11 Psychological Foundations of Personality .............................................................. .12 Personality Trait Measurement ................................................................................ 13 A Paradigm Crisis..................................................................................................... 15 State versus Trait ...................................................................................................... 17 Summary ................................................................................................................... 18
THEORETICAL AND EMPIRICAL CONTEXT ...................................................................... 19 OF THE CURRENT STUDY................................................................................................ 19
Grounds for Excluding the Individual ...................................................................... 20 PERSONALITY AND CRIME ............................................................................................. 22
The EPQ and Crime.................................................................................................. 22 The MPQ and Crime................................................................................................. 27 Summary ................................................................................................................... 31
DEVELOPMENTAL THEORY AND PERSONALITY.............................................................. 33 Self Control Theory................................................................................................... 34 Lifecourse Theory ..................................................................................................... 36 Developmental Theory .............................................................................................. 38 Summary ................................................................................................................... 42
PERSONALITY AND CORRECTIONS ................................................................................. 42 Effective Classification ............................................................................................. 43 Psychological Classification Systems ....................................................................... 46 Summary ................................................................................................................... 55
AN INTEGRATION OF PERSONALITY TYPES .................................................................... 55 Relationship Between Types ..................................................................................... 56
MEASUREMENT OF RESEARCH VARIABLES.................................................................... 69 Independent Variables .............................................................................................. 69 Dependent Variables................................................................................................. 74
DATA ANALYSIS ............................................................................................................ 76 Model #1: Personality and Any New Arrest ............................................................ 76 Model #2: Personality and Frequency of Arrest ..................................................... 82 Model #3: Personality and the Seriousness of the Offense....................................... 84
CHAPTER 4: RESULTS…...……………..…………………………………………….89
RESULTS. ....................................................................................................................... 89 Multivariate Analysis of Arrest: Model 1 ................................................................ 89 Multivariate Analysis of Multiple Arrests: Model 2 ................................................ 95 Multivariate Analysis of Offense Type: Model 3 ..................................................... 97
CHAPTER 5: CONCLUSIONS…………………………………………………….…109
DISCUSSION AND CONCLUSIONS. ................................................................................. 109 Limitations .............................................................................................................. 109
SUMMARY OF FINDINGS............................................................................................... 111 BROADER IMPLICATIONS OF THE FINDINGS.................................................................. 117 FUTURE RESEARCH DIRECTIONS.................................................................................. 126
REFERENCES………………………………………………………………………….128
APPENDIX A…………………………………………………………………………..140
APPENDIX B……………………………………………………………………………146
APPENDIX C…………………………………………………………………………..155
APPENDIX D……………………………………………………………………………162
APPENDIX E……………………………………………………………………………168
APPENDIX F……………………………………………………………………………171
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TABLE OF TABLES
Table 1. Summary of key personality constructs for the Eysenck Personality Questionnaire ………………….………………………………………………………………………26
Table 2. Summary of key personality constructs for the Multidimensional Personality
Questionnaire……………………………………………………………..………….32 Table 3. Summary of key predictors of criminal behavior…..………………..………….45 Table 4. Summary of Interpersonal maturity levels ……………………………..………..51 Table 5. Summary of key personality constructs for the Jesness Inventory……..………52 Table 6. Summary of key personality constructs for the collapsed Jesness Inventory
types…………………………………………………………………………..……...54 Table 7. Relationship between EPQ, MPQ and Jesness I-level, personality, and collapsed
types…………………………………………………………………………..……...57 Table 8. Percentage and frequency distribution of participants’ social demographic
characteristics………………………………………………………………….…….64 Table 9. Percentage and frequency distribution of participants’ prior and current offense
record……….……...…………………………………………………………..…….66 Table 10. Percentage and frequency distribution of participants’ I-level and personality
types…………………………………………………………………………..……...72 Table 11. Percentage and frequency distribution of participants’ Salient Factor Scores.…74 Table 12. Percentage and frequency distribution of participants’ recidivism rates..….….77 Table 13. Distribution by Year of Parolees at Risk (Model 1)…………………..….……80 Table 14. Distribution by Year of Parolees at Risk (Model 3: Drug Offenses)…………..86 Table 15. Distribution by Year of Parolees at Risk (Model 3: Property Offenses)………87 Table 16. Distribution by Year of Parolees at Risk (Model 3: Violent Offenses)…….…..88 Table 17. Model 1 Logistic Regression: Probability of Rearrest by Personality & Control
Variables..………………………………………………………………………….…..92
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Table 18. Model 2 Logistic Regression: Probability of Multiple Rearrest by Personality & Control Variables…………….………………………………………………….……..98
Table 19. Model 3 Logistic Regression: Probability of Rearrest for a Drug Offense by
Personality & Control Variables………………………………………………..……100 Table 20. Model 3 Logistic Regression: Probability of Rearrest for a Drug Offense by
Personality & Control Variables……………………………………………………..103 Table 21. Model 3 Logistic Regression: Probability of Rearrest by Personality & Control
Variables predicting Property Offenses…………..………………………………..105 Table 22. Model 3 Logistic Regression: Probability of Rearrest by Personality & Control
The traits comprising the type extraversion include: sociable, lively, active, assertive,
sensation seeking, carefree, dominant, surgent, and venturesome. Finally, the traits
included in the type psychoticism consist of: aggressive, cold, egocentric, impersonal,
impulsive, antisocial, unempathetic, creative, and tough-minded. The author notes,
however, that the majority of individuals are characterized by a balance between the
types (Eysenck & Eysenck, 1985).
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Later in his career, Eysenck extended his work to crime causation studies.
Eysenck kept his original typology consisting of neuroticism, extraversion, and
psychoticism to explain crime. He theorized that psychoticism was always related to
crime, extroversion was related in younger samples, and neuroticism in older samples.
Eysenck postulated that neuroticism becomes more important in older samples and
contributes to stronger antisocial habits in adults (Eysenck, 1983). Eysenck viewed the
study of personality as having two interlocking aspects: the descriptive or taxonomic
aspect and the causal aspect including genetics and environment. These two aspects,
together, describe the dynamics of why personality exists (Eysenck & Eysenck, 1985).
Eysenck’s theory includes a chain of factors that begins with genetics and ends
with criminal behavior. He theorized that an individual’s DNA, the genetic structure
underlying individual differences, was the first link in the causal chain. But DNA is not
responsible for behavior, rather an individual’s DNA impacts what Eysenck terms
biological intermediaries such as cortical arousal and as a result conditionability and
conscience (Eysenck, 1996). Cortical arousal is a state in the brain that is marked by
being alert and attentive. Low cortical arousal is related to both extraversion and
psychoticism in that both are marked by poor arousal and therefore cause individuals to
act out in an effort to attain greater arousal. Specifically, he theorizes that individuals
with low cortical arousal seek out arousing and often risky activities that may include
criminal acts.
Eysenck sought an explanation not of antisocial behavior, but rather why people
behave in socially desirable ways. This is where the causal chain continues. Eysenck
states that criminals know right from wrong, but prefer the wrong to the right. He then
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explains the reason some commit crimes and others do not is a matter of conscience
(Eysenck, 1996). Specifically, he theorized that individuals learn behaviors through
operant and classical conditioning. That is, the child who is punished repeatedly for an
antisocial act and does not develop the appropriate response (i.e., fear of repeated
punishment) will not learn from this punishment and correct behavior in the future by
developing a moral conscience (Eysenck, 1983). The likelihood of developing a moral
conscience is dependent on a number of factors, including whether conditioning
experiences are missing, whether the wrong experiences are reinforced, and whether the
person has low cortical arousal (Eysenck, 1996). He conceded that the learning process,
or the breakdown of this process, contributes to the likelihood of criminal behavior
(Eysenck, 1983). The causal chain ends here with the likelihood of criminal behavior
being predicted from the genetic make-up of the individual.
The personality tie becomes clearer as he explains that extroversion and
psychoticism are linked to low cortical arousal that influences conditionability,
conscience, and ultimately behavior. And with regard to neurotics, anxiety may act as
the drive or motivation for criminal behavior. In this circumstance, emotions may over-
ride reason leading to aggressive and impulsive behavior. In sum, according to Eysenck
(1983) “all three are involved in antisocial conduct, so that typically the person indulging
in such conduct would be extraverted rather than introverted, emotionally unstable rather
than stable, and high on psychoticism rather than on superego functioning” (p. 64). Table
1 lists the key personality constructs for Eysenck theory of personality.
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Table 1. Summary of key personality constructs for the Eysenck Personality Questionnaire (Eysenck, 1952).
______________________________________________________________________________________________________ Construct Definition Relationship to Criminal Behavior Extroversion Tend to be outgoing, talkative Eysenck found extroverts tend to have low and friendly. But also tend to cortical arousal which influences conditionability be assertive, sensation seeking, Extroversion more likely in younger samples. and dominate in social situations Neuroticism Tend to be anxious in social Eysenck found neuroticism to be positively situations and often experience associated with criminal behavior in older feelings of low self esteem and samples. guilt. Also tend to be irrational tense, and emotional. Emotions may override reason. Can also exhibit aggressive and impulsive behavior. Psychoticism Tend to be very egocentric and are Eysenck found psychotics tend to have low cortical Unempathetic to others needs. Also tend arousal which influences conditionability. Eysenck To be impulsive, cold, and impersonal suggests psychoticism is positively associated with criminal behavior among all individuals exhibiting
the trait.
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The MPQ and Crime
Types similar to those defined by Eysenck emerged from the Multidimensional
Personality Questionnaire (MPQ). The MPQ is a self-report personality instrument
designed to assess a broad range of individual differences in behavioral style (Silva,
1990). The ten scales produced by the MPQ can be combined to produce three types or
superfactors: constraint, negative emotionality, and positive emotionality (Silva, 1990).
As described by Caspi & Silva (1995) “constraint is the combination of the
traditionalism, harm avoidance, and control scales. Individuals high on this factor tend to
endorse social norms, act in a cautious and restrained manner, and avoid thrills. Negative
emotionality is a combination of the aggression, alienation, and stress reaction scales.
Individuals high on this dimension have a low general threshold for the experience of
negative emotions such as fear, anxiety, and anger and tend to be involved in antagonistic
relationships. Positive emotionality is a combination of the achievement, social potency,
well-being, and social closeness scales. Individuals high on positive emotionality have a
lower threshold for the experience of positive emotions and tend to view life as being
essentially a pleasurable experience” (p. 492). Researchers found that positive
emotionality, negative emotionality, and constraint emerged as three major dimensions
related to a variety of behavioral measures (Tellegen, 1985).
Utilizing a modified version of the Multidimensional Personality Questionnaire
(MPQ), Caspi et al. (1994) studied the relationship between personality and crime among
adolescents involved in both the Dunedin Multidisciplinary Health and Development
(DMHD) study and the Pittsburgh Youth Study. The DMHD study is a longitudinal
examination of the health, development, and behavior of 862 adolescent subjects. The
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complete cohort of consecutive births between April 1, 1972 and March 31, 1973 in
Dunedin, New Zealand were traced at age 3, and every two years thereafter until the age
of 25 (Silva, 1990). Four measures of delinquency were examined: self-report, informant,
police contact, and court convictions. The study concluded that among both males and
females, constraint and negative emotionality emerged as predictors of delinquent
behavior across the self-report, informant, and official record measures (Caspi et al,
1994). In a second analysis of the data, the authors found that persons who engaged in
serious delinquent behavior score significantly lower on the superfactor constraint and
significantly higher on the superfactor negative emotionality (Caspi et al., 1994).
The DMHD study included only Caucasian adolescents living in a mid-sized city.
As a result of this sample limitation, a second study was conducted using data from the
Pittsburgh Youth Study, a longitudinal study examining the causes and correlates of
delinquency that included a more heterogeneous sample (Caspi et al., 1994). The sample
consisted of 508 10 year old boys, of which 54 percent were African-American, and 44
percent lived in a house where the main caregiver has been separated, divorced,
widowed, or never married (Caspi et al., 1994). The MPQ was not appropriate with this
age group, hence, the Child Q-sort (Block, 1961) was used as a replacement. The Child
Q-sort scales were used to create the three superfactors: constraint, negative emotionality,
and positive emotionality (Caspi et al., 1994). The delinquency measures included the
child’s self report, teacher’s self report, and parent’s self report. Among both Caucasians
and African American samples, low constraint and the presence of negative emotionality
emerged as correlates of delinquency.
Taken as a whole, the study revealed that individual differences in personality can
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predict delinquency across age groups, geographic location, race groups, and gender.
The authors suggest that the importance of negative emotionality and constraint may
work in tandem. Specifically, negative emotionality refers to a tendency to construe life
in a very negative and anxious way and often may perceive benign situations as
threatening. Individuals with high trait anxiety, often called negative affectivity, tend to
be distressed and have a very negative view of themselves, often worry, and tend to dwell
on frustrations and disappointments (Watson & Clark, 1984). In addition, those
individuals who also score low on constraint tend to take risks and approach situations
with fewer worries of the consequences (Caspi et al., 1994). As a result, individuals with
a personality marked by negative emotionality paired with low constraint have a higher
likelihood of engaging in criminal acts (Caspi et al., 1994).
Given those scoring high on negative emotionality have a negative view of
themselves and perceive the world as hostile and threatening, it is also likely that they
will not undertake challenging situations. In fact, one study found that Italian children
who had high efficacy with regard to academic achievement performed better in school
and were able to resist negative peer pressure (Bandura, Barbaranelli, Vittorio Caprara, &
Pastorelli, 1996). In contrast, those who believe they cannot handle threatening or
challenging experiences are more likely to experience depression, anxiety, and stress
(Bandura, 1989).
A subsequent study explored whether personality could predict abstention from
delinquency (Kreuger, Schmutte, Caspi, Moffitt, Campbell, & Silva, 1994). Using the
same data as the previous study, the authors constructed a measure of delinquency that
consisted of three categories: those who reported they had never engaged in delinquency,
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those who engaged in a wide variety of acts, and those who engaged in normative4
delinquency (Kreuger et al., 1994). The study revealed very different personality profiles
between those who engaged in delinquency on a persistent basis in comparison to those
who abstained from delinquency. Specifically, constraint and negative emotionality
emerged as combined correlates of delinquency among the persistent group. Just as
Moffitt (1993) suggests with her life course persisters typology, those high in negative
emotionality are likely to approach relationships with adversarial attitudes and have
relationships that are short lived. In contrast, the males and females who abstained from
delinquency were characterized as conventional, planful, non-aggressive, and non-
assertive (Kreuger et al., 1994).
Subsequent research has also explored the relationship between personality
characteristics, a child’s ability to delay gratification, and his or her behavior at home or
school. With a sample from the Pittsburgh Youth Study, 508 fourth grade boys were
asked to complete the California Q-sort (Kreuger, Caspi, Moffitt, White, and Stouthamer-
Loeber, 1996). Teachers and mothers were asked to complete the Child Behavior
Checklist (Achenbach and Edelbrock, 1983). The delay of gratification task had
participants choose between an immediately available 40 percent chance to win a nickel
or a delayed 80 percent chance to win a nickel. The authors found that youth who
persistently sought immediate gratification in the lab (e.g., those exhibiting poor impulse
control) were more likely to exhibit problem behaviors in school and at home (Krueger et
al., 1996). In contrast, those children who were able to delay gratification by diverting
attention away from the task at hand, experienced less frustration. The authors concluded
4 Normative delinquency refers to behavior such as truancy and curfew violations.
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that an antisocial tendency, operationalized as low self-control, influenced the child’s
likelihood of seeking immediate gratification (Kreuger et al., 1996).
Finally, personality researchers have also attempted to document the relationship
between personality and violence. It is rare to find individuals who specialize in criminal
violence, although it is also rare to find complete generality (Farrington, 1982). In
addition, the reason why individuals are violent is the subject of much disagreement. A
study by Caspi, Harrington, Moffitt, Begg, Dickson, Langley, and Silva (1997)
investigated the relationship between personality and a variety of what they called health
risk behaviors that included violent crime. The study used the DMHD data at age 21
from 961 individuals. Personality was assessed at age 18 with the MPQ. The study not
only included violent crime but also alcohol dependence, sexual behavior, and driving
habits. The results concluded that all categories of health risk behavior were correlated
with negative emotionality and low constraint. With regard to violence, individuals with
at least one prior violent conviction were exhibiting personality types marked by a very
negative and hostile view of the world paired with an impulsive nature.
Summary
The purpose of this section is to review the literature on crime and personality.
The literature base is limited, however, it should be noted that each sample represents a
diverse group of individuals over a period of time. The results indicate that personality is
related to various types of criminal behavior with a variety of measures, in a variety of
situations, with a variety of individuals. Table 2 lists the key personality factors formed
by the MPQ and crime research. Given the research finding from the above-mentioned
studies, the current study will attempt to mirror those types and examine their relationship
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Table 2. Summary of key personality constructs for the Multidimensional Personality Questionnaire (Caspi & Silva, 1990).
______________________________________________________________________________________________________ Construct Definition Relationship to Criminal Behavior Negative Emotionality Tend to experience adverse By itself may not be related to crime.
states such as anger, anxiety, and However, high negative emotionality related irritability. They perceive life to crime in tandem with low constraint. events and acts of others in very hostile and negative ways
Positive Emotionality Tend to view life in a very Not related; may act as insulator pleasurable way. They tend to engage in positive social and working interactions with others Constraint Tend to endorse convention and By itself may not be related to crime. normative behavior. Avoid risky Lack of constraint paired with negative situations and act cautious. emotionality more likely in criminal population.
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to recidivism. The following section examines how personality fits into the
developmental theories of crime.
Developmental Theory and Personality
An emerging area of research in criminology is now focusing on the unique
circumstances of the individual in an effort to explain criminal behavior over time.
Lifecourse is not personality-crime research per se, but some aspects of the paradigm are
relevant. Lifecourse or developmental theorists explore how an individual develops over
time, the circumstances with which he or she is exposed, and his or her individual
position on the developmental continuum (Loeber & LeBlanc, 1990). It is commonly
thought that individuals differ with regard to their propensity to commit crime. Certain
individuals may commit one crime during their lifetime, whereas others commit multiple
crimes of varying severity throughout. Moreover, research indicates that most offenders’
start at the same level of seriousness, however, only certain individuals persist (Loeber &
LeBlanc, 1990). In an effort to explain these differences, lifecourse theorists rely on
longitudinal data. These studies allow the researcher to uncover how the patterns of
offending emerge over time and what factors may have an impact on behavior.
Within the discipline there is a debate between general theorists who assert a
predisposition established early in life explains the onset and persistence of criminal
behavior and lifecourse theorists who claim that age-related factors across the lifespan
can influence behavior. Both factions in this debate see personality as at least tangentially
important in the explanation of behavior. General theorists, although not directly viewing
their theory as involving personality, theorize that enduring characteristics are
responsible for behavior. In contrast, lifecourse researchers view behavior as influenced
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by different pathways and turning points throughout the lifespan. A third group, the
developmental theorists, study the development of individual and structural factors and
their impact on behavior at different points in time. All three views will be discussed in
detail.
Self Control Theory
General theorists, Gottfredson and Hirschi (1990) view crime as a hedonistic
event. They assert that everyone has the same motivation to commit crime regardless of
their social situation or the existence of laws and sanctions designed to govern the
acceptability of behavior. Gottfredson and Hirschi developed a theory of crime that
focuses on a single determinant or cause. The determinant or cause of behavior,
according to Gottfredson and Hirschi (1990) is the existence of low self control. Low
self control is an enduring characteristic developed early in life that determines behavior.
Individuals with low self control tend to need immediate gratification, seek out exciting
or thrilling situations, and have very little control over their own behavior. Criminal acts
provide the excitement and gratification to satisfy their needs.
Gottfredson and Hirschi (1990) delineate a number of propositions to explain how
self control can explain criminal behavior over time. First, low self control is established
very early in life and cannot be altered. The causes of low self control, however, do not
fall on the individual, rather, from the absence of nurturance and discipline from families
(Gottfredson & Hirschi, 1990). They place the blame for this development on the
parents’ inability to socialize the child correctly. The authors mention a number of
factors that may inhibit the child’s socialization, including: inadequate punishments,
parental criminality, family size, and mothers who work outside of the home.
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Second, the theory includes six components to explain behavior. Low self control
involves a “here and now” orientation in that the individual does not consider future
consequences. Individuals with low self control seek simple and gratifying tasks. The
individuals are adventuresome and enjoy thrilling situations. These individuals are more
likely to embrace physical rather than mental tasks. Individuals low in self control tend
to be selfish and self centered in their needs and desires. And finally, these individuals
appear to have a very low frustration tolerance and are more likely to become angered by
these events. Gottfredson and Hirschi (1990) “clearly assert that these six traits we have
sketched are not alternative ways of having low self control; rather, they form a single
one-dimensional latent trait” (Grasmick, Tittle, Bursik & Arkenklev, 1993, p. 9). This
trait then is able to predict involvement in crime as well as success in conventional
occupations and relationships.
Third, individuals with low self control are likely to commit crime or analogous
behaviors throughout their lifespan. The authors assert “these differences remain
reasonably stable with change in the social location of individuals and change in their
knowledge of the operation of the sanction system” (Gottfredson and Hirschi, 1990). In
other words, low self control remains stable over time regardless of environmental
changes or changes in the laws or punishments.
And finally, Gottfredson and Hirschi (1990) argue that age is not important in the
explanation of crime, but that the fluctuations in opportunity explain increases and
decreases in criminal behavior. Interestingly, the authors do not claim that low self
control is by itself the primary reason for crime. Rather, individuals with low self control
are unable to resist the opportunity to commit crimes. Hence, the condition (i.e. low self
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control) interacts with the opportunity to increase the likelihood of the criminal act
(Grasmick, et al., 1993). More important, however, is the assertion that when
opportunity for crime decreases, as the offender ages, an individual with low self control
is likely to engage in analogous behaviors that are problematic but not necessarily
criminal. Analogous behaviors may include, smoking, gambling, drinking and driving,
and/or sexual promiscuity (Gottfredson and Hirschi, 1990).
As mentioned, Gottfredson and Hirschi (1990) assert that their theory is not a
theory of personality. However, the construct of low self-control is very similar to the
personality dimensions of impulsivity, and on some levels extraversion discussed by
Eysenck. In addition, low self control is directly related to a lack of constraint as
measured by the MPQ. This theory departs from traditional sociological explanations of
crime and for that should be applauded. However, Gottfredson and Hirschi (1990) depart
from many psychologists in their assertion that self-control does not change over time as
individuals react with the environment.
Lifecourse Theory
Sampson and Laub’s (1993) lifecourse perspective recognizes that factors in the
environment may influence an individual’s criminal career patterns. Although
personality is only tangentially discussed in Sampson and Laub’s lifecourse theory, its
relevance to the proposed study is two fold. First, the real value to the discipline is the
introduction of an individual-centered explanation. That is, they recognize that behavior
can change and that individual decision making influences behavior. Second, in contrast
to Gottfredson and Hirschi (1990), Sampson and Laub discuss how other established risk
factors such as employment, marriage, and changes in lifestyle could cause desistence
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from crime. Although the authors fail to explore the relationship between personality and
criminal acts, the discussion of protective factors is relevant. This proposition will
become increasingly clear in preceding sections devoted to classification.
The authors reanalyzed the data originally utilized in the study by Sheldon and
Eleanor Glueck (1959), titled Unraveling Juvenile Delinquency. They examined why
some people commit crime throughout their lifespan and others desist or stop committing
crimes once they reached adulthood. They viewed individuals as active recipients to their
environment and concluded that the external influences could shape how individuals
make decisions. Each individual has experiences that may lead his or her life in different
directions or pathways (Sampson and Laub, 1993). Hence, the authors argued that
criminal behavior could change as pathways and turning points through life emerge.
Sampson and Laub (1993) address three main themes in their book Crime in the
Making. First, the authors contend that informal controls such as those exerted by
families, schools, and/or peers can mediate the structural contexts that may lead to
delinquency. Second, the authors recognize the existence of stability for some offending
careers. They maintain that early onset of delinquency is one of the best predictors of
future offending. Finally, they recognize that fluctuations in criminal behavior can also
result from what they term social capital. For example, job stability in a quality
occupation and the involvement in quality relationships or marriages can impact the
individual’s likelihood of remaining in crime. The authors, however, fail to include
personality as a factor that influences an individual’s environment. Specifically, McCrae
and Costa (1999) argue that an individual’s personality can influence their preference for
friends, situations, their ideologies about how the world works, and their own social
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roles.
In sum, lifecourse perspective and subsequent debate is concerned with the
development and stability of criminal behavior. Any reference to personality is made
only as a recognition that individual decision making is influenced by many factors, one
of which may be personality. Although the studies provide valuable insight and much
needed support from individual theories of crime, lifecourse research in criminology has
failed to include personality as a causal or predictive variable. However, research
conducted by developmental theorists is able to contribute to this stability debate.
Developmental Theory
Developmental theories have been popular in psychology for many years.
However, the research on the developmental precursors to crime or deviance is more
limited. A review of the literature appearing in both psychological and criminological
journals will be discussed.
The full debate on the consistency of personality over time will not be addressed
here, however, the findings of a few studies will be highlighted. For example, there is a
growing consensus among psychologists that personality is very stable in adulthood
(McGue, Bacon, Lykken, 1993). However, others argue that personality can change
drastically during the transition from adolescence to adulthood. Researchers have found
evidence of both stability and change during this transition. For example, one study found
that subjects exhibited a decrease in negative emotionality, increase in constraint, but no
change in positive emotionality as measured by the MPQ (McGue, et al., 1993). Eysenck
(1996) also found that extroversion was more likely to decrease with age as did
Newcomb and McGee (1985) with regard to sensation-seeking behaviors. It may be that
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socializing experiences influence risk taking behaviors in some individuals.
A number of studies have documented the relationship between early aggressive
or antisocial behavior and later delinquency. First, Statin and Magnusson (1989)
explored the relationship between early aggressive behavior and the frequency,
seriousness, and crime patterns occurring later in life. They found a clear and positive
relationship between early aggressive behavior and later crime among male subjects. The
authors also concluded that aggressive boys exhibited restless behavior, poor
concentration, and had poor peer relations as children (Stattin & Magnusson, 1989).
Second, research by Moffitt (1990) explored the relationship between juvenile
delinquency and attention deficit disorder of boys from three to fifteen years of age. The
study found that delinquent boys with attention deficit disorder were most likely to
engage in criminal activity, and exhibited the greatest family adversity, the worst
neuropsychological disorders, and were rated as most aggressive by parents and teachers
at age 13 (Moffitt, 1990). The overall findings indicated, however, that this group
exhibited significantly more antisocial behavior and non-disordered controls at every age.
Third, a study by Patterson, Crosby, and Vuchinich (1992) also found age of onset as a
significant predictor of childhood aggression. Finally, Farrington & West (1990) found
that future juvenile delinquents were more likely to have been rated as troublesome by
their teachers, tended to be hyperactive and lack concentration, and tended to be rated as
impulsive. Moreover, at age 18 they tended to engage in violence while drinking, tended
to be heavy gamblers, held low status jobs or were unemployed with erratic work
histories. All of these studies illustrate that the majority of eventual chronic offenders
were not only displaying antisocial and delinquent behaviors during childhood but
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frequently exhibiting certain personality characteristics known to be related to criminal
The research on the stability of behavior over time directly relates to the work of
Gottfredson and Hirschi. The above evidence illustrates that personality types remain
stable over time and can impact later behavior. However, are those with certain
personality characteristics destined to become future criminals? Terrie Moffitt claims
that the consistency of behavior exists only among certain individuals. Moffitt (1993)
proposes a dual taxonomy that contains two qualitatively different groups of offenders,
those who will persist in their behavior over the lifecourse and those who will limit their
criminal behavior to adolescence. The adolescent limited offender commits crime as a
rebellious act resulting from the maturing gap presented by today’s society. That is, youth
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are physically and mentally capable of achieving adult status, however, are frustrated by
the fact that they are not given the opportunities until later in life. Their criminal behavior
is the result of this frustration. Adolescent limited offenders will stop committing crimes
once they have been given the adult role and have jobs and relationships. In contrast, the
lifecourse persisters’ antisocial behavior is stable over time, they tend to commit crimes
in a variety of situations, they tend to be aggressive, and their crimes tend to escalate over
time.
The relevance of this theory to personality rests with the antisocial persister. As
children they tend to be more impulsive and undercontrolled which may influence how
they conform to school and prosocial peers (Caspi & Silva, 1995). They tend to have
personality traits that have a negative orientation and have a difficult time controlling
their own behavior. The persisters, then, display the same personality constellation
discussed by Caspi and associates (1994). Personality could impact what Moffitt’s calls
cumulative continuity where the individual may have neurological deficits that influence
personality and their perceptions. These in turn influence cognitive ability that in turn
influences behavioral development (Moffitt, 1993). These problems interact to produce
an antisocial personality marked by impulsiveness, worry, and negativity. Or as
theorized by Eysenck (1996), the individual is less conditionable due to low cortical
arousal that interacts with personality to produce criminal behavior. Both Gottfredson and
Hirschi and Sampson and Laub recognize a group of persistent criminals exist, but they
both overlook this literature in their analysis.
Although personality may seem to “fit” with Gottfredson and Hirschi’s self
control theory, most tests of personality support Moffitt’s developmental theory. For
42
example, a study by Bartusch, Lynam, Moffitt, and Silva (1997) found that the
developmental model was supported as it represents a more comprehensive view of
behavior. Further, although self-control is similar to a lack of constraint, Caspi et al
(1994) argue that Gottfredson and Hirschi’s theory is simplistic and “crime proneness is
defined not by a single tendency (such as self control or impulsivity) but by multiple
psychological components” (p. 187).
Summary
The developmental tradition that encompasses both Gottfredson and Hirschi’s
theory of self-control and Sampson and Laub’s turning points for behavior are very
relevant to explaining delinquency and adult crime. However, both theories have not
embraced the impact of personality on behavior. Although it is unrealistic to assume that
personality is the only “cause” of behavior, the exclusion of multiple psychological
components leaves a theory of criminal behavior incomplete. This study will attempt to
determine if personality is predictive of criminal behavior over time among an adult
sample. A final group of studies pertinent to personality and criminal behavior appear in
the correctional classification literature.
Personality and Corrections
Criminological theory and corrections are distinct but inter-related areas in
criminal justice. Although many researchers may consider themselves as conducting
research in either theory or corrections, the two areas must co-exist as their contributions
are in fact reciprocal. Related to this, Andrews and Bonta (1998) assert “there are two
major empirical tests of the adequacy of a theoretical understanding of criminal behavior.
One involves the ability to predict accurately variation in criminal behavior. The second
43
involves the potential to influence criminal activity by way of deliberate interventions
that focus on the causal variables suggested by the theory” (p. 7). That is, the
development of a theory of crime allows for both: (a) an understanding of how
personality and crime are related (which then may be incorporated into risk assessments
of its likelihood of reoccurring) and (b) the formulation of correctional interventions to
address personality both as a risk and responsivity factor. The development and testing
of both provides a test of the adequacy of the theory (Andrews and Bonta, 1998).
Effective Classification
The prediction of criminal behavior is one of the most important issues in
criminal justice. Classification refers to arranging groups according to some principle
such as a risk of flight or amenability to treatment. Prior to the development of
assessment measures, classification was primarily based on intuition or gut level feelings
regarding placement decisions. Predicting who will reoffend offers guidance for
managing offenders as well as intervening to reduce the probability of future behavior.
Research indicates that if the principles of effective classification are followed, the
effectiveness of our correctional interventions is likely to increase (Andrews & Bonta,
1998). In this vein, much of the classification work in criminology has a predictive aim
and advocates assessing key factors based on the probability of an expected event
(Gottfredson, 1987).
Knowledge of the predictors of criminal behavior originates from research
utilizing longitudinal designs and meta-analysis techniques. The most common goal of
classification instruments rests with determining risk. The risk principle refers to
identifying personal attributes or circumstances predictive of future behavior (Andrews,
44
Bonta, & Hoge, 1990). Risk is important for two reasons. First, assessing an individual’s
risk for future behavior indicates to us what factors are important in explaining criminal
behavior. Second, risk can be used to match offenders with appropriate levels of service.
Research indicates that we should reserve our intensive services for our higher risk
offenders whereas minimal intervention is needed for our low risk group (Andrews &
Bonta, 1998).
There are a number of factors or correlates of criminal behavior that aid in the
prediction of future crime and in turn the explanation of behavior. The factors identified
in the literature include both factors that cannot change and those that can be influenced
through treatment. Static factors include such individual characteristics as gender, prior
record, and age. These factors, although static, are established in the literature as
important to the explanation of criminal behavior. Dynamic factors such as companions,
attitudes, personality, family relationships, education, and employment status are also
important in predicting future criminal behavior.
Often what is unknown, however, is the magnitude or importance each predictor
has in explaining future criminal behavior. In response to this problem, Andrews and
Bonta (1998) discuss the meta-analysis conducted by Gendreau, Andrews, Goggin, and
Chanteloupe (1992) to determine the predictors of recidivism. Table 3 illustrates the
factors in order of importance.
45
Table 3. Summary of key predictors of criminal behavior.
Factor Mean r # of studies examined Lower social class origin .06 97 Personal distress/ .08 226 Psychopathology Educational/vocational .12 129 Parental/family factors .18 334 Temperament/personality .21 621 Anti-social attitudes/associates .22 168 ________________________________________________________________________ The major predictors include: lower social class origin, personal
______________________________________________________________________________________________________ Construct Definition Relationship to Criminal Behavior I-level 2 Tend to feel world should take Delinquency may result from poor care of him/her. Unable to impulse control or inability to cope understand or predict behavior with external pressures from others. Tends to be impulsive and unaware of effects of behavior on others; and sees others as barriers to
his or her satisfaction. I-level 3 Attempts to manipulate the Delinquency may result in an attempt environment in his/her favor. The to gain peer approval, gratification of manipulation may take the form of material needs, or an attempt to gain conforming behavior. They understand control in a situation via a “bad guy” how behavior is affected by role. relationships, but make no emotional investment. Unable to empathize and typically engage in only short term planning. I-level 4 Internalizes a set of standards by May respond to emotional conflict by which he/she judges one self acting out. This conflict may lead to internalizing and others. Feels guilt when not criminal standards and value systems. measuring up to standards or feels conflict when admiring others behavior
52
Table 5. Summary of key personality constructs for the Jesness Inventory (Jesness, 1962).
______________________________________________________________________________________________________ Construct Definition Relationship to Criminal Behavior Unsocialized aggressive (Aa) Displays negative attitudes toward High self reported criminal behavior convention. Behavior is unpredictable aggressive and antisocial Unsocialized passive (Ap) Displays negative attitudes toward Probability of criminal behavior average convention. Behaves in inappropriate and often bizarre ways. Negative self concept. Immature conformist (Cfm) Displays positive attitudes toward Low self reported criminal behavior. Peers convention. Behavior is conforming may be especially important explanation of and dependent (i.e. follower). criminal involvement. Cultural conformist (Cfc) Displays low motivation, negative High self reported criminal behavior. Above attitudes toward convention. Feels average violent activity alienated and hostile towards authority. Manipulator/pragmatist (Mp) Shows positive attitudes toward Low self reported criminal behavior but convention. Behaves in manipulative official records indicate high probability. way and obtrusive ways. Especially prone to property crimes
53
Table 5. Summary of key personality constructs for the Jesness Inventory, continued. ______________________________________________________________________________________________________ Construct Definition Relationship to Criminal Behavior Neurotic acting out (Na) Displays negative attitudes toward High self reported criminal behavior. convention. Experience conflicts in More apt to use drugs. relationships. See themselves as somewhat cynical and disenchanted, often exhibiting outspoken and non-conforming behavior Neurotic anxious (Nx) Mostly positive attitudes toward convention. Low self reported criminal behavior conforming but also dependent, anxious, and insecure. Do not have criminal orientation. Situational emotional reaction (Se) Positive attitudes toward convention. They are Low reports of criminal behavior, confidant, but naïve, rigid, and conforming. Expects to be caught if he/she breaks the law Cultural identified/adaptive (Ci) High verbal aptitude and positive toward Low reports of criminal behavior. convention. Maintains good interpersonal relationships. This type differs from Warren’s cultural identifier. Warren defines this type as having an internalized delinquent value system
54
Table 6. Summary of key personality constructs for the collapsed Jesness Inventory types (Van Voorhis, 1994).
______________________________________________________________________________________________________ Construct Definition Relationship to Criminal Behavior Aggressives Tend to be manipulative. Behavior High probability of criminal behavior. (Aa, Cfc, Mp) is unpredictable and negative. Feels alienated and hostile and has antisocial values. Not likely to possess prosocial values. Neurotics Tend to be anxious and insecure. High criminal probability when unable. (Na and Nx) Tend to be cynical, hostile, and to cope with anxiety. act inappropriately when anxious6. Dependents Although behavior may be Criminal behavior less likely than other types (Ap and Cfm) conforming, he or she tends When criminal behavior does occur it may be to follow others, including criminals the result of the influence of others and their This type is less clearly defined among limited cognitive functioning.
adults than among juveniles. Situationals Tend to view convention positively. Criminal behavior less likely, situational (Se and Ci) Tend to maintain good relationships if it does occur. but can be naïve and rigid
6 Some studies have found value in separating the Na and Nx categories (Van Voorhis, 1994).
55
expectedly mixed for the situational group, however, the dependent group was likely to
be evaluated unfavorably by staff. Finally, with regard to I-level, the results were often
not significant, however, when significant results were found it was the I-2 and I-3
inmates receiving more unfavorable results (Van Voorhis, 1994).
Summary
Classification in corrections has been evolving since the early 1900s. Recent
classification systems are increasingly comprehensive and have multiple goals. The
psychological systems designed to differentiate offenders for treatment have been in use
since the 1950s. It is widely accepted that differential treatment matching increases the
opportunity for interventions to be successful. Combining the importance of each
system, the proposed study will explore how well a psychological system such as the
Jesness Inventory predicts recidivism, but also how important personality is to predicting
risk. In order to accomplish these goals, a framework must be established.
An Integration of Personality Types
It is theorized that the Jesness Inventory can be used to mirror previous research
regarding the personality crime link. The extensive review of the relationship between
personality and crime as measured by the EPQ, MPQ, and Jesness Inventory have been
included to illustrate the importance of personality theory to criminology. As mentioned
in Chapter 1, the personality classifications produced by the Jesness Inventory will be
used to assess the relationship between personality and recidivism. The studies using the
EPQ and MPQ are of causation and the samples often include offenders and non-
offenders. However, the Jesness Inventory was developed to distinguish among a
criminal population and is more relevant to the exploration of recidivism. The types and
56
levels set forth by the JI and subsequent researchers (Van Voorhis, 1994) will be
analyzed in such as way as to mirror those types produced by both the EPQ and MPQ.
Table 7 details the relationships proposed between the various instruments.
Relationship Between Types
EPQ. The Jesness I-level types are more difficult to link to the types defined by
the EPQ and MPQ. However, I-2 and I-3 levels are more applicable than the I-4. The I-2
and I-3 three types most closely mirror the extraversion type set forth by Eysenck as they
show a tendency to act impulsively. It is important to note, however, that impulsivity is
not necessarily attributable to extraversion in all situations. There is no clear link
between the extraversion type and either the Jesness personality types or the collapsed
types set forth by Van Voorhis (1994). However, the two neurotic categories (i.e., Na’s
and Nx’s) correspond to his type neuroticism. Similarly, the collapsed neurotic type
discussed by Van Voorhis is also applicable. We see this in the descriptions set forth by
all three authors. Specifically, the type tends to be anxious and insecure, often irrational
and tense with emotions overriding reason. With regard to psychoticism, the Jesness
types Aa, Cfc, and Mp corresponds most closely. Moreover, the aggressive type set forth
by Van Voorhis (1994) best fits with the type of individual who is unempathetic, cold,
impersonal, and exhibits impulsive tendencies.
MPQ. With regard to the types defined by the MPQ, it appears the Se and the Ci
on the Jesness Inventory most closely resembles the type referred to as positive
emotionality given that Jesness found minimal occurrence of delinquency in these types.
The situational type defined by Van Voorhis (1994) mirrors this group. Again these
individuals tend to view convention positively and maintain good relationships but can
57
Table 7. Relationship between EPQ, MPQ and Jesness I-level, personality, and collapsed types.
EPQ/MPQ types Jesness I-level Jesness Personality Types Collapsed Jesness type Extroversion --- impulsivity not clearly tied to extraversion (Eysenck, 1952) Neuroticism --- Na & Nx Neurotic (Eysenck, 1952) Psychoticism --- Aa, Cfc, & Mp Aggressive (Eysenck, 1952) Positive Emotionality --- Not a criminal type Situational7 (Capsi and Silva, 1990) Negative Emotionality --- Na Neurotic (Caspi and Silva, 1990) Constraint --- Se & Ci Situational (Caspi and Siliva, 1990) Lack of Constraint I-2 & I-3 Ap, Cfm, Aa, Mp, & Cfc Aggressives/Dependents (Caspi and Silva, 1990) --- = no clear relationship 7 Although situational is defined as a criminal type, this type most closely reflects type positive emotionality.
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Table 7. Relationship between EPQ, MPQ and Jesness I-level, personality, and collapsed types, continued. EPQ/MPQ types Jesness I-level Jesness Personality Types Collapsed Jesness type Negative Emotionality/ I-4 Na Neurotic Lack of Constraint Extraversion/ I-4 Na Neurotic Neuroticism Extraversion/ --- Aa, Cfc, Mp Aggressive Psychoticism Neuroticism/ I-4 Na Neurotic Psychoticism Extraversion/Neuroticism/ --- Aa, Cfc, Mp Aggressive Psychoticism
59
exhibit the tendency to be naïve. The negative emotionality type defined by the MPQ
most closely resembles the Na type defined by Jesness and the neurotic group defined by
Van Voorhis (1994). Negative emotionality seems to represent a slightly different type
than the neurotic category defined by Eysenck. The type negative emotionality tends to
display anger, irritability, and perceives life in a very threatening and hostile way. These
traits are more similar to the Na than the Nx category set forth by the Jesness. The Na
type also displays negative and hostile attitudes toward convention and relationship.
Finally, individuals low on the type constraint most closely resemble the I-2 and I-3 level
and the Se and Ci type set forth by the Jesness. Again individuals with high levels of
constraint are less likely to be involved in crime, tend to endorse social norms and be
cautious. This also most closely resembles the situational type set forth by Van Voorhis.
The individual who exhibits a lack of constraint however, would more closely resemble
the Ap, Aa, Cfm, Mp, and Cfc types set forth by Jesness. These are individuals who
typically display negative attitudes toward convention. These individuals are both
aggressive and feel alienated and can become hostile and manipulative. The types
aggressive and dependent most closely mirror this type as well.
Combined Types. The combined types set forth by the EPQ and the MPQ
represent high-risk cases. That is, those individuals who are high on negative
emotionality and low on constraint are more likely to be involved in criminal behavior.
Likewise, it can be theorized that those individuals who exhibit extraversion paired with
neuroticism may also be more likely to commit crimes. The analysis will center on each
type separately and in tandem to determine the relationship between personality and
recidivism.
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Conclusions
The purpose of this review is not to disparage sociological theories of crime.
Rather, to formulate an argument that personality deserves recognition within
criminological discourse. Research has not progressed to the point of identifying and
organizing how personality interacts with many other social variables. Moreover,
although personality is discussed tangentially in lifecourse theories and is actually used in
correctional typologies as a consideration for treatment and sometimes placement,
research has not progressed to detailing how personality might act as a predictor of
criminal behavior. Hence, the current study hopes to address two areas. First, this study
is primarily concerned with adding to the current literature on personality and crime.
Second, although personality is recognized as a risk factor (Andrews & Bonta, 1998),
whether personality can predict future criminal acts of adult males over an extended
period of time remains to be explored.
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CHAPTER 3
Method
This study rests on both theoretical and empirical assumptions. As indicated in the
literature review the research pertaining to personality and criminal behavior is limited.
This study extends previous research by using a corrections-based personality measure
and a long-term follow-up period. This chapter will discuss the design, sample
characteristics, data sources, the various independent and dependent variables, and the
data analysis plan.
Design
In order to assess the relationship between personality and recidivism and the
degree of importance personality has in conjunction with traditional risk factors, the
current study utilizes a longitudinal design. Specifically, the study reports outcomes of a
sample of federal inmates over a 10 to 12 year follow-up period. A number of research
questions will be addressed. First, the study will explore whether certain personality types
are related to criminal behavior. Second, the relationship between personality and
persistence as outlined in the lifecourse and developmental research will be explored.
Finally, the study will explore whether individuals with certain personality profiles have
a propensity to offend in specific ways.
Sample
The original (Time 1) sample is comprised of inmates admitted to either a Federal
Penitentiary or a Federal Prison Camp between September 1986 and July 1988. The
individuals were selected as part of an NIJ funded classification study conducted in Terre
62
Haute, Indiana (Van Voorhis, 1994). The penitentiary is designated level 4/5 on the
Federal Bureau of Prisons security continuum. The facility is considered a low-
maximum security or high medium security facility. The prison camp is a minimum
security or Level 1 facility in the federal system. The groups will be combined in the
analysis to expand the sample size and represent a more diverse group of offenders8.
Given that inmates’ psychological states are often affected by imprisonment
(Bukstel & Kilman, 1980), the Time 1 sample was limited to those who recently began
their incarceration experience. The sample included only new inmates received at the
facility immediately after sentencing. Additional selection criteria excluded those who
were non-English speaking (n=80) or could not read or were expecting to be released
within four months after admission (n=26). Participation in the original study was
voluntary. In addition to those excluded above, it should be noted that some new intakes
did not participate in the Time 1 study for a variety of reasons, including: (a) refusal to
participate (n=97), (b) a repeated failure to respond to “call outs” (n=12), (c) being
unavailable during the first month of their sentence (e.g., out of writ, in lock-up) (n=13),
and (d) not being contacted by the research staff (n=168). The last situation occurred
when there were too many admissions for program staff to contact all potential
participants. In this situation, staff members were instructed to select inmates at random
in order to avoid biasing the selection process. The overall response rate was 76 percent
for the penitentiary group and 85 percent for the prison camp group. This resulted in an
overall sample of 369 individuals (179 from the penitentiary and 190 from the prison
8 Group membership (institution vs. prison camp) will be included as a control variable in the analysis
63
camp).
Attrition Analysis. Both data from Time 1 and Time 2 were utilized in the
current study. Demographic and Jesness Inventory data from Time 1 comprise the
independent variables used in the multivariate analysis. Jesness Inventory data were
available for approximately 88 percent of the Time 1 sample. Time 2 recidivism data
were used to create the dependent variables used in the analysis. However, some attrition
occurred when social security numbers did not produce an NCIC record. Specifically,
recidivism data were available for approximately 85 percent of the Time 1 sample. Once
the sample was limited to only those with a NCIC record and Jesness Inventory data, the
overall attrition rate for the Time 2 sample was 25 percent. Tables 1 and 2 in Appendix
A illustrate that there are no significant differences between the Time 1 sample (N=369)
and the current Time 2 sample limited to those with recidivism and Jesness Inventory
(N=277) data.
Tables 8 and 9 describe the current sample. With regard to race, 66 percent were
Caucasian and 28 percent were African American. The remaining 6 percent were
members of American Indian, Hispanic, or Asian ethnic groups. The mean age of the
sample is 34 years of age (median age 32). Marital status was mixed among the group as
40 percent classified themselves as married, 23 percent single, and 23 percent divorced.
Sixty-four percent of the males in the sample had a least one child. With regard to
education, 33 percent of the sample did not complete high school and 47 percent reported
they had experienced school failure. Finally, 47 percent were unemployed at the time of
the arrest that led to their Time 1 incarceration.
The majority of the participants had a prior record. Specifically, 87 percent of
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Table 8. Percentage and frequency distribution of participants’ social demographic characteristics. ________________________________________________________________________ Characteristic N % ________________________________________________________________________ Race White 184 66.4 Black 77 27.8 American Indian 6 2.2 Hispanic 8 2.9 Asian 2 0.7 Age at admission 19 to 29 90 32.7 30 to 45 154 56.0 46 and older 31 11.3 Mean 33.5 Median 32 Marital Status Married 109 39.9 Never married 66 23.1 Divorced 66 23.1 Separated 16 5.9 Widowed 1 0.4 Common-law 21 7.7 Number of Dependents None 98 36.3 One 57 27.1 Two 44 16.3 Three 42 15.6 Four 17 6.3 Five 6 2.2 Six or more 6 2.8
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Table 8. Percentage and frequency distribution of participants’ social demographic characteristics. ________________________________________________________________________ Characteristic N % ________________________________________________________________________ Education 6 to 11 years 91 33.3 High school 81 29.7 GED 46 16.8 Some post high school 31 11.4 College graduate 20 7.3 Some post college 4 1.5 Evidence of school failure Yes 125 47.2 No 140 52.8 Primary Occupation No occupation 26 9.6 Professional 19 7.0 Manager/Admin 27 10.0 Sales 13 4.8 Clerical 2 0.7 Craftsman 29 10.7 Equipment Operator 9 0.4 Laborer 105 38.9 Farmer 6 2.2 Service worker 2 0.7 Armed Services 3 1.1 Student 2 0.7 Househusband 2 0.8 Criminal occupation 24 8.9 Employment Status Not working 127 47.0 Full time 100 37.0 Occasionally 33 12.2 Status Unknown 10 3.7
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Table 9. Percentage and frequency distribution of participants’ prior and current offense record.
________________________________________________________________________ Characteristic N % ________________________________________________________________________ Prior adult or juvenile record Yes 242 87.4 No 35 12.6 Prior adult arrests Yes 240 86.6 No 37 13.4 Prior adult convictions Yes 220 79.7 No 55 19.9 Conflicting evidence 1 0.4 Prior prison sentence Yes 108 44.8 No 133 55.2 Amount of prior time served Two years or less 42 30.4 25 mo. to 5 years 33 23.9 61 mo. to 10 years 13 8.7 More than 10 years 7 5.1 Time served unknown 44 31.9 Prior arrest dealing drugs Yes 66 27.6 No 173 72.4 Prior professional criminal activity Yes 65 27.2 No 174 72.8 Prior violence Yes 104 43.7 No 134 56.3
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Table 9. Percentage and frequency distribution of participants’ prior and current offense record, continued. ________________________________________________________________________ Characteristic N % ________________________________________________________________________ Prior arrest for same offense As present offense Yes 60 25.0 No 180 75.0 Prior or current drug related offense Yes, prior 26 9.7 Yes, current 42 15.7 Yes, both 47 17.5 No 153 57.1 Prior or current alcohol offense Yes, prior 54 19.8 Yes, current 11 4.0 Yes, both 21 7.7 No 187 68.5 Prior prison escapes Yes 22 16.2 No 114 83.8 Prior probation revocations Yes 50 35.0 No 89 65.0 Prior parole revocations Yes 36 10.5 No 45 55.6 Prior record as a juvenile Yes 81 30.5 No 185 69.5 ________________________________________________________________________
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Table 9. Percentage and frequency distribution of participants’ prior and current offense record, continued. ________________________________________________________________________ Characteristic N % ________________________________________________________________________ Prior incarceration as a juvenile Yes 43 58.1 No 31 41.9 Prior violent record as juvenile Yes 19 25.7
Characteristic N % ________________________________________________________________________ Arrested new offense Yes 145 52.3 No 132 47.7 Arrested Multiple Times Yes 85 30.0 No 191 69.0 Arrest Charge Drugs 34 23.5
Property 42 29.0 Violence 25 17.2 Other 44 30.3
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Sorensen, 1990, p. 292). By using this technique, this analysis will distinguish whether
those who fail (i.e. incur an arrest) differ by personality or I-level type while controlling
for time at risk.
To accurately estimate the probabilities of an event, in this case arrest, the
analysis must deal with the fact that subjects are at risk for differing time periods of time.
Given the sample includes inmates entering prison at different periods and the
individuals are arrested at differing rates, it would be inaccurate to treat the length of their
histories at risk as equivalent. Specifically, incarceration dates for the adults in the study
varied from September 1986 to July 1988. Record checks were conducted in November
of 1998; hence the follow-up period varied from 10 to 12 years. Because inmates can be
arrested while in prison, the follow-up period began at the beginning of their
incarceration in the original study11. The record check data revealed that 25 inmates,
17.4% of those rearrested at least once, were charged with offenses while in prison. The
charges incurred while in prison include: drugs (40%), attempted escape (24%), property
(12%), weapons (8%), unspecified violations (8%), fraud (4%), and sexual misconduct
(4%).
Concepts such as the risk set, person periods, and the hazard rate are key to event
history analysis (Allison, 1984). The risk set refers to the number of individuals who are
at risk at each time period. The follow-up period is collapsed into years at risk after
analyses indicated that no one month was significantly different from another in
predicting outcome. In other words, overall there does not appear to be a tendency for
11 Data on the exact release date were not available on all participants. Projected release dates were available for 64% of the sample.
79
the arrest rate to significantly increase or decrease in any one month. Everyone is at risk
in year one (n=277). In each subsequent year, the number arrested were taken out of the
sample that remain at risk. As can be seen in Table 13, 16 individuals were arrested in
year one and are then no longer at risk in the remaining 11 years. Logically, the number
at risk declines with each year. One person died while in prison during year two and was
subsequently removed from the model. In addition, the follow-up period varied for those
incarcerated in 1987 and 1988 (i.e., they were at risk for shorter periods of time), hence
three additional individuals not arrested by the end of their follow-up period were taken
out of the at-risk pool in year 10 and 69 were taken out in year 11.
The second important concept in event history analysis is the creation of the
person period. For each period of time at risk (e.g., each year), a record is created for that
case or individual. This is known as the person period and it corresponds to the number
of years the person is at risk. The person arrested in the fourth year of the follow-up
would be given four person periods. The person arrested in the eight year would have
eight person periods. Those never arrested are considered right censored cases. In other
words, “right censoring occurs when the waiting time for the occurrence of an event is
longer than the period of observation” (Palloni and Sorenson, 1990, p. 304). They are
assigned the number of person periods that corresponds with the highest number of years
at risk. The follow-up period varies between ten and twelve years depending on when the
case entered prison, hence if the person was not arrested at all during our follow-up
period they were assigned either 10, 11, or 12 person periods. We do not want to
conclude that the censored cases will never incur another arrest, rather all we
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Table 13. Distribution by Year of Parolees at Risk (Model 1)
_______________________________________________________________________ Number Number Estimated Year Arrested at Risk Hazard Rate 1 18 277 .0649 2* 19 259 .0733 3 12 239 .0502 4 19 227 .0837 5 17 208 .0817 6 16 191 .0837 7 12 175 .0686 8 8 163 .0491 9 5 155 .0323 10** 6 150 .0400 11** 10 141 .0709 12 3 64 .0468 Total 145 2249 * One person died during the follow-up period and was excluded after year 2 **The follow-up period varied from 10 to 12 years. Of the 277, 3 only had a 10 year follow up and were taken out of the number possibly at risk and 67 had a 11 year follow up and were also censored at that time.
81
can say is that during our follow-up period an arrest was not incurred by these
individuals. Yet the need to “analyze simultaneously the data from individuals with
censored and noncensored events times is apparent because the former are a key group of
people: those least likely to experience the event” (Willett and Singer, 1997, p. 273). As
a result, for the sample of 277, there were 2268 person periods created. As seen in Table
13, this is just the sum of the number at risk in each of the 12 years. For each person
period, the dependent variable is coded a 1 if they are arrested and a 0 if not arrested.
The third concept that is important in event history analysis is the hazard rate. As
indicated by Allison (1984) “the hazard rate is the probability that an event will happen at
a particular time to a particular individual, given that individual is at risk” (p. 16). In the
present study, the hazard rate is the probability of an arrest within a particular year for
those who have not been arrested. To get the hazard rate, we divide the number arrested
by the total number still at risk during a particular year. Table 13 lists the hazard rates by
year. In addition to the hazard rate, it is useful to display the proportion of the sample
that fail through each period. Because there are individuals who never experience an
arrest during the follow-up period, the curves will never reach 100 percent. The rate of
failure over time will be displayed in the results section through failure curves.
Multivariate analyses were conducted by estimating a logistic regression model in
which arrest was regressed simultaneously on personality while controlling for the time
invariant variables: group (penitentiary vs. prison camp), race, time, and risk score.
Participants who do not recidivate are considered censored but their survival times are
included in the denominator, which is used to evaluate the failure rate. The multivariate
approach has two advantages in this case: it isolates the effects of personality on
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recidivism while controlling for other variables and illustrates the significant predictors
of recidivism.
Model #2: Personality and Frequency of Arrest
In the second model, repeated event history analysis was utilized to explore the
relationship between personality, I-level, and persistence. This second model explored
the relationship between personality and persistence. In other words, whether those
individuals arrested multiple times are likely to have certain personality profiles. This
analysis also included race, risk, time, and group as covariates with personality; however,
risk now becomes a time varying instead of a time-invariant predictor. It was mentioned
in earlier chapters that the salient factor score was chosen because it consisted of static
factors or those that cannot change. However, the overall risk score can increase over
time as individuals incur additional arrests. This analysis takes into account this
possibility and recalculates the risk score as needed.
In the first model once the person was arrested they were removed from the
analysis of subsequent years. Now the occurrence of an arrest does not remove
individuals from the risk set given they can experience multiple arrests during the
remaining follow-up periods. The only qualitative difference between this second model
and the first is in the definition of time; now the probability of an arrest refers to the
“duration since the prior occurrence rather than to chronological time elapsed since the
origin of the process” (Palloni and Sorensen, 1990, p. 303). Each arrest is treated
separately and the duration is measured since the date of the last arrest. The question
becomes whether the occurrence of one event or arrest predicts the occurrence of another
event or arrest (Willett and Singer, 1997). For example, if an individual is arrested in
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year one and then again in year three and finally in year eight, all three of those arrests
are accounted for in the calculation of the regression model predicting outcome.
The formulation of the person period database remained the same as in model 1,
however, instead of creating person periods based on the amount of time to elapse (i.e,
years) until first arrest, the calculation was based on whether a person was arrested in
each year. Years with an arrest were under study instead of the raw number of arrests
over the 12 year period. Considering that an arrest does not remove a case from the
analysis of subsequent years, each person had 10, 11, or 12 periods depending on the
length of his follow-up period.
Naturally, race and group membership do not change, however, the overall risk
score will for some cases. Hence, the risk score was recalculated each time an arrest
occurred in a given year. This model allows the risk score to increase over time (e.g.,
low to high). In each person period the individual is still given a risk score, however, in
some cases that value changes based on the calculation.
Multivariate analyses using logistic regression were conducted in which arrest
was regressed on personality, group, race, time, and risk score. Analyzing repeated
events with logistic regression might yield biased standard error terms. In response to
this possible problem the analysis was also conducted in Stata, which compensates and
corrects for the repeated measures effect by adjusting the standard error terms. However,
the analysis between the traditional logistic regression in SPSS and the regression
analysis conducted in Stata produced similar results. The results presented will be those
conducted in SPSS.
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Model #3: Personality and the Seriousness of the Offense
In the third model, discrete time event history analysis was utilized to explore the
relationship between personality, I-level, and offense type. There are a variety of ways to
explore offense seriousness. Offense seriousness scales have been used in sentencing
decisions for a number of years. For example, “since 1973 the United States Parole
Commission has used explicit guidelines to structure its discretion in deciding the
duration of prison terms for federal prisoners under its jurisdiction. The guidelines
matrix is in the form of two dimensional charts. The seriousness of the prisoner’s current
offense is graded on an index that forms the vertical axis of the chart. The empirically
developed scale known as the Salient Factor Score, designed to assist in assessing the
prisoner’s risk of recidivism forms the horizontal axis of the guideline chart” (Hoffman
and Hardyman, 1986, p. 414). However, in order to calculate a severity scale such as this
one, the researcher must know not only the offense type, but also whether the offense
involved a weapon, whether co-defendants were involved, whether the crime resulted in
physical injury, or, if a theft, the amount and type of goods stolen.
The data on offense details in the NCIC system is very limited. Data on charge
type is available, however, other characteristics such as weapon and co-defendant
involvement, victim impact, and theft characteristics are not collected. As a result, this
study is limited to an analysis of the impact of personality on type of offense. The
categories chosen are: drug offenses, property offenses, and violent offenses. In effect to
answer whether individuals with certain personality profiles are more likely to be arrested
for specific offense types. While these categories may not reflect a detailed assessment
of seriousness, the types do reflect important distinctions in offending behavior.
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The creation of the risk set, person periods, and hazard rates are the same as found
in Model 1. However, the current models are limited to those arrested for specific
offenses. In all three models, those not arrested for the offense in question are all treated
the same. That is, if someone was not arrested for a drug related offense but was arrested
for a theft, that person would be combined in the first model with those never arrested
during the follow-up period. As seen in Table 14, there were 34 individuals arrested for
drug related offenses. As a result, for the sample of 277, there were 2948 person periods
created. For each person, the dependent variable is coded a 1 if they were arrested for a
drug related offense and 0 if they were not arrested for a drug related offense. Similarly,
Table 15 indicates that 42 individuals were rearrested for a property related offense. For
the sample of 277, there were 2891 person periods created. Again, the dependent
variable is coded a 1 if the person is arrested for a property related offense and 0 if not.
Finally, as seen in Table 16, 25 individuals were arrested for a violent offense. For the
sample of 277, there were 3037 person periods created. The dependent variable was
coded
Multivariate analyses were conducted by estimating each model separately to
determine whether personality was related to offense type. Only the first offense was
analyzed in this model. That is, the analysis determined whether personality was related
to the offense type of the first arrest during the follow up period. The regression model
includes group, race, risk, time, and personality as predictors of arrest for the three
offense categories. As with the previous models, participants who are not arrested for the
specific offense in question are included in the analysis and considered right censored.
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Table 14. Distribution by Year of Parolees at Risk (Model 3: Drug Offenses)
________________________________________________________________________ Number Number Estimated Year Arrested at risk Hazard Rate For Drugs 1 7 277 .0253 2* 7 270 .0259 3 2 262 .0076 4 3 260 .0115 5 3 257 .0116 6 1 254 .0039 7 3 253 .0118 8 1 250 .0040 9 1 249 .0004 10** 3 248 .0121 11** 2 240 .0083 12 1 128 .0078 Total 34 2948 * One person died during the follow-up period and was excluded after year 2 **The follow-up period varied from 10 to 12 years. Of the 277, 5 of those not arrested only had a 10 year follow up and were taken out of the number possibly at risk and 110 of those not arrested had a 11 year follow up and were also censored at that time.
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Table 15. Distribution by Year of Parolees at Risk (Model 3: Property Offenses)
________________________________________________________________________ Number Number Estimated Year Arrested at risk Hazard Rate For Property 1 4 277 .0144 2* 6 273 .0220 3 4 266 .0150 4 8 262 .0305 5 6 254 .0236 6 5 248 .0202 7 1 243 .0041 8 4 242 .0165 9 1 238 .0042 10** 1 237 .0042 11** 1 231 .0043 12 1 120 .0083 Total 42 2891 * One person died during the follow-up period and was excluded after year 2 **The follow-up period varied from 10 to 12 years. Of the 277, 5 of those not arrested only had a 10 year follow up and were taken out of the number possibly at risk in the subsequent years and 110 of those not arrested had a 11 year follow up and were also censored in that year.
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Table 16. Distribution by Year of Parolees at Risk (Model 3: Violent Offenses)
________________________________________________________________________ Number Number Estimated Year Arrested at risk Hazard Rate For Violence 1 2 277 .0072 2* 2 275 .0073 3 2 272 .0074 4 2 270 .0074 5 1 268 .0037 6 4 267 .0150 7 5 263 .0190 8 1 258 .0039 9 2 257 .0078 10** 1 255 .0039 11** 3 249 .0120 12 0 126 .0000 Total 25 3037 * One person died during the follow-up period and was excluded after year 2 **The follow-up period varied from 10 to 12 years. Of the 277, 5 of those not arrested only had a 10 year follow up and were taken out of the number possibly at risk in the subsequent years. In addition, 120 of those not arrested had a 11 year follow up and were also censored after that time.
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CHAPTER 4
Results
Multivariate Analysis of Arrest: Model 1
Of the 277 participants in the study, 145 (52.3%) were arrested at least once
during the follow-up period. By using event history analysis, it is possible to estimate a
model of the probability of an arrest during the follow-up period and distinguish the
relative influence of three variables assumed to be constant over time: personality, race,
and risk (as measured by the SFS which includes only static risk factors). Results of the
event history analysis by personality type are shown in Figure 1. Figure 1 represents the
independent effects of personality with race and risk controlled. For ease of
interpretation, the figures will be presented as failure curves instead of survival curves.
The failure curves shown are based on the probability of arrest at each year based on the
proportion that failed or were arrested. The results are presented as curves and “the
smoothness results from the constraints inherent in the population hazard model
stipulated in (1), which forces the vertical separation between the two hazard functions to
be identical in every time period. Just as we do not expect a fitted regression line to
touch every data point in a scatterplot, we do not expect a fitted hazard function in
survival analysis to match every sample value of hazard” (Willett and Singer, 1997, 281).
A number of regression equations were estimated to determine the impact of a
variety of independent variables. After exhausting several models that included race,
risk, group, personality, and time (see appendix B) the final model is limited to the
significant variables which include: a dichotomous measure of risk (1= low/good risk; 0=
high/poor risk), race (1=white; 0= non-white), and the four personality types (aggressive
1=yes; 0 = other; neurotic 1=yes; 0= other; etc) originally discussed in Van Voorhis
(1994). The analysis indicated that all three variables were significantly related to the
probability of failure among the group of federal inmates (see Table 17). The curves
shown represent the predicted failure rates by each personality group by length of time
(years) from the beginning of the inmate’s incarceration. Throughout the follow-up
period, the highest probabilities of arrest was among the neurotics followed by the
aggressives, situationals, and finally the dependents (Appendix B also illustrates the
models with rotated personality types). The analysis indicated that aggressives and the
neurotics were significantly different than the dependents but not significantly different
from each other or the situationals. In fact, the situationals were not significantly different
from any of the other types. In addition to the importance of the difference between
types was the finding that personality still remains a significant predictor of recidivism
even while controlling for race and risk12. Finally, the same models were estimated using
Interpersonal level maturity categories, however, as seen in Appendix B the relationship
between I-level and outcome was not significant.
12 Regression models with and without personality were estimated to distinguish whether personality contributed to the model. It is noted that exploring R-square differences is not an ideal method for assessing the magnitude of the relationship, however, the differences are informative. The model including only race and risk had an Nagelkerke R-square is .136 and with the addition of the personality variables the R-square valued increased to .179.
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Table 17. Model 1 Logistic Regression: Probability of Rearrest by Personality & Control Variables
Aggressive .610** .281 1.841 Neurotic .909** .322 2.482 Situational .483 .354 1.621 Intercept -3.690 Model Chi Square 33.520 * p <.05 ** p <.01 *** p <.001 a Categories: 0= low; 1= high b Categories: 0= white; 1= non-white c Dependent type is the omitted variable
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Figure 2
Rearrest Rate by Personality Type:
Low Risk
Statistically significant differences
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non-white), and the four personality types (aggressive 1=yes; 0 = other; neurotic 1=yes;
0= other; etc). Results are presented in a failure curve in Figure 5. As indicated in Table
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Table 18. Model 2 Logistic Regression: Probability of Multiple Rearrest by Personality & Control Variables
________________________________________________________________________ Parameter Standardized Odds Ratio Estimate Estimate SFS Riska .826*** .125 2.284 Raceb .649*** .126 1.914 Personalityc Aggressive -.208 .160 .812 Dependent -.483* .216 .617 Situational -.447* .220 .639 Time Time 2 -.319 .175 .727 Time 3 – 6 -.756*** .148 .470 Time 7 – 12 -1.197*** .179 .302 Intercept -2.0109 Model Chi Square * p <.05 ** p <.01 *** p <.001 a Categories: 0= low; 1= high b Categories: 0= white; 1= non-white c Neurotic type is the omitted variable Year one is the omitted variable
Table 19. Model 3 Logistic Regression: Probability of Rearrest for a Drug Offense by Personality & Control Variables
________________________________________________________________________ Parameter Standardized Odds Ratio Estimate Estimate SFS Riska .179*** .377 1.116 Raceb .109*** .387 1.197 Personalityc Aggressive -.987* .408 .372 Dependent -1.479* .662 .228 Situational -1.124* .582 .325 Intercept -3.7633*** Model Chi Square 8.486 * p <.05 ** p <.01 *** p <.001 a Categories: 0= low; 1= high b Categories: 0= white; 1= non-white c Neurotic type is the omitted variable
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19, all three variables were significantly related to the probability of failure for a drug
related offense. Throughout the follow-up period the neurotics had a much greater
likelihood of being arrested for a drug related crime in comparison to the aggressives,
dependents, and situationals. The neurotics were significantly different from the other
personality categories; however, the other three (i.e., aggressives, dependents, and
situationals) were not significantly different from each other (see Appendix D).
The same models were estimated using the I-level categories. The model
included a dichotomous measure of risk (1= low/good risk; 0= high/poor risk), race
(1=white; 0= non-white), and the three I-levels (I-2 1=yes; 0 = other; I-3 1=yes; 0= other;
etc). Results are presented in a failure curve in Figure 6. As seen in Table 20, the
relationship between I-level and arrest for a drug related offense were significant. The I-
4 category had the highest probability of arrest followed by the I-2s and I-3s. The I-4
category is significantly different from the I-3 category, however, not significant from the
I-2 category. In fact, the I-2 category is not significantly different from either the I-4 or
the I-3 categories.
Property Offenses. Of the 277 individuals in the study, 42 were arrested for a
property offense. Appendix E contains the results of several runs, however, the final
model included a dichotomous measure of risk (1= low/good risk; 0= high/poor risk),
race (1=white; 0= non-white), and the four personality types (aggressive 1=yes; 0 =
other; neurotic 1=yes; 0= other; etc). Results are presented in a failure curve in Figure 7.
As indicated in Table 21, the only variable found to be significant was risk. Risk (higher
risk individuals) was significantly related to the probability of failure for a property
related offense. Neither personality or the levels were significantly related to the
Table 20. Model 3 Logistic Regression: Probability of Rearrest for a Drug Offense by Personality & Control Variables
________________________________________________________________________ Parameter Standardized Odds Ratio Estimate Estimate SFS Riska .224 .389 1.251 Raceb .054 .389 1.055 Interpersonal Leveld I – 2 -.177 .786 .838 I – 3* -.722* .375 .486 Intercept -4.180*** Model Chi Square 4.014 * p <.05 ** p <.01 *** p <.001 a Categories: 0= low; 1= high b Categories: 0= white; 1= non-white c I-4 is the omitted variable
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Figure 7
Rearrest Rate by Personality Type: Property
Offenses
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Table 21. Model 3 Logistic Regression: Probability of Rearrest by Personality & Control Variables predicting Property Offenses
________________________________________________________________________ Parameter Standardized Odds Ratio Estimate Estimate SFS Riska .157*** .332 1.170 Raceb .814 .333 2.256 Personalityc Aggressive -.095 .425 .910 Dependent -.463 .559 .629 Situational -.595 .616 .552 Intercept -4.4328*** Model Chi Square 8.987 * p <.05 ** p <.01 *** p <.001 a Categories: 0= low; 1= high b Categories: 0= white; 1= non-white c Neurotic type is the omitted variable
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probability of failure for a property related offense (see Appendix E).
Violent Offenses. Of the 277 participants in the study, 25 were arrested for violent
offense. The same procedure and models listed above were used to predict the probability
of violence by personality types. The more inclusive model can be seen in Appendix F,
however, the final model included a dichotomous measure of risk (1= low/good risk; 0=
high/poor risk), race (1=white; 0= non-white), and the four personality types (aggressive
1=yes; 0 = other; neurotic 1=yes; 0= other; etc). The results are presented in Figure 8.
As indicated in Table 22, race was the only significant variable found in the model. It
appears that African Americans have a higher probability of violence than whites with
risk and personality included in the model. The I-level types were not significantly
related to the probability of failure for a violent offense (see Appendix F). Similar to the
property offense model, the personality characteristics were not significantly different
from each other in their probability of being arrested for violent offenses.
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Figure 8
Rearrest Rate by Personality Type: Violent Offenses
Table 22. Model 3 Logistic Regression: Probability of Rearrest by Personality & Control Variables predicting Violent Offenses.
________________________________________________________________________ Parameter Standardized Odds Ratio Estimate Estimate SFS Riska 1.173 .438 3.233 Raceb .597*** .448 1.816 Personalityc Aggressive -.125 .596 .883 Dependent .282 .696 1.326 Situational -.526 .875 .591 Intercept -5.540*** Model Chi Square 14.702* * p <.05 ** p <.01 *** p <.001 a Categories: 0= low; 1= high b Categories: 0= white; 1= non-white c Neurotic type is the omitted variable
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CHAPTER 5
Discussion and Conclusions
As depicted in Chapter 2, the research on personality and crime is fairly limited.
More importantly, comparatively fewer studies have isolated the likelihood that
personality predicts recidivism over time. The current study builds upon the research in
criminology and in corrections to develop an understanding of whether certain
personality types are more likely to be arrested and whether a measure of personality
should be added to traditional risk assessment instruments. The implications of this
research impact theory development, risk assessment technology, and correctional
programming. The purpose of this chapter is to summarize the limitations, findings,
theoretical and policy implications, and suggestions for future research.
Limitations
Although this study does advance the current literature base, there are several
limitations worth noting. First, the sample is limited to adult males. The findings, while
relevant for female populations, are based on male offenders. Second, the recidivism
data were collected via official records. The NCIC records are based on reports of
criminal activity by state. As of 1998, 36 states provided records to this national arrest
data source. The fourteen states that did not report data to NCIC include: Nebraska,
Kansas, Wisconsin, Louisiana, Mississippi, Tennessee, Kentucky, West Virginia, District
of Columbia, Rhode Island, Massachusetts, Maine, New Hampshire, and Vermont. This
limitation paired with the recognition that official records fail to capture crimes not
reported to police (Schneider and Wiersemer, 1990) may lead some to question whether
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the study has failed to capture all of the crimes committed by these research subjects.
Although the extent of this problem for the current study cannot be assessed, a few points
should be taken into consideration. First, given many of the participants in the study
have lengthy prior records and were on parole supervision, one may speculate that the
detection of their criminal behavior was more likely. Second, the overall recidivism rate
of 52 percent is within a typical range of recidivism for adult male offenders. Finally, the
findings are consistent with the research by Caspi et al. (1994), which included four
measures of delinquency: self-report, informant, police contact, and court convictions.
Even with these considerations in mind, future efforts to understand the relationship
between personality and criminal behavior among an adult sample would inevitably
benefit from incorporating multiple outcome measures.
Second, and of equal concern, the data reported to NCIC are often limited to the
arrest date and charge and do not adequately capture crime seriousness. Having access
to data limited to charge type can bias an analysis of crime seriousness in two ways.
First, arrest charge is subject to decision bias, that is, the decision to charge with a
specific offense. Second, data including weapons or co-defendant involvement were not
available. Data of this nature would allow for a more accurate assessment of crime
seriousness. Given this circumstance, there is no way to examine seriousness. However,
it should be noted that the models pertaining to offense type do contribute to our
understanding of the relationship between personality and criminal behavior.
Third, the data were collected at only two points in time. The personality and
demographic data were collected at Time 1 (1986-1988) and the recidivism data at Time
2 (1998). This method did not allow for an assessment of important dynamic risk factors
111
(i.e., marriage, stability, employment status, educational attainments) that may be related
to outcome (Sampson, & Laub, 1993; Andrews & Bonta, 1998). Moreover, data
pertaining to service exposure or treatment post-release were not available. As such, it
cannot be discerned whether the effects found would have been mediated by other
variables.
Finally, and in a similar vein, it is recognized that this study examines the
individual’s offending career in late adulthood. Hence, the investigation of personality
and crime across developmental periods could not be examined. Although research
supports the idea that personality is relatively stable throughout adulthood, it is also
recognized that personality and the situation are mutually reinforcing (Caspi & Roberts,
1999). Future research would benefit from studying the personality of an offender
during developmental stages to further explore the impact of the environment on
personality among a criminal population.
Summary of Findings
As observed by Winter and Barenbaum (1999), the attempt to study personality
rests on the assumption that personality is worth studying. Although the development of
personality research did not escape the scrutiny of situationists, the literature review in
Chapter 2 illustrates that personality is able to contribute to our understanding of
behavior. In criminology, the view that personality is correlated with criminal behavior
has developed with some resistance (Andrews & Bonta, 1998; Andrews & Wormith,
1989). Aiding this cause, current research conducted with personality inventories such as
the EPQ and the MPQ finds that personality is related to various types of criminal
behavior and can predict criminal behavior over time (Caspi et al., 1994; Caspi et al.,
112
1994; Eysenck, 1952, 1983, 1996; Krueger et al., 1994; & Krueger et al., 1996).
Moreover, research in corrections with psychological typologies supports the notion that
treatment should be matched with individual differences in mind. As such, this study was
designed to address several research questions.
The main focus of the current research identified whether certain personality
types were significantly related to recidivism among adult male offenders. The data were
used to explore three main research questions in an effort to explore whether individuals
with certain personality characteristics were more likely to be (a) arrested, (b) arrested
multiple times, or (c) arrested for a certain type of offense. The findings from the
questions above have implications for answering whether the Jesness Inventory is useful
in predicting criminal behavior and whether personality should be added to existing
traditional risk assessment models. Overall, the findings from this study support the
notion that personality is an important predictor of criminal behavior.
The findings from the first model indicate that personality contributes to the
prediction of criminal behavior while controlling for race and risk. In other words,
personality is significantly related to the probability of criminal behavior above and
beyond the variation explained by the risk variable and race. The findings from this
study support the research conducted by Eysenck and Caspi et al. (1994) and the research
on psychopaths conducted by Hemphill and Hare (1995) and Hemphill, Hare, and Wong
(1998), which finds a relationship between psychopathology and recidivism. The current
study found that the highest probabilities of arrest were among the neurotics followed by
the aggressives, situationals, and dependents. The neurotics and aggressives were
significantly different from dependents in their probability of criminal behavior. Simply
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put, neurotics and aggressives had a significantly higher probability of experiencing an
arrest in comparison to the dependents. Interpersonal maturity levels were also explored,
however, the levels were not significant in the model predicting arrest.
Second, having established that personality was significantly related to outcome,
the analysis shifted to exploring whether personality could predict persistence.
Specifically, whether certain personality types could characterize the individuals who
were arrested multiple times. Much of the previous work in this area has been with
juveniles and delineates the relationship between temperament styles or early aggressive
behavior and later delinquent behavior (Caspi, et al., 1995; Moffitt, 1990; Patterson, et
al., 1992; & Statin & Magnusson, 1989). While many of the previous studies followed
the youth into their early twenties, the average participant in the current study was 30
when the follow-up period began. This study provided an opportunity to study the adult
offender during the later stages of their criminal career.
The findings from the second model are similar to those found in previous
research. The results indicate that the neurotic type had a significantly higher probability
of being arrested multiple times in comparison to the other types even when controlling
for time, race, and risk. Although previous research finds that the Jesness aggressive
type, which is argued to mirror the EPQ psychoticism type, may be more likely to be
arrested over the lifespan (Eysenck, 1983), neuroticism is also important. Specifically,
Eysenck (1983) argues that neuroticism is more important in older samples and
contributes to stronger antisocial habits in adults. By using repeated event history
analysis, it became clear that neurotics had the highest probability of arrest and were
significantly different from both the dependents and situationals. The significant
114
differences in this model are slightly different from the first in that the aggressives were
not significantly different from any of the other types. It should be noted that while not
significant, the aggressive type did have slightly higher probabilities of multiple arrests in
comparison to the dependents and situationals. Finally, the differences between
personality types holds over time. That is, the neurotic type predicted the probability of
multiple offenses over an extended period of time.
Finally, the analysis focused on offense type to explore whether personality could
predict specific types of offending behavior. Personality was significant in one of the
models. Specifically, neurotics were more likely than the other types to be arrested for a
drug related offense. This is consistent with previous research on the neurotic type;
specifically, research that finds drug and alcohol use among those with high levels of trait
anxiety (Chambless, Cherney, Caputo, and Rhienstein, 1987; Smail, Stockwell, Canter,
and Hodgeson, 1984). Further, research indicates alcohol may be used to reduce the
negative affect that drinkers feel when they are anxious or overaroused (Cloninger,
Sigvardsson, Prybeck, and Svrakic, 1995). Related, a study by Roy (1999) found that
depressed alcoholics had significantly higher neuroticism scores as measured by the EPQ.
In addition, I-level was also significant in relationship to drug charges. The I-4s have an
internalized value system and are typically more likely to speak out against criminal
values; however, the use of drugs and alcohol may inhibit this control.
The evidence suggests that personality types were not significantly different from
one another in the analysis of violent or property offense types. In other words, the
analysis was unable to distinguish any differences between the types when predicting the
probability of being arrested for either a property or violent offense. This finding is
115
counterintuitive to the research which finds that psychopathy is related to violent
Cormier, 1991). However, there may be a number of explanations for these findings.
First, it is very difficult to predict violent behavior (Andrews & Bonta, 1998). The
difficulty stems from the low base rate considerations. Specifically, the results with
regard to violence may be related to the occurrence of violence in the current sample. Of
the 277 individuals at risk, only 25 individuals were arrested for a violent offense during
the follow-up period. It may be that the behavior did not occur frequently enough within
the personality types to distinguish any differences. Second, the previous research that
finds psychopathy is related to violence classifies psychopathy with the Psychopathy
Checklist Revised (PCL-R) (Hare, 1991). The aggressive type measures by the Jesness
Inventory includes offenders who have antisocial values and lifestyles but may not be
psychopaths. In fact, Hare (1982) questions the comparability between the psychoticism
type as measured by the EPQ and the construct of psychopathy measures by the PCL-R.
Third, we may speculate that by the time individuals reach this stage of their criminal
career, they may be less likely to engage in violent acts or are more likely to be serving
time in institutions. Finally, with regard to property offenses, while research supports the
relationship between personality and drug related behavior, there is no evidence that
personality should distinguish the likelihood of an offender committing a property
offense. This analysis was explorative and the lack of evidence is not necessarily
inconsistent with previous research. Future research, however, may benefit from
exploring the relationship between offense type and personality across developmental
stages.
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The findings regarding the situational type appear somewhat inconsistent with
previous research. Specifically, situationals were not significantly different from the
aggressives or the dependents in any of the models. While it would be expected that the
dependents and situationals might have similar arrest patterns, the fact that the situtionals
had a rate similar to the aggressives was surprising. Although it is difficult to pinpoint
why this may be the case, a few points of speculation may be offered.
First, the Case Management Classification (CMC) system, which is another
personality classification system that measures the situational type, divides the type into
situationals with a substance abuse problem and those without such a problem. The data
for the current study do not allow for this distinction. In other words, it may be that the
situational type used in the current study is too broad and does not accurately capture the
personality traits that comprise positive emotionality or constraint as outlined by the
MPQ. Second, this group is assumed to have a lower risk of recidivism over time;
however, no long-term recidivism studies with Jesness Inventory have been conducted. It
could be that the older more entrenched criminals that are classified as situationals
perform differently. Finally, the effects of prisonization (see Thomas, 1977) may have
influenced individuals characterized as situationals more than the other types. The
personality data obtained for this research were collected at prison intake for Time 1
(1986-1988). The impact of prison and exposure to criminal peers may have influenced
the future behavior of these individuals.
Finally, taken as a whole, I-level did not contribute to our understanding of
criminal behavior in this sample of adult males. Theoretically we should expect the
highest probability of arrest among the I-2s followed by the I-3s and I-4s. In fact,
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although the findings pertaining to I-level in the original study were minimal, the inmates
classified as I-2 and I-3 were significantly more likely to be cited for disciplinary
infractions in comparison to the I-4s (Van Voorhis, 1994). These maturity levels
represent successive stages from least mature to a higher level of maturity. While the
analysis indicated that there were significant relationships between I-level and drug use
(e.g., I-4s had a significantly higher probability of arrest from the I-3s), I-level was not
significant in any of the other models. There are a number of possibilities why I-level
was not significant. The first is sample size; of the 277 individuals under study only 12
(4.3%) were classified as I-2. The second possibility is that the I-level types do not
perform as expected in a prediction model with an adult sample. Given this study is the
first of its kind with an adult sample, the findings may illustrate the limitations of using
maturity levels to predict future criminal behavior.
Overall, the data appear to suggest that the relationship between personality and
crime is significant. The aggressive type performed as expected in the first model
predicting arrest and the neurotic type consistently emerged as important in each of the
models predicting offending behavior. By successfully using the a correctional typology
scale in predicting recidivism, this study supports the notion that personality is an
important risk factor and can assist our understanding of offenders both theoretically as
an explanation for behavior and practically for the application of treatment. As such,
these findings have implications for both theory and policy development.
Broader Implications of the Findings
This research set out to answer several questions about personality and crime. As
a result, the study advances the knowledge reviewed in Chapter 2 and provides support
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for a theory of personality in criminology and the formulation of policies in corrections
designed to assess and treat the offender.
Overall these findings are important because they provide support for the work of
criminologists researching individual-centered theories of crime. The findings are in
contrast to the earlier reviews of personality and crime (e.g., Schuessler & Cressey, 1950;
Tennenbaum, 1977; and Waldo & Dinitz, 1967) and claims by researchers such as Vold
and Bernard (1986) who argue that personality provides no theoretical relevance to
understanding criminal behavior. As researchers begin to take seriously the individual-
centered theories of crime, the relationship between personality and criminal behavior,
and as additional longitudinal studies assessing this relationship are conducted, better
estimates of how personality influences behavior over the lifecourse will come available.
While accumulating studies are finding a relationship between personality and
criminal behavior, many of these studies are conducted with adolescents and young
adults (Caspi et al, 1994; Caspi et al., 1997; Krueger et al., 1996). This study adds to the
current research by delineated the importance of personality among adult male offenders.
Investigation of this group is important because some may argue that personality acts
differently across age groups (e.g., extroversion in adolescent groups in contrast to
neuroticism in older adults; see Eysenck, 1983). As mentioned previously, many of the
previous studies only follow the juvenile into their early 20s. The average age of the
participant in this study was 30 years old and by the end of the follow up period the
typical person was in their early 40s. This exploration of older adults sheds light on the
importance of personality in predicting offending behavior in what is considered the late
stages of an individual’s criminal career.
119
From a policy perspective, researchers advancing risk assessment technology
should also consider the effects of personality. Few researchers have used personality or
correctional typologies as predictors of risk. Moreover, although the research by
Andrews and Bonta (1998) find that personality is among the strongest predictors of
behavior, risk assessment instruments currently in use such as the Salient Factor Score
(Hoffman & Beck, 1985), the Level of Service Inventory-Revised (Andrews & Bonta,
1995), and Wisconsin Risk Assessment System (Baird, Heinz & Bemus, 1979) typically
do not include measures of personality. Further, while researchers such as Andrews and
Bonta (1998) identify a variety of individuals characteristics such as impulsivity,
restless/aggressive energy, and weak problem solving skills that are descriptors of
personality types (e.g., the neurotic and aggressive), they do not specifically outline
which personality type(s) is most important. The finding that neurotics and aggressives
are more likely than the other groups indicates that future assessment research should
explore these types in particular. Notwithstanding, the finding that personality was
significantly related to outcome while controlling for risk implies that personality should
be added to existing risk assessment models.
In addition to risk assessment, the results of this research could also impact
treatment strategies. From this perspective, the results would suggest that strategies to
intervene with the adult male offender should recognize personality, specifically the
characteristics of the neurotic type. Given that individuals high in negative affectivity
construe their environment in ways that are consistent with their personality traits (Costa
and McCrae, 1999), teaching individuals how to change thought patterns and subsequent
reactions becomes central to their probability of change. Similarly, behavior therapy is
120
based on the notion that environment affects learning and behavior. Cognitive-behavioral
therapy targets thinking and problem solving through a system of reinforcement, pro-
social modeling, and role-playing. Individuals with antisocial attitudes that are high on
neuroticism need intensive treatment towards increasing their self-regulation. Similar
strategies would work with the aggressive type. For example, as described by Van
Voorhis (1994), the aggressive type tends to be manipulative, often feel alienated and
hostile and has antisocial attitudes. Cognitive based curriculums designed to challenge
these attitudes would be an effective option. Similarly, Caspi and Roberts (1999) argue
that behavioral models can influence change in a person’s behavior and over time can
impact change in personality.
While many of these strategies (i.e., cognitive behavioral driven models) are
designed to treat the aggressive criminal with antisocial attitudes supportive of criminal
behavior, fewer strategies have been developed for the aggressive psychopath. Treatment
strategies should also be matched to this type, however, the research on the effectiveness
of treatment with psychopaths is relatively mixed. Research indicates that psychopaths
suffer from an information-processing disorder and this inhibits their ability to pay
attention (Wallace, Schmitt, Vitale, & Newman, 2000). One study done by Hitchcock
(1995) found psychopaths who participated in a cognitive thinking errors group appear to
have decreased their the frequency of thinking errors as measured by a post-test.
Interestingly, another study by Seto and Barbaree (1999) found that subjects
characterized as psychopaths did well in treatment but were more likely to reoffend than
non-psychopaths. This may imply that psychopaths are more likely to appear to make
progress in treatment but in reality do not internalize the appropriate skills.
121
In addition to considering personality type and the impact on treatment strategies,
personality should also be further explored as a responsivity factor. As argued by Van
Voorhis (1987), by failing to recognize that individuals respond to treatment differently,
we routinely mask a treatment effect. If personality types are not considered when
matching offenders to treatment groups, the result could be a decrease in the overall
effectiveness of the intervention. It could be argued, for example, that highly anxious
individuals would not perform well in a therapeutic community setting that utilizes
shaming and highly confrontive techniques. Similarly, research reviewed by Hobson,
Shine, and Roberts (2000) found that psychopaths who participate in therapeutic
communities tended have high attrition rates and low levels of motivation. Taken
together, these findings suggest that the effectiveness of treatment strategies will
inevitably be compromised if personality type is not taken into consideration.
Prison officials should recognize that the neurotic type might respond negatively
to incarceration. In fact, in the original study, Van Voorhis (1994) found evidence of this
as the neurotic types experienced difficulties with stress and fear and expressed a greater
need for programmatic support. The neurotic type was more likely than the aggressive or
situational type to report higher scores on the Center for Epidemiological Studies
Depression Scale (CEDS) (Radloff, 1977). In addition, the study found that only 30
percent of the participants perceived others as willing to help them. Research by Warren
(1983) also finds the neurotic category showing evidence of emotional disturbance such
as depression. Moreover, she argues that these individuals often evidence tension and
fear due to their feelings of failure and guilt. This is in line with the idea that the
neurotic type tends to construe his or her life and interactions negatively. This negative
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orientation and self-defeating thought process should be recognized before deciding
treatment strategy and placement.
From a broader policy perspective, future research should explore why the
neurotic type has a higher probability of criminal behavior given that comparatively less
research has been devoted to the neurotic type in comparison to the aggressive type in
corrections. Strategies to intervene earlier in the offender’s life could have a later impact.
For example, one reason why the neurotic type has an increased probability of criminal
behavior could lie with Moffitt’s theory of cumulative continuity. As reviewed in
Chapter 2, Moffitt theorizes that later adult criminals who are high on negative
emotionality and low on constraint tend to be more impulsive and undercontrolled as
children, which may influence how they conformed to school and prosocial peers (Caspi
& Silva, 1995).
In a similar vein, Caspi and Roberts (1999) outline three types of transactions and
discuss how they “play particularly important roles both in promoting the continuity of
personality across the life course and in controlling the trajectory of the life course itself”
(p. 313). The three types of interactions are as follows (Caspi & Roberts, 1999, p. 314):
Reactive transactions occur when different individuals exposed to the same environment experience it, interpret it, and react to it differently. Evocative transactions occur when an individual’s personality evokes distinctive responses from others. Proactive transactions occur when individuals select or create environments of this own.
The basic assumption in all of these models is that individuals play a role in creating their
own environment. Researchers argue that people have a hand in promoting the
continuities in their life by the environments they select themselves into (Bandura, 1999;
Bandura (1999) theorizes, “through selection and construction of environments,
personality patterns can become self-perpetuating” (p 164). McCrae and Costa (1999)
also discuss how interactions with the environment could serve to strengthen certain
personality traits. For example, they argue that an individual’s personality can influence
their preferences both for friends and situations, their ideologies about how the world
works, and their own social roles. In effect, these traits influence the situations they
choose to become involved in, the friends they seek out, and their subsequent behaviors.
The personality and environment are mutually reinforcing.
Research, however, also points to the impact that parents can have in childhood
toward reducing the later impact of personality on behavior. Caspi and Roberts (1999)
argue this point and review an example regarding behaviorally inhibited children:
Behaviorally inhibited children experience greater levels of distress at lower thresholds when confronted with novel situations….. Parents who expose their inhibited children to novelty and provide firm and consistent limits and do not overprotect their children from novel situations may help children overcome behavioral inhibition. In contrast, many parents respond to their child’s distress in novel situations by rewarding the child for avoiding these situations in the future. The reinforcement of these avoidance behaviors inadvertently promotes continued behavioral inhibition and may increase the likelihood that the child grows up to become an inhibited adult. (p. 316)
Drawing from this same perspective, research indicates that individuals high in negative
affectivity avoid challenging situations, dwell on frustration, and have a lower threshold
Bolger & Zuckerman, 1995). It could be argued that neurotics often act out as they have
difficulty controlling their emotional state, establishing boundaries, and so on. Similarly,
according to Andrews and Bonta (1998), “the aggressive personality has learned to
124
interpret a wide variety of persons and situations as threatening and frustrating, and has
learned habits of aggression to these cues” (p. 123).
Moreover, the experience of a negative event can strengthen the expectation that
the world is a negative place. This is echoed by the work of Warren (1983) as she argues
that the neurotics often do not communicate well with other family members and feel
they are not meeting expectations. Related to this, McHale (undated) finds that the
neurotic type tends to perceive their family as chaotic and believes that they play a
significant role in the development of this chaos. Moreover, he theorizes that the
neurosis begins as a result of feelings of rejection and insecurity. The parents, according
to McHale, tend to reinforce the negative thoughts and feelings of the individual, which
fosters feelings of guilt and inadequacy. As a result, the neurotic become more proficient
at being negative over time and also becomes more proficient at making excuses, getting
drugs, committing crime, etc. In addition, Eysenck (1992) argues that “with anxious
individuals, their attentional and interpretive biases may lead them to regard other
people’s social behavior towards them as more threatening than it actually is. As a
consequence they may behave aggressively or defensively or in other ways which
actually elicits threatening behavior from others” (p. 162).
However, if the environment of a neurotic person has clear expectations, good
role models, and teaches the individual strategies for control, the change in personality is
possible. In fact, research by Warren (1983) found certain factors seemed to predict
whether neurotic youth did well in the Community Treatment Project. They include:
disengagement from family problems, the availability of a strong and caring figure, and
the improvement of the individual’s self esteem. In sum, it is clear that environmental
125
contingencies on behavior could serve to teach the individual the coping skills or sense of
coping efficacy needed to avoid negative situations or deal with them more effectively
when they do occur.
Related to the discussion of the interaction of personality and the environment, is
the relationship between personality and social support. Cullen (1994) theorizes that
social support reduces the probability of criminal involvement. Related to this,
individuals who perceive that others will be there for them should they need it experience
less stress and better psychological health. Many researchers assert that our perceived
network size and satisfaction is related to personality factors (Sarason, Pierce, & Sarason,
1990). Additionally, Vinokur, Schul and Caplan (1987) assert, “people who are disposed
to view themselves and their personal experiences in a negative light are thus
hypothesized to misperceive or underrate the social support provided to them” (p. 1138).
Hoboll and Wallfisch in examining the role of personality as a predictor of social support
ask “whether the mediating effect attributed to social support is a byproduct of
personality characteristics of persons who see themselves as successful, in control, or
who react and utilize social support in a manner different than persons who see
themselves as failures and helpless” (1984, p. 89).
As mentioned, individuals high in neuroticism were shown to be more distressed
on average than low neuroticism subjects which could be attributed to a lower threshold
for responding to stressful events (Bolger & Schilling, 1991). An individual predisposed
to negatively construe his or her environment may require much different levels of
support (Sarason et al., 1990). In one study, a negative correlation was found between
neuroticism and perceived availability of support with regards to reported or perceived
126
stress (Sarason et al., 1983). Moreover, another study suggested not only does a general
negative outlook influence our perceptions of reality but also may influence how much
support is actually obtained (Vinokur et al., 1987). Finally, a study by Russell, Booth,
Reed, and Laughlin (1997) found that the personality characteristics of extraversion and
neuroticism were related to perceived social support in both the cross sectional and
longitudinal analyses. In sum, certain personality types are likely to impact treatment
certain treatment strategies, reactions to sanctions such as incarceration, and interactions
with others. This impact should be taken into consideration when designing strategies
designed to change offending behavior.
Future Research Directions
Many of the implications outlined above should also serve as directions for future
research. A number of the questions related to personality and criminal behavior have
yet to be addressed. First, the research should investigate the process that underlines the
development and stability of personality. For example, the research should assess the
impact of cumulative continuity and personality into late adulthood. In addition, the
future studies should explore whether criminal-prone personalities interact with negative
social environments differently than types predicted to behave more prosocially.
Second, the impact of personality on assessment results and treatment strategies
should be further explored. Specifically, research should explore personality and its
relationship to predicting criminal behavior with different age, gender, and racial groups.
Arguably, empirical literature on assessment for these groups would provide the
foundation for the development of risk assessment technology. Similarly, research
should be conducted to develop and test treatment strategies to deal with highly neurotic
127
subjects. Testing the effectiveness of interventions with a variety of populations will
begin to unravel which treatment strategies work best with different individuals.
Third, race was significantly related to outcome in the current study. However,
sample size precluded an interaction analysis of race and personality. As with any
research involving race, the research is limited and highly controversial. Currently, there
is very little research to support the idea that racial differences in personality traits exist.
Even so, future research should explore the impact of race and personality as it relates to
criminal behavior. For example, research is still needed to determine whether we can
expect to find differences in personality by race within criminal populations.
In conclusion, it is anticipated that this study will provide a framework for further
research in an area relatively untapped. Hopefully, by exploring the impact of
personality on behavior and the effectiveness of correctional interventions, we will move
the field of corrections one step closer to becoming an effective agent in the rehabilitation
of offenders. As poignantly suggested by Costa and McCrae (1999), “people are neither
passive victims of their life circumstances nor empty organisms programmed by histories
of reinforcement. Personality is actively involved in shaping people’s lives” (p. 142).
Without exploring the underlying causes of behavior and understanding how those
factors impact treatment, the field will likely fall victim to another swing in the pendulum
towards more punitive and retributive polices.
128
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APPENDIX A
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Table 1. Percentage and frequency distribution of inmates’ social demographic characteristics _______________________________________________________________________ Original Current
Sample Sample Characteristic N % N % (N = 369) (N = 277) ______________________________________________________________________ Race White 240 64.5 184 66.4 Black 105 28.6 77 27.8 American Indian 6 1.6 6 2.2 Hispanic 13 3.5 8 2.9 Asian 3 0.8 2 0.7 Age at admission 19 to 29 111 30.6 90 32.7 30 to 45 198 54.5 154 56.0 46 and older 54 14.9 31 11.3 Marital Status Married 145 40.2 109 39.9 Never married 85 23.5 66 23.1 Divorced 80 22.2 66 23.1 Separated 21 5.8 16 5.9 Widowed 4 1.1 1 0.4 Common-law 25 6.9 21 7.7 Other 1 0.3 0 0.0 Number of Dependents None 136 37.8 98 36.3 One 78 21.7 57 27.1 Two 52 14.4 44 16.3 Three 57 15.8 42 15.6 Four 23 6.4 17 6.3 Five 7 1.9 6 2.2 Six or more 6 1.6 6 2.8 Education 6 to 11 years 128 35.3 91 33.3 High school 108 29.8 81 29.7 GED 52 14.3 46 16.8 Some post high school 43 11.8 31 11.4 College graduate 26 7.2 20 7.3 Some post college 6 1.7 4 1.5
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Table 1. Percentage and frequency distribution of inmates’ social demographic characteristics ________________________________________________________________________ Original Current
Sample Sample Characteristic N % N % (N = 369) (N = 277) ________________________________________________________________________ Evidence of school failure Yes 165 46.6 125 47.2 No 189 53.4 140 52.8 Primary Occupation No occupation 43 12.0 26 9.6 Professional 30 8.4 19 7.0 Manager/Admin 39 10.9 27 10.0 Sales 18 5.0 13 4.8 Clerical 4 1.1 2 0.7 Craftsman 37 10.3 29 10.7 Equipment Operator 12 3.4 9 0.4 Laborer 127 35.4 105 38.9 Farmer 6 1.7 6 2.2 Service worker 3 0.8 2 0.7 Armed Services 3 0.8 3 1.1 Student 2 0.6 2 0.7 Househusband 2 0.6 2 0.8 Criminal occupation 31 8.6 24 8.9 Employment Status Not working 170 47.8 127 47.0 Full time 133 37.4 100 37.0 Occasionally 40 11.2 33 12.2 Status Unknown 13 3.7 10 3.7
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Table 2. Percentage and frequency distribution of inmates’ prior and current offense record. ________________________________________________________________________ Original Current
Sample Sample Characteristic N % N % (N = 369) (N = 277) ________________________________________________________________________ Prior adult or juvenile record Yes 311 84.7 242 87.4 No 56 15.3 35 12.6 Prior adult arrests Yes 309 84.2 240 86.6 No 58 15.8 37 13.4 Prior adult convictions Yes 280 76.5 220 79.7 No 85 23.2 55 19.9 Conflicting evidence 1 0.3 1 0.4 Prior prison sentence Yes 146 47.4 108 44.8 No 162 52.6 133 55.2 Amount of prior time served Two years or less 52 29.1 42 30.4 25 mo. to 5 years 42 23.5 33 23.9 61 mo. to 10 years 23 12.8 13 8.7 More than 10 years 10 5.6 7 5.1 Time served unknown 52 29.1 44 31.9 Prior arrest dealing drugs Yes 86 28.0 66 27.6 No 221 72.0 173 72.4 Prior professional criminal activity Yes 85 27.8 65 27.2 No 221 72.2 174 72.8 Prior violence Yes 130 42.8 104 43.7 No 174 57.2 134 56.3
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Table 2. Percentage and frequency distribution of inmates’ prior and current offense record. ________________________________________________________________________ Original Current
Sample Sample Characteristic N % N % (N = 369) (N = 277) ________________________________________________________________________Prior arrest for same offense As present offense Yes 77 24.9 60 25.0 No 232 75.1 180 75.0 Prior or current drug related offense Yes, prior 31 8.7 26 9.7 Yes, current 54 15.2 42 15.7 Yes, both 61 17.1 47 17.5 No 210 56.9 153 57.1 Prior or current alcohol offense Yes, prior 67 18.7 54 19.8 Yes, current 14 3.9 11 4.0 Yes, both 30 8.4 21 7.7 No 248 69.1 187 68.5 Prior prison escapes Yes 30 16.8 22 16.2 No 149 83.2 114 83.8 Prior probation revocations Yes 65 36.7 50 35.0 No 110 63.3 89 65.0 Prior parole revocations Yes 46 41.1 36 10.5 No 66 58.9 45 55.6 Prior record as a juvenile Yes 101 29.3 81 30.5 No 244 70.7 185 69.5 Prior incarceration as a juvenile Yes 55 59.1 43 58.1 No 38 40.9 31 41.9
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Table 2. Percentage and frequency distribution of inmates’ prior and current offense record. ________________________________________________________________________ Original Current
Sample Sample Characteristic N % N % (N = 369) (N = 277) ________________________________________________________________________ Prior violent record as juvenile Yes 28 30.4 19 25.7 No 64 69.6 55 74.3
146
APPENDIX B
147
Table 1. Model 1 Logistic Regression: Probability of Rearrest by Personality & Control Variables Parameter Standardized Odds Ratio Estimate Estimate Group .221 .216 1.247 Race .489** .197 1.631
SFS Risk Poor 1.452 .373 4.273
Fair .673 .292 1.959 Good .495 .259 1.641 Time Year 2 .024 .354 1.024 Year 3 -.240 .388 .787 Year 4 .328 .346 1.388 Year 5 .337 .356 1.401 Year 6 .410 .363 1.506 Year 7 .185 .391 1.203 Year 8 -.148 .442 .863 Year 9 -1.080 .635 .340 Year 10 .319 .488 .727 Year 11 .324 .416 1.383 Year 12 -.029 .647 .971 Personality Aggressive .630* .291 1.877 Neurotic .879* .328 2.409 Situational .439 .361 1.551 Intercept -4.081*** Model Chi Square 59.077*** * p <.05 ** p <.01 *** p <.001
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Table 2. Model 1 Logistic Regression: Probability of Rearrest by Personality & Control Variables Parameter Standardized Odds Ratio Estimate Estimate SFS Riska .559** .188 1.749 Raceb .636*** .185 1.888 Personalityc Dependent -.610** .281 .543 Neurotic .229 .230 1.348 Situational -.127 .273 .881 Intercept -3.080 Model Chi Square 33.520 * p <.05 ** p <.01 *** p <.001 a Categories: 0= low; 1= high b Categories: 0= white; 1= non-white c Aggressive type is the omitted variable
149
Table 3. Model 1 Logistic Regression: Probability of Rearrest by Personality & Control Variables Parameter Standardized Odds Ratio Estimate Estimate SFS Riska .559* .188 1.749 Raceb .636*** .185 1.888 Personalityc Aggressive -.229 .230 .742 Dependent .909* .322 .403 Situational -.426 .305 .653 Intercept -2.781 Model Chi Square 33.520 * p <.05 ** p <.01 *** p <.001 a Categories: 0= low; 1= high b Categories: 0= white; 1= non-white c Neurotic type is the omitted variable
150
Table 4. Model 1 Logistic Regression: Probability of Rearrest by Personality & Control Variables Parameter Standardized Odds Ratio Estimate Estimate SFS Riska .559** .188 1.749 Raceb .636*** .185 1.888 Personalityc Aggressive .127 .273 1.135 Neurotic .426 .305 1.531 Dependent -.483 .354 .617 Intercept -3.207 Model Chi Square 33.520 * p <.05 ** p <.01 *** p <.001 a Categories: 0= low; 1= high b Categories: 0= white; 1= non-white c Situational type is the omitted variable
151
Table 5. Model 1 Logistic Regression: Probability of Rearrest by I-Level & Control Variables Parameter Standardized Odds Ratio Estimate Estimate Groupa .385 .209 1.470 Raceb .418** .194 1.520
SFS Riskc .505* .208 1.657 Time Year 2 .022 .352 1.022 Year 3 -.251 .386 .778 Year 4 .316 .345 1.371 Year 5 .306 .355 1.357 Year 6 .354 .361 1.425 Year 7 .118 .389 1.125 Year 8 -.231 .440 .794 Year 9 -1.171 .634 .310 Year 10 -.402 .486 .669 Year 11 .219 .413 1.245 Year 12 -.196 .644 .822 Interpersonal Leveld I – 3 -.308 .378 .735 I – 4 -.054 .398 .948 Intercept -3.053*** Model Chi Square 43.795*** * p <.05 ** p <.01 *** p <.001 a Categories: 0= low; 1= high b Categories: 0= white; 1= non-white c I-2 is the omitted variable
152
Table 6. Model 1 Logistic Regression: Probability of Rearrest by I-Level & Control Variables Parameter Standardized Odds Ratio Estimate Estimate SFS Riska .494** .188 1.639 Raceb .684** .187 1.981 Interpersonal Leveld I – 3 -.280 .375 .756 I – 4 -.008 .394 .992 Intercept -2.958*** Model Chi Square 26.853*** * p <.05 ** p <.01 *** p <.001 a Categories: 0= low; 1= high b Categories: 0= white; 1= non-white c I-2 is the omitted variable
153
Table 7. Model 1 Logistic Regression: Probability of Rearrest by I-Level & Control Variables Parameter Standardized Odds Ratio Estimate Estimate SFS Riska .494** .188 1.639 Raceb .684** .187 1.981 Interpersonal Leveld I – 2 .008 .394 1.008 I – 3 -.272 .195 .762 Intercept -2.966*** Model Chi Square 26.853*** * p <.05 ** p <.01 *** p <.001 a Categories: 0= low; 1= high b Categories: 0= white; 1= non-white c I-4 is the omitted variable
154
Table 8. Model 1 Logistic Regression: Probability of Rearrest by I-Level & Control Variables Parameter Standardized Odds Ratio Estimate Estimate SFS Riska .494** .188 1.639 Raceb .684** .187 1.981 Interpersonal Leveld I – 2 .280 .375 1.323 I – 4 .272 .195 1.313 Intercept -3.238*** Model Chi Square 26.853*** * p <.05 ** p <.01 *** p <.001 a Categories: 0= low; 1= high b Categories: 0= white; 1= non-white c I-3 is the omitted variable
155
APPENDIX C
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Table 1. Model 2 Logistic Regression: Probability of Multiple Rearrest by Personality & Control Variables Parameter Standardized Odds Ratio Estimate Estimate Group .260 .139 1.297 Race .633*** .129 1.884
SFS Risk
Poor .674** .246 1.963 Fair .597** .185 1.816 Good .214 .178 1.239 Time Year 2 -.335 .174 .715 Year 3 -.892*** .218 .410 Year 4 -.695*** .217 .499 Year 5 -.726** .231 .484 Year 6 -.859*** .259 .424 Year 7 -1.083*** .299 .339 Year 8 -1.395*** .361 .248 Year 9 -1.863*** .469 .155 Year 10 -1.619*** .433 .198 Year 11 -.722** .279 .462 Year 12 -1.248* .607 .287 Personality Aggressive -.232 .160 .793 Dependent .-.548* .217 .578 Situational -.565* .219 .568 Intercept -1.989*** Model Chi Square 172.89*** * p <.05 ** p <.01 *** p <.001
157
Table 2. Model 2 Logistic Regression: Probability of Rearrest by Personality & Control Variables Parameter Standardized Odds Ratio Estimate Estimate SFS Riska .826*** .125 2.284 Raceb .649*** .126 1.914 Personalityc Aggressive .274 .181 1.316 Neurotic .483* .216 1.620 Situational .035 .241 1.036 Timed Time 2 -.319 .175 .727 Time 3 – 6 -.756*** .148 .470 Time 7 – 12 -1.197*** .179 .302 Intercept -2.493*** Model Chi Square 181.246*** * p <.05 ** p <.01 *** p <.001 a Categories: 0= low; 1= high b Categories: 0= white; 1= non-white c Dependent type is the omitted variable d Time one is the omitted variable
158
Table 3. Model 2 Logistic Regression: Probability of Rearrest by Personality & Control Variables Parameter Standardized Odds Ratio Estimate Estimate SFS Riska .826*** .125 2.284 Raceb .649*** .126 1.914 Personalityc Dependent -.274 .181 .760 Neurotic .208 .160 1.231 Situational -.239 .195 .787 Timed
Time 2 -.319 .175 .727 Time 3 – 6 -.756*** .148 .470 Time 7 – 12 -1.197*** .179 .302 Intercept -2.219*** Model Chi Square 181.246*** * p <.05 ** p <.01 *** p <.001 a Categories: 0= low; 1= high b Categories: 0= white; 1= non-white c Aggressive type is the omitted variable d Time one is the omitted variable
159
Table 4. Model 2 Logistic Regression: Probability of Rearrest by Personality & Control Variables Parameter Standardized Odds Ratio Estimate Estimate SFS Riska .826*** .125 2.284 Raceb .649*** .126 1.914 Personalityc Aggressive .239 .195 1.270 Neurotic .447* .220 1.564 Dependent -.035 .241 .965 Timed Time 2 -.319 .175 .727 Time 3 – 6 -.756*** .148 .470 Time 7 – 12 -1.197*** .179 .302 Intercept -2.458*** Model Chi Square 181.246*** * p <.05 ** p <.01 *** p <.001 a Categories: 0= low; 1= high b Categories: 0= white; 1= non-white c Situational type is the omitted variable d Time one is the omitted variable
160
Table 5. Model 2 Logistic Regression: Probability of Multiple Rearrest by I-level & Control Variables Parameter Standardized Odds Ratio Estimate Estimate Group .238 .140 1.268 Race .580*** .128 1.785
SFS Risk
Poor .697** .250 2.008 Fair .681** .186 1.977 Good .294 .176 1.342 Time Year 2 -.345* .174 .708 Year 3 -.893*** .218 .409 Year 4 -.700*** .217 .496 Year 5 -.730** .231 .482 Year 6 -.874*** .259 .417 Year 7 -1.103*** .299 .332 Year 8 -1.423*** .361 .241 Year 9 -1.891*** .469 .151 Year 10 -1.635*** .433 .195 Year 11 -.807** .279 .446 Year 12 -1.290* .607 .275 Interpersonal Level I- 2 -.102 .295 .903 I-3 -.057 .135 .945 * p <.05 ** p <.01 *** p <.001
161
Table 6. Model 2 Logistic Regression: Probability of Rearrest by Personality & Control Variables Parameter Standardized Odds Ratio Estimate Estimate SFS Riska .878*** .125 2.407 Raceb .595*** .125 1.818 Interpersonal Levelc I- 2 -.213 .290 .809 I-3 -.069 .134 .933 Timed Time 2 -.329 .175 .727
Time 3 – 6 -.759*** .148 .470 Time 7 – 12 -1.215*** .179 .302 Intercept -2.196*** Model Chi Square 181.246*** * p <.05 ** p <.01 *** p <.001 a Categories: 0= low; 1= high b Categories: 0= white; 1= non-white c I-4 is the omitted variable d Time one is the omitted variable
162
APPENDIX D
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Table 1. Model 3 Logistic Regression: Probability of Rearrest for a Drug Offense by Personality & Control Variables Parameter Standardized Odds Ratio Estimate Estimate Group .160 .408 1.173 Race .086 .310 1.090
SFS Risk Poor -.973 1.087 .378 Fair .141 .502 1.151 Good -.278 .471 .757 Time Year 2 .051 .544 1.052 Year 3 -1.193 .808 .303 Year 4 -.774 .698 .461 Year 5 -.763 .698 .466 Year 6 -1.860 1.074 .156 Year 7 -.741 .697 .477 Year 8 -1.839 1.074 .159 Year 9 -1.838 1.074 .159 Year 10 -.724 .698 .485 Year 11 -1.086 .809 .338 Year 12 -6.497 14.272 .002 Personality Aggressive -.997* .414 .369 Dependent -1.455* .665 .233 Situational -1.129* .589 .323 Intercept -2.870*** Model Chi Square 25.658 * p <.05 ** p <.01 *** p <.001
164
Table 2. Model 3 Logistic Regression: Probability of Rearrest for a Drug Offense by Personality & Control Variables Parameter Standardized Odds Ratio Estimate Estimate SFS Riska .168 .403 1.181 Raceb .101 .397 1.106 Personalityc Dependent -.491 .643 .612 Neurotic .984* .409 2.675 Situational -.138 .581 .871 Intercept -4.760*** Model Chi Square 8.494 * p <.05 ** p <.01 *** p <.001 a Categories: 0= low; 1= high b Categories: 0= white; 1= non-white c Aggressive type is the omitted variable
165
Table 3. Model 3 Logistic Regression: Probability of Rearrest for a Drug Offense by Personality & Control Variables Parameter Standardized Odds Ratio Estimate Estimate SFS Riska .179 .377 1.196 Raceb .109 .387 1.116 Personalityc Aggressive .138 .581 1.147 Neurotic 1.124* .582 3.077 Dependent -.355 .777 .701 Intercept -4.887 Model Chi Square 8.486 * p <.05 ** p <.01 *** p <.001 a Categories: 0= low; 1= high b Categories: 0= white; 1= non-white c Situational type is the omitted variable
166
Table 4. Model 3 Logistic Regression: Probability of Rearrest for a Drug Offense by Personality & Control Variables Parameter Standardized Odds Ratio Estimate Estimate SFS Riska .179 .377 1.196 Raceb .109 .387 1.116 Personalityc Aggressive .492 .642 1.636 Neurotic 1.479* .662 4.388 Situational .355 .777 1.426 Intercept -5.242*** Model Chi Square 8.486 * p <.05 ** p <.01 *** p <.001 a Categories: 0= low; 1= high b Categories: 0= white; 1= non-white c Dependentl type is the omitted variable
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Table 5. Model 3 Logistic Regression: Probability of Rearrest for a Drug Offense by Personality & Control Variables Parameter Standardized Odds Ratio Estimate Estimate Group .082 .393 1.086 Race .050 .397 1.051
SFS Risk .183 .420 1.201 Time Year 2 .032 .542 1.033 Year 3 -1.208 .807 .299 Year 4 -.788 .696 .455 Year 5 -.777 .696 .460 Year 6 -1.874 1.073 .154 Year 7 -.761 .696 .274 Year 8 -1.856 1.073 .156 Year 9 -1.849 1.073 .157 Year 10 -.734 .696 .480 Year 11 -1.106 .807 .331 Year 12 -6.521 14.424 .001 Interpersonal Level I – 2 -.129 .790 .879 I – 3 -.704 .376 .495 Intercept -3.398*** Model Chi Square 19.566 * p <.05 ** p <.01 *** p <.001
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APPENDIX E
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Table 1. Model 3 Logistic Regression: Probability of Rearrest of a Property Offense by Personality & Control Variables Parameter Standardized Odds Ratio Estimate Estimate Group -.127 .373 .881 Race .842 .346 2.322
SFS Risk Poor 1.598** .591 4.944
Fair .149 .539 1.161 Good .548 .462 1.729 Time Year 2 .434 .655 1.544 Year 3 .073 .715 1.075 Year 4 .808 .622 2.244 Year 5 .555 .655 1.741 Year 6 .415 .680 1.514 Year 7 -1.207 1.124 .299 Year 8 .204 .716 1.226 Year 9 -1.187 1.124 .305 Year 10 -1.183 1.124 .307 Year 11 -1.141 1.124 .319 Year 12 -5.828 14.671 .003 Personality Aggressive -.016 .429 .984 Dependent -.470 .569 .625 Situational -.536 .621 .585 Intercept -4.803*** Model Chi Square 36.716** * p <.05 ** p <.01 *** p <.001
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Table 2. Logistic Regression: Probability of Rearrest of a Property Offense by I-Level & Control Variables Parameter Standardized Odds Ratio Estimate Estimate Group .086 .359 1.089 Race .760 .342 2.137
SFS Risk .155 .366 1.168 Time Year 2 .438 .652 1.549 Year 3 .061 .713 1.063 Year 4 .790 .620 2.203 Year 5 .524 .653 1.689 Year 6 .370 .678 1.448 Year 7 -1.245 1.122 .288 Year 8 .159 .714 1.172 Year 9 -1.222 1.122 .295 Year 10 -1.219 1.122 .295 Year 11 -1.185 1.122 .306 Year 12 -5.931 14.851 .003 Interpersonal Level I – 2 .055 .795 1.057 I – 3 .067 .365 1.070 Intercept -4.700*** Model Chi Square 26.829* * p <.05 ** p <.01 *** p <.001
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APPENDIX F
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Table 1. Model 3 Logistic Regression: Probability of Rearrest of Violent Offense by Personality & Control Variables Parameter Standardized Odds Ratio Estimate Estimate Group .578 .529 1.783 Race .393 4.65 1.482
SFS Risk Poor 1.649 .971 5.110 Fair 1.701 .840 5.479 Good 1.035 .837 2.814 Time Year 2 .019 1.006 1.020 Year 3 .031 1.006 1.031 Year 4 .039 1.006 1.039 Year 5 -.642 1.230 .526 Year 6 .775 .874 2.170 Year 7 1.022 .845 2.779 Year 8 -.581 1.231 .559 Year 9 .135 1.007 1.144 Year 10 -.537 1.231 .585 Year 11 .601 .921 1.824 Year 12 -5.159 13.958 .006 Personality Aggressive -.128 .599 .879 Dependent .409 .709 1.505 Situational -.469 .876 .626 Intercept -6.606*** Model Chi Square 27.586 * p <.05 ** p <.01 *** p <.001
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Table 2. Logistic Regression: Probability of Rearrest of a Violent Offense by I-Level & Control Variables Parameter Standardized Odds Ratio Estimate Estimate Group .755 .525 2.127 Race .519 .438 1.680
SFS Risk .864 .477 2.372 Time Year 2 .015 1.006 1.015 Year 3 .026 1.006 1.026 Year 4 .037 1.006 1.037 Year 5 -.648 1.230 .523 Year 6 .769 .874 2.158 Year 7 1.014 .845 2.757 Year 8 -.585 1.230 .557 Year 9 .127 1.007 1.135 Year 10 -.541 1.230 .582 Year 11 .590 .920 1.804 Year 12 -5.184 14.096 .006 Interpersonal Level I – 2 .508 .859 1.662 I – 3 .166 .503 1.181 Intercept -6.201*** Model Chi Square 24.817 * p <.05 ** p <.01 *** p <.001