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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

Committee Chair: Patricia Van Voorhis

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TABLE OF CONTENTS

TABLE OF CONTENTS………………………………………………………………….1

TABLE OF TABLES………………….…………………………………………………..4

TABLE OF FIGURES………………….…………………………………………………6

CHAPTER 1: INTRODUCTION………..…………………………………………….…7

METHOD ......................................................................................................................... .9

CHAPTER 2: LITERATURE REVIEW.………………………………………………11

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

CONCLUSIONS................................................................................................................ 60

CHAPTER 3: METHOD…...……………………………………………………………61

METHOD………………………………………………………………………………….61

Design ....................................................................................................................... 61 Sample....................................................................................................................... 61

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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

Variables predicting Violent Offenses………………………………………………108

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TABLE OF FIGURES

Figure 1. Rearrest rates by personality type………………………….………………….….88 Figure 2. Rearrest rates by personality type: Low risk…..……………………………….91 Figure 3. Rearrest rates by personality type: High risk….....…………………………….92 Figure 4. Personality predicting multiple rearrest………..………………………………94 Figure 5. Rearrest rate by personality type: Drug offenses..……...……………………...97 Figure 6. Rearrest rate by I-level type: Drug offenses…….……………………………100 Figure 7. Rearrest rate by personality type: Property offenses.………………………...102 Figure 8. Rearrest rate by personality type: Violent offenses……..……………………...105

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CHAPTER 1

Introduction

Understanding why crime occurs requires an appreciation for the complexity of

human behavior. Behavior is not determined by one factor, but rather influenced by a

host of interrelated factors (Bandura, Reese, & Adams, 1982). For example, an

individual’s reaction to losing his or her job may vary according to factors such as age,

coping skills, personality, future job opportunities, level of social support, or financial

status. Thus, both individual and structural level factors are essential to explaining

current behavior and to predicting future behavior. Related to this, the field of

criminology has intended to be interdisciplinary in nature and should logically support

the inclusion of literature from other disciplines. One such discipline that is particularly

relevant is psychology. However, some scholars assert that criminologists often fail to

consider psychological variables in their research (Andrews & Bonta, 1998; Andrews &

Wormith, 1988).

One glaring omission in this regard is criminology’s refusal to acknowledge well-

established findings showing a relationship between personality and crime (Andrews and

Wormith, 1989). Until the recent inclusion of personality as a key factor in social

learning (Andrews and Bonta, 1998) and several life course paradigms (see Caspi,

Moffitt, Silva, Stouthamer-Loeber, Kreuger, & Schmutte, 1994) interest in personality

and crime was primarily confined to corrections. Most notably during the 1960s and

1970s, Quay and Parsons (1972), Edwin Megargee (1983; 1984), Marguerite Warren

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(1966) and Carl Jesness (1964) created a number of psychological typologies designed to

differentiate offenders for correctional treatment purposes. A number of studies find the

personality types identified by those typologies to be relevant to offender behavior (Van

Voorhis, 1994; Heide, 1982; and Jesness, 1988). More recently, correctional scholars

(see Andrews & Bonta, 1998) identify personality as a “responsivity” consideration,

which recognizes that individual characteristics may influence the success of correctional

programming.

Notwithstanding the important findings from the classification research, “few

studies have used classification models as measures of personality, developmental, or

behavioral contributors to models of crime causation” (Van Voorhis, 1994, p. 8). This is

unfortunate because a number of the types identified by the correctional typologies will

be shown to mirror those scales utilized by criminologists such as Eysenck (1996) and

Caspi et al. (1994).

Drawing from several paradigms, the present study explores the relationship

between one correctional typology, the Jesness Inventory, and long-term recidivism. In

doing so, the correctional paradigm is moved into the broader field of criminology to

assess its utility to explain, not simply correctional adjustment, but criminal behavior.

Moreover, a relationship between types identified by the Jesness Inventory and

recidivism supports its use as a measure of the constructs paralleled by the above-

mentioned criminologists. As outlined in the lifecourse and developmental research, the

study will also explore whether certain personality types are related to persistent criminal

behavior.

Finally, this study utilizes long term recidivism data to explore the full extent or

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potential of the Jesness Inventory in predicting criminal behavior. It is theorized that if

the personality subtypes defined by the Jesness Inventory are useful in predicting

recidivism, the research will imply that personality should be added to existing risk

assessment models to increase their predictive power. As a result, the current research

will address both applied and theoretical issues. To date, no one has taken these two areas

of research and bridged the gap to develop a better understanding of criminal behavior

and to improve predictions of its likelihood.

Method

The current study extends a previous classification study conducted at a federal

penitentiary and prison camp in Terre Haute, Indiana between 1986 and 1988. The

National Institute of Justice (NIJ) funded study1, conducted by Patricia Van Voorhis,

accumulated extensive intake data on federal prison inmates. The original sample

includes 179 federal penitentiary inmates and 190 prison camp inmates. This intake

cohort is one of very few samples to contain personality-based classification measures of

adult male offenders. The research approach for the proposed study builds from the

design of the initial classification study. Given the sample is of federal inmates,

recidivism data includes nationwide arrest statistics by individuals. These data were

collected via the National Crime Information Center (NCIC) database by a pretrial

services center in a midwestern county. The following dependent measures will be

constructed: (a) time at risk to first offense, (b) time at risk to each offense (for those

arrested multiple times), and (c) nature or seriousness of the offense.

Preparing the data in this manner will facilitate a recidivism analysis that accounts

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for time to first offense, the frequency and rate of offending, and the nature of the

offense. The data will afford the opportunity to employ bivariate and multivariate

analyses. Event history will be used to study the probability of arrest. By using this

technique, the study will distinguish whether those who fail (i.e., incur an arrest) differ by

personality type while controlling for time at risk.

In conclusion, using the Jesness Inventory and other data from this sample of

federal inmates, the proposed study endeavors to document the relationship between

personality and criminal behavior. If personality types set forth by the Jesness Inventory

are useful in predicting recidivism, they may also be useful in theory development, risk

assessment technology, and understanding the adult male offender. The following

research questions will be addressed:

(1) Are certain personality subtypes related to criminal recidivism? And if so,

which ones?

(2) Do certain personality subtypes predict persistence? And if so, which

ones?

(3) Can personality subtypes be used to predict the nature of the offense?

(4) Will the relationships found in the empirical literature on personality and

crime also be found in the current study limited to federal inmates?

(5) Can the Jesness Inventory be used to predict criminal behavior over time?

(6) Should personality be added to existing risk assessment models to further

increase the predictive power of more traditional predictors of recidivism

(i.e. age, prior record, and substance abuse)?

1 Grant Number 85-IJ-CX-0063

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CHAPTER 2

Literature Review

This chapter reviews the theoretical groundwork and empirical findings regarding

personality and crime. A substantial amount of research is examined to discern the

following: (a) how the construct of personality has developed over time in the field of

psychology, (b) why psychology was neglected in criminology theory, discourse, and

research, (c) what conclusions cab be drawn from the current crime and personality

literature, (d) how personality fits into current theories such as self control, lifecourse,

and developmental criminology, (e) how personality is used in correctional typologies to

differentiate offenders for treatment, and (f) whether personality can be used to predict

future criminal behavior.

Finding Value in Individual Theories

Definition of an Elusive Concept

Gestalt psychologists maintain that in order to understand the whole, you must

take into consideration the sum of its parts. To understand individual behavior, a number

of factors such as personality must be considered. Personality researchers are concerned

with providing an account of how individuals differ from one another in their behavior

(Derlega, Winstead, & Jones, 1989). The study of personality focuses on the stable and

enduring characteristics influence emotion, motivation, and behavior. That is not to say

that all behavior is stable and not subject to external influences, rather that “human

behavior can change over time and that an individual’s behavior is often influenced by

situational factors. But it is still possible to speak of a set of personality characteristics

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that organize behavior” (Derlega, et al, 1989, p. 4). Those sets of characteristics, then,

could also be used to explain criminal behavior.

Defining exactly what personality is, however, remains somewhat problematic.

Some psychologists theorize that individual definitions are a reflection of the researcher’s

bias rather than a true reflection of the construct (Allport, 1937; Hall & Lindzey, 1957

Murray, 1938; Eysenck, 1983). In fact, in the 1930s and 1940s, a time in which the study

of personality was gaining prominence, one author recorded over 50 definitions of the

construct (Allport, 1937). However, for the purpose of this study, personality will be

defined as follows: “the dynamic organization within the individual of those

psychophysical systems that determine his (or her) unique adjustments to his (her)

environments” (Allport, 1937, p.48). Personality is seen as evolving and dynamic,

determining actions; as every person is unique but also shares similar characteristics

(Allport, 1937).

Psychological Foundations of Personality

It is important to mention that although the early research on personality was not

concerned with criminal behavior, per se, its focus on explaining general behavior

remains applicable. A brief review of the historical development of the construct and the

varied ways in which it is measured are essential to defending its place in criminological

discourse.

Personality research originated in the 1930s with the work of Gordon Allport and

Henry Murray (Pervin, 1990). Although the study of personality developed throughout

the decade, Gordon Allport’s 1939 work, ‘Personality: A Psychological Interpretation,’

lent clear definition to this emerging field. Allport criticized psychologists for

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consistently ignoring the individual for a more generalized study of human behavior. By

rejecting the view that all behavior is composed of simple innate habits, Allport defined

personality. The author introduced the concept that personality guides behavior and

decisions as people interact with the environment (Pervin, 1990).

Allport recognized that personality developed early in life and was influenced by

external factors in the environment. Personality, according to Allport, emerges around

four months of age, and develops continually while consistently influencing behavior.

Unlike criminologists such as Gottfredson and Hirschi (1990), however, Allport notes

that the consistency of personality at four months of age should not be taken to imply that

personality is fixed during the first year of life; rather later circumstances or interventions

can have a tremendous impact (Allport, 1937).

The importance of Allport’s book at this time was paralleled by the work of

Henry Murray (Pervin, 1990). Murray (1938) concurred with Allport and claimed that

psychologists have ignored the individual in their analyses. Murray advocated for

uncovering the determinants of single behavioral events. He viewed the individual as

consisting of an “infinitely complex series of temporally related activities extending from

birth to death” (p.39). Murray (1938) sought an understanding of the consistency of

personality while also recognizing that it is subject to maturation. That is, that individuals

display a tendency to react consistently but that fluctuations may occur.

Personality Trait Measurement

The study of personality gained prominence throughout the 1930s and 1940s,

however, one problem persisted: the measurement of personality traits. Strictly speaking,

no two individuals have an identical organization of personality traits, but rather develop

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comparable characteristics that are manifested differently (Allport, 1938). Assuming

individual personality traits can be labeled, determining which labels to use was a

complex task. For example, Allport (1937) found there are “approximately 18,000 terms

(chiefly adjectives) in the English language designating distinctive and personal forms of

behavior” (p. 304). Within these groups’ of adjectives, it is theorized that some represent

major or cardinal traits and other minor or secondary traits (Allport, 1937). The field of

psychology needed a taxonomy that would permit researchers to study and replicate

personality traits across individuals instead of studying thousands of adjectives (John,

1990).

The work of Cattell (1950) set the stage for the measurement of personality

variables. Cattell criticized psychologists for jumping into personality research without

thoroughly dealing with the task of objectively describing and measuring its components.

The author argued that psychologists relied on the clinical guesswork in their efforts to

determine personality structure (Cattell, 1965). As a result, factor analysis gained

popularity as a way to develop a taxonomy of personality. The procedure involves

“present(ing) hundreds-in the long run thousands- of carefully chosen items to large

groups of normal persons. Each item is then correlated with every other to see which sets

‘go together’” (Cattell, 1965, p. 70). The factor analytic approach can be used to compile

traits with the recognition “that there are natural unitary structures in personality and that

it is these traits, rather than the endless labels in the dictionary, on which we should

concentrate” (Cattell, 1965, p. 55).

Once factor analytic procedures produced personality factors, psychologists began

constructing questionnaires. In addition, both World War I and World War II led to the

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need for mass standardized testing of soldiers. This need combined with the inception of

graduate programs in clinical psychology acted as a catalyst to the development of the

standardized personality questionnaire. As such, the development and widespread use of

the MMPI (Hathaway & Meehl, 1951), a paper and pencil instrument consisting of 566

statements, would become the framework for many of the personality tests to emerge

(Pervin, 1990). Unfortunately the widespread expansion of personality tests also fueled a

paradigm crisis.

A Paradigm Crisis

This rapidly developing branch of psychology did not escape the 1950s and 60s

without difficulties. In fact, the study of personality all but disappeared during the 1960s

(Pervin, 1980). Negative research findings fueled the eventual shift away from

personality research. Specifically, studies testing the reliability and validity of personality

scales were not meeting with success. Moreover, there still was no agreed upon definition

of personality, researchers were using a variety of instruments that were plagued by

response bias, and many studies were breaking down under replication (Pervin, 1980).

These factors led many to question the utility of assessing such an abstract construct.

Furthering the crisis, Mischel critiqued traditional psychology in support of the

situational perspective (Pervin, 1990). Proponents of the situational perspective

advocated a shift from emphasizing traits to exploring how individuals cognitively

perceive and respond to the environment (Mischel, 1990).

The situational perspective was seen as important throughout the 1970s, with one

notable exception: the development of the interactional perspective. Researchers began

arguing that the trait and situational approaches need not be at odds with one another

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(Epstein & O’Brien, 1985; Kendrick & Funder, 1988; Magnusson, 1990). For example,

Epstein and O’Brien (1985) outline the interaction focus as follows “interactionism

provides a reasonable resolution to the person-situation debate as behavior can never be

determined by the person or the situation alone, but is always the result of the interaction

between them” (p. 515). In addition, Kendrick and Funder (1988) reviewed the

arguments proposed by the situationists and advocated for a more inclusive model that

viewed traits as influencing behavior indirectly by causing the individual to respond to

different environmental settings and events.

Similarly, Eysenck & Eysenck (1985) argue that individuals choose the situations

in which they find themselves; therefore, situations are the consequence of an already

existing system of perceptions and attitudes influenced by personality traits. Magnusson

(1990) concurred and argued that past experiences influence current thought and shape

the individual; however, the individual’s personality influences and paints the situations

he or she will experience. Related to this was the development of Albert Bandura’s

social learning theory (Pervin, 1990).

Social learning theories embrace the complexity of this perspective insisting that

individual behavior is the result of the interaction between the individual’s prior learning

and the immediate environment. From this socio-cognitive perspective, learning and

behavior are driven by and individual’s perception of the world and their personality

experiences. Individual behavior and reactions emerge as the result of both mimicking

the behavior of others and from past behaviors that were reinforced (Bandura, 1989). As

such, features of the environment that include interactions with others both facilitate and

inhibit behavior (Andrews & Bonta, 1998). Social learning theories delineate the

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importance of environmental stimuli while not discounting the importance of individual

traits2 (Bandura, 1989; Bierman & Furman, 1984; Farrington, 1994; and Moffitt, 1993).

In a similar vein, self-efficacy beliefs are theorized to affect thought patterns and

influence motivation. That is, those with high-perceived self-efficacy were more likely to

set higher goals and view themselves as competent and capable (Bandura, 1989). Self-

efficacy becomes increasingly important as we explore the literature with regard to

personality. Bandura (1989) theorizes that “people possess self-directive capabilities that

enable them to exercise some control over their thoughts, feelings, and actions by the

consequences they produce for themselves” (p. 1179).

State versus Trait

Throughout this discussion, personality has been portrayed as relatively stable and

enduring. However, some critics of personality theory often assert that traits are merely

states of mind or moods that constantly fluctuate (Mischel, 1968). Specifically, the

construct of neuroticism is questioned as a state of mind and not a stable or enduring

personality trait. Neuroticism has been defined as “a mood dispositional dimension that

reflects the pervasive individual differences in negative emotionality and self concept”

(Brief, Burke, George, Robinson, & Websert, 1988). Although many would agree that

anxiety can exist as a state of mind, others argue for its consistency across time and

situations (Tellegen, 1985).

Researchers have found that anxiety can exist both as a state of mind and as a

personality trait. Studies conclude that individuals higher in trait anxiety are consistently

more prone to perceive greater danger in their relationships and respond with greater

2 Yet early social learning theorists did not specifically focus on personality.

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elevations of situational or state anxiety (Speilberger, 1985). 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). Moreover, individuals high in neuroticism were shown to be

more distressed on average in comparison to low neuroticism subjects. This difference

could be attributed to a lower threshold for responding to stressful events among highly

neurotic subjects (Bolger & Schillings, 1991). Although some individuals may

experience these feelings as a state of mind during times of stress, those high in negative

affectivity manifest these feelings even in the absence of stress (Watson & Clark, 1984).

In sum, the research concludes that personality traits, including neuroticism, are enduring

and consistently affect behavior.

Summary

Personality research emerged in the 1930s and research continues to the present.

Although the feasibility of measuring personality has been questioned in the past, the

development of increasingly reliable tools now provides support for further research.

Moreover, factor analysis has proven to be a reliable tool in the construction of

personality scales and data support the validity and reliability of recent personality

inventories (Church, 1994; McCrae & John, 1992; & Tellegen, 1985). Although the

development of personality research did not escape the scrutiny of situationists, many

psychologists embrace a more tempered view of personality. That is, individual’s react

with the environment but also that individual’s choose the situations in which they find

themselves and the situations are the consequence of an already existing system of

perceptions and attitudes influenced by personality traits (Eysenck and Eysenck, 1985).

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A review of modern personality texts will reveal that there are many areas

relevant to explaining individual behavior. Only a few have been discussed, however, the

hope is that this review will provide a historical framing, albeit brief, of how personality

has evolved over the last seventy years. Furthermore, in the field of criminology,

personality and crime causation studies are supporting the idea that personality is a

worthwhile area of inquiry. The focus of the following sections will now shift to the

review of how personality influenced the field of criminology.

Theoretical and Empirical Context of the Current Study

During the 1930s when the field of psychology was adopting personality as an

important factor in the explanation of individual behavior, criminology shifted away from

individual- centered explanations. As a result, the criminological literature has been

dominated by sociological theories for the majority of the twentieth century. Science

traditionally values knowledge based in theory that is supported by data and empirical

findings. However, with the exception of a few researchers such as Eysenck (1964),

studies since the 1950s regarding personality and crime have been limited. For example,

Stitt and Giacopassi (1992) concluded that one of the leading journals in the field,

Criminology, has only published four articles on the link between personality and crime

since its inception. Moreover, a review of Criminology journal issues from 1990 to 2000,

conducted by the author, found only three additional personality articles3. There are a

number of justifications cited as to why the field has been dominated by more

3 Self control articles were excluded given the authors argue that self-control is not a psychological construct.

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environmental causes of crime.

Grounds for Excluding the Individual

For a number of reasons, explanations of crime have drawn heavily from

sociology and only tangentially from other disciplines such as psychology and biology.

The lack of individual-centered theories and research was justified by some on moral

grounds. That is, psychological research was discounted in an effort to protect the

individual from being labeled different and confined or segregated based on these

differences (Andrews & Bonta, 1998; Andrews & Wormith, 1989; and Hirschi &

Hindeland, 1977). Psychological theories were mentioned in the context of Lombroso’s

(1911) or Hooten’s (1939) work pertaining to physical inferiority. This type of research

did in fact arm some researchers in the early 1900s with evidence to support their own

personal biases (Gould, 1980); however, it did not itself negate the existence of

individual differences. As a result of the fear of abuse, criminological research moved

away from the individual and toward structural explanations of behavior (Andrews &

Bonta, 1998, & Lilly, Cullen, & Ball, 1994). However, as pointed out by Andrews and

Wormith (1989) “although this type of moral reasoning is appreciated in view of some

abuse in the early history of correctional treatment, it is obviously suspect on more

general grounds” (p. 290).

Other than protecting the offender, psychological considerations were ignored on

professional grounds. Specifically, Andrews & Wormith (1989) point out,

“professionally, many sociologists discounted psychological contributions in order to

promote the importance of their favored variables of political economy, social location,

social reaction, and inequality in the distribution of societal resources” (p. 291). This

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trend is seen in three popular reviews of the psychological literature (See e.g.; Schuessler

& Cressey, 1950; Tennenbaum, 1977; and Waldo & Dinitz, 1967). The studies all found

differences in the personalities of offenders and non-offenders even when “only four of

the 30 different personality tests employed in the early studies were able to meet the

psychometric standards of the 1960s” (Andrews and Wormith, p. 300, 1989)

In another example of this trend, Vold and Bernard (1986) acknowledge a link

between personality and crime but as pointed out by Andrews and Wormith (1989),

dismissed the link as providing no theoretical relevance to understanding criminal

behavior. Interestingly, these reviews were met with resounding support and uncritical

acceptance (Hirschi and Hindelang, 1977). Hence, it appears the bias against theories of

personality and crime had less to do with protecting the disadvantaged and more to do

with protecting sociological criminology.

A third factor related to what has been termed “anti-individual bias,” (Andrews &

Wormith, 1989) is the effect of the political crisis during the 1960s and 1970s. Most

often the anti-individual bias is discussed in relation to the demise of rehabilitation (See

e.g., Andrews, Zinger, Hoge, Bonta, & Cullen, 1990; Cullen & Gendreau, 1989; Cullen

& Gilbert, 1982; Gendreau & Ross, 1987; and Martinson, 1974). During this period

conservative politics and the decline in support for treatment at the “hands of the state”

lent support for the development of social or environment-focused policies to reduce

crime. As a result, it became popular to discount the individual in discussions of

rehabilitation or theory development.

A final reason offered by some researchers as an explanation of anti-individual

bias, is the death of positivism and a rise in theoreticism (Andrews & Bonta, 1998;

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Andrews & Wormith, 1989). Andrews and Bonta (1998) define theoreticism to refer to

theorists who “adopt and discard knowledge insofar as it is personally or politically

rewarding to do so” (Andrews & Bonta, 1998, p. 28.). One can argue, individual-

centered theories were discarded not due to evidence, but rather to support more popular

and often politically correct theories of crime.

It is apparent that early reviews and current thought among some criminologists

were influenced by anti-individual bias and knowledge destruction rather than an

unbiased reporting of the quality and significance of the findings (Andrews & Bonta,

1998 & Andrews & Wormith, 1989). This review was included to illustrate the

importance of exploring reasons why certain ideologies are attacked. The following

discussion details the research and possible reasons why personality should be integrated

into a broader discussion of criminological theory.

Personality and Crime

Given the focus of criminological research, studies exploring personality and

crime have been limited. However, the existing research is extremely informative. Some

of the earliest research on personality and crime comes from the work of Hans Eysenck.

A review of the research by Eysenck, outlining his proposal for why criminal behavior

occurs, will follow.

The EPQ and Crime

According to Eysenck, in order to study personality and the impact that it may

have on behavior, a theoretical system must be developed. The system should be able to

measure traits and make distinctions based on the similarities and differences among

individuals (Eysenck & Eysenck, 1985). As Eysenck and Eysenck (1985) claim “what

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one must say, essentially, is that although human beings clearly do differ from each other,

they differ along certain dimensions, and their differences and similarities can therefore

be quantified and measured” (p. 8).

Many of the studies of personality and behavior rely on a descriptive model

containing: (a) traits and (b) types or superfactors. As stated previously, personality traits

are enduring characteristics within the individual that determine his or her behavior. A

type, however, is defined as “a group of correlated traits” (Eysenck & Eysenck, 1985).

The term type in modern personality theory is superordinate to that of a trait. The type

corresponds to what others, using factor analysis, have called second order types or

superfactors (Eysenck & Eysenck, 1985). Hence, the type or superfactor consists of

multiple individual personality traits.

Eysenck’s work emerged from his exploration of personality traits among 700

male service patients in the 1940s. Eysenck completed a large scale factorial study that

resulted in two main types: neuroticism and extroversion-introversion (Eysenck, 1947).

Later, Eysenck (1952) constructed the Eysenck Personality Questionnaire (EPQ) that

included psychoticism. The traits that compose the type neuroticism include: anxious,

depressed, guilt feelings, low self-esteem, tense, irrational, shy, moody, and emotional.

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).

24

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.

26

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

28

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

29

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,

30

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

32

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.

33

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

34

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.

35

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

36

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

37

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

38

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

39

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

40

frequently exhibiting certain personality characteristics known to be related to criminal

behavior (Farrington, Loeber, Elliott, Hawkins, Kandel, Klein, McCord, Rowe, &

Trembley, 1990).

In addition to measures of aggressive or troublesome behavior, other studies have

explored whether specific personality types are predictive of future criminal behavior.

Specifically, one study explored the temperamental qualities, some say the origins of

personality, at age three and whether they predicted personality traits among young adults

up to age 18. The study found that children labeled as undercontrolled at age three had a

behavioral style similar to low constraint, described themselves as danger seeking,

impulsive, and were prone to negative emotions at age 18 (Silva, 1990). Another study

found a link between early temperamental style and later childhood problems. Among

both boys and girls, lack of control at age three and five was consistently and positively

related to reports of antisocial behavior at age nine and eleven and conduct disorder in

adolescence (Caspi, Henry, McGee, Moffitt, & Silva, 1995).

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

41

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

distress/psychopathology, educational/vocational achievement, parental/family factors,

temperament/personality, and antisocial attitudes and associates. The best predictors in

this study are personality and antisocial attitudes and associates. Collectively, research

indicates that the best predictors of criminal behavior, often referred to as the big four,

are antisocial associates, attitudes, personality, and criminal history (Andrews & Bonta,

1998; Gendreau, Little, and Goggin, 1996). This finding lends additional support to the

existence of a personality-crime link. Other dynamic factors considered to be major

predictors of criminal behavior include: familial factors and interpersonal relationships as

well as educational and vocational or financial achievement (Simourd and Andrews,

1994).

The second principle of effective classification refers to targeting the

criminogenic need presented by the offender in an effort to change behavior. The most

promising targets related directly to the most significant areas of risk: changing antisocial

attitudes, feelings and values, attending to skill deficiencies in the area of poor problem

46

solving skills, self management and self efficacy, and impulsivity, poor self control, and

irresponsibility (Van Voorhis, 1997).

The third principle of effective classification is responsivity. The responsivity

principle refers to delivering an intervention that is appropriate and matches “the specific

competencies, interests of learning styles that a client must posses in order to benefit

from particular types of programs” (Andrews & Bonta, 1998, p. 89). A number of

studies have found that the characteristics of the client may have an impact on treatment

(see, Andrews & Bonta, 1998). Overall, the effectiveness of correctional interventions is

dependent upon whether the program is varied based on risk, need, and responsivity

factors of the individual.

Psychological Classification Systems

In corrections, much of the work on personality and crime considers personality

as a responsivity factor. Specifically, since the 1950s, psychological classification

systems have been used to develop differential treatment and supervision plans for

offenders (Van Voorhis, 1994). This typology approach is based on the notion that

offenders are not all alike and different methods are needed to effectively deal with

different individuals (Levinson, 1988). This body of research relies on specific crime-

related personality dimensions (Andrews & Bonta, 1998). Previous research suggests

that offenders who are inappropriately placed are more likely to perform worse than those

who were appropriately placed (Palmer, 1974).

Interpersonal Maturity Level Classification. There are a number of classification

systems that could be used to distinguish personality. For the purpose of this study,

however, we will discuss Interpersonal Maturity level (I-level) classification theory and

47

its related systems. The theory was developed at University of California at Berkeley

during the 1950s (Warren, 1966). The system was originally developed to differentiate

treatment plans for juveniles under community supervision (Warren, 1966). As described

by Warren (1966) “the classification system focuses upon the ways in which the

delinquent is able to see himself and the world, especially in terms of emotions and

motivations; i.e. his ability to understand what is happening between himself and others

as well as between others” (p. 1). Specifically, the I-level theory maintains that cognitive

development involves changes in thought processes. An increase in I-level requires that

the thought processes must increase in complexity and remain stable over time. Seven

stages exist in the system and are hierarchical in nature (Warren, 1966). The levels of

interpersonal development range from least mature to a theoretically ideal stage of

interpersonal maturity rarely attained (Warren, 1966). Each level represents a stage at

which a crucial interpersonal problem must be mastered (Harris, 1988). The range of

maturity levels found in a delinquent population is from level 2 to level 5, although level

5 is infrequent.

In addition to the levels of interpersonal maturity, Warren defined nine different

personality subtypes. The types were defined for use in the Community Treatment

Project as a way to describe the juvenile population for treatment purposes. The types

included: unsocialized, aggressive/undersocialized, active (AA), unsocialized, passive

undersocialized, passive (AP), immature conformist/conformist (CFM), cultural

conformist/group-oriented (CFC), manipulator/pragmatist (MP), neurotic, acting

out/autonomy oriented (NA), neurotic, anxious introspective (NX), situational emotional

reaction/inhibited (SE), cultural identifier/adaptive (CI). According to Warren (1966) the

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“nine subtypes then were described by lists of item definitions which characterized the

manner in which each group perceived the world, responded to the world, and were

perceived by others” (p. 3).

The Jesness Inventory. The Jesness Inventory (JI) classification system is a

revised version of Warren’s the I-level system (Jesness & Wedge, 1983; Sullivan, Grant,

& Grant, 1957; Warren et al., 1966). Jesness (1964) sought to revise earlier I-level

assessment process, which involved cumbersome and time consuming interviews. The

inventory was developed to provide a personality test that was “responsive to change,

comprehensible to youths as young as 8 years, predictive of delinquency, and

multidimensional with respect to a variety of attitudes, perceptions, and traits” (Jesness,

1964, p. 3).

The Jesness inventory consists of 155 true/false questions. An initial research

pool of 250 items were reduced to eliminate items that were nondiscriminating (Jesness,

1964). In 1972, revisions were made to make the instrument more relevant to adults and

females (Jesness, 1983) and further research illustrates it applicability to adult males

(Van Voorhis, 1994). “Initial normative and validation studies were based on a sample of

970 males delinquents and 1075 male non-delinquents between the ages of 8 and 18 and

on a sample of 450 female delinquents and non-delinquents ranging in age from 11

through 18” (Jesness, 1964, p. 5). The personality types are based on the types set forth

by Warren and associates (1961). The Jesness sample, however, did not contain any

level I-5 individuals; hence, the system contains 3 I-levels (e.g., I-2 to I-4).

The JI contains 11 personality scales and 9 subtypes scales. The nine subtypes

include the same labels as defined by Warren (1961), however, the cultural identifier type

49

differs between the two systems. The subtypes contained in the Jesness5 are as follows:

unsocialized, aggressive/undersocialized, active (AA), unsocialized, passive

undersocialized, passive (AP), immature conformist/conformist (CFM), cultural

conformist/group-oriented (CFC), manipulator/pragmatist (MP), neurotic, acting

out/autonomy oriented (NA), neurotic, anxious introspective (NX), situational emotional

reaction/inhibited (SE), cultural identifier/adaptive (CI) (Jesness, 1964). Tables 4 and 5

provide a description of each I-level and type.

Although the system was developed to differentiate treatment plans for offenders,

researchers have also explored the relationship to criminal behavior. Behavior. Palmer

(1974) grouped youths into three categories based on Warren’s subtypes that includes:

5 The eleven personality scales include: social maladjustment (e.g., exhibiting attitudes

associated with inadequate or disturbed socialization), valued orientation (e.g., exhibiting

tendency to share attitudes of persons in lower socioeconomic classes), immaturity (e.g., tendency

to display attitudes consistent with persons of younger age), autism (e.g., tendency to distort

reality according to personal desires), alienation (e.g., presence of distrust in others, especially

those representing authority), manifest aggression (e.g., tendency to react in response to

frustration or anger without control), withdrawal-depression (e.g., indicates extent of individual’s

dissatisfaction with him- or herself and others, and a tendency toward isolation), social anxiety

(e.g., exhibiting feelings of anxiety in interpersonal situations), repression (e.g., exclusion of

conscious awareness of feelings or failure to label emotions), denial (e.g., reluctance to

acknowledge unpleasant events or conditions of daily life), asocial (e.g., reflects a generalized

disposition to resolve problems in a way that shows disregard for social customs or rules).

50

passive conformist (Cfm’s), power oriented (comprised of Cfc’s and Mp’s), and

Neurotics (comprised of Na’s and Nx’s) to determine if juveniles with certain profiles

should remain in an intensive treatment program in the community. He found that all

groups performed well in the community if given intensive treatment services. However,

the likelihood of criminal behavior among the power oriented and passive conformist

group increased in direct relationship to the duration of the follow-up period (Palmer,

1974).

Finally, a study by Van Voorhis (1994) among adults found that the Jesness

Inventory types could be grouped into four categories: aggressives (comprised of Aa’s,

Cfc’s, Mp’s), neurotics (comprised of Na’s and Nx’s), dependents (comprised of Ap’s

and Cfm’s) and situationals (comprised of Se’s and Ci’s). Table 6 illustrates the

description of each collapsed type. The study was designed to determine, among other

things, whether the Jesness Inventory subtypes could predict disciplinary and treatment

related outcomes. The study concluded that aggressives were more likely to be cited for

disciplinary infractions in the prison camp, report aggressive behaviors in the prison

camp and penitentiary, and be evaluated unfavorably by staff in the penitentiary (Van

Voorhis, 1994). Moreover, the neurotic type in the penitentiary scored high on a self-

report aggression scale in both a penitentiary and prison camp samples. Van Voorhis

(1994) asserts that neurotic acting out inmates showed less stress but more problems with

emotional control and learning. Moreover, 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. The findings were

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Table 4. Summary of Interpersonal maturity levels (Warren, 1966; Jesness, 1988).

______________________________________________________________________________________________________ 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

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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

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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

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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

64

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

No 55 74.3

________________________________________________________________________

69

the participants reported a prior record (either adult or juvenile) and 45 percent served at

least one prior prison sentence. Twenty-eight percent had a prior record that involved

drug dealing and 44 percent a record of violence. Prior records also involved: (a) prison

escapes (16%), (b) probation revocations (35%), (c) parole revocations (11%), (d) a

juvenile record (31%), (e) incarceration history as a juvenile (58%), and (f) violent

behavior as a juvenile (26%).

Data Collection

The data for the present study were obtained from three sources. First, social,

demographic, and criminal record background data were obtained through interviews and

the review of PSI’s by the research staff working on the original project. For the current

study, Time 1 data were utilized to create the Salient Factor Score (SFS81) to account for

risk of reoffending. Second, the Jesness Inventory was administered by research staff at

Time 1. Test results were sent to Consulting Psychologists Press in Palo Alto, California

for computerized scoring. Third, recidivism data were collected in November 1998

(Time 2) by a pretrial services agency through the use of the National Crime Information

Center (NCIC) database. As of 1998, 36 states provided criminal record data to this

national database designed to provide states with criminal history information. As of

February 1, 1998, there were 27,368,119 subjects on file.

Measurement of Research Variables

Independent Variables

A variety of independent variables or factors are explored in relationship to

recidivism. This analysis provides a measure of the overall importance of each of the

variables with specific attention paid to the relative importance of personality as a

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predictor. All of the variables and items used in the analysis have been previously

discussed in the literature as factors related to recidivism. Four independent variables are

included in the multivariate analysis.

Personality. Personality is considered one of the four main risk factors discussed

in the classification literature (see Andrews and Bonta, 1998) and as important in the

emerging criminological literature (Caspi et al., 1994; Krueger et al., 1994; 1996). The

Jesness Inventory is a paper-and-pencil test that contains 155 true-false items that yield

scores on eleven personality scales and 9 subtype scales (see description in Chapter 2).

Research for the Jesness Inventory spans over 50 years. The original sample of 1,862

used by Carl Jesness included approximately equal numbers of delinquent and non-

delinquent males ranging in age from 10 to 18. The mean odd-even item correlations

found was .73 with correlations ranging from .62 to .88 (Jesness, 1962). Moreover,

according to Jesness (1962), the “research shows the JI leads to correct classification and

prediction a large percentage of the time. This research supports the discriminate validity

of the JI” (p. 27). More recent research adds considerable support for the construct

validity of the JI (Jesness, 1988). Studies both with delinquents and non-delinquents

found that the subtypes could predict different types of behavior (Jesness & Wedge,

1982; Jesness, 1986). Finally, one study found differences across subtypes in the average

number of probation referrals, and the highest proportion of delinquency was found

among those classified as CFC, NA, and AP (Jesness, 1988). The four types used in the

present study were successfully tested for construct and predictive validity in the original

study (Van Voorhis, 1994).

Besides the established validity and reliability of the Jesness Inventory, its use in

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the current study has several additional advantages. First, the Jesness Inventory was

developed out of the work of Warren on I-level theory (Warren, 1966). The Jesness

Inventory, however, is shorter and more reliable than the lengthier I-level assessment.

Moreover, Van Voorhis (1994) found that the Jesness Inventory subtypes, with one

exception9, are in fact comparable to the types produced by the more complex I-level

assessment developed by Warren and associates. Second, the Jesness Inventory was

developed to provide a personality classification that was relevant to criminal

populations. Unlike the MMPI or the California Psychological Inventory that were not

developed with the purpose of distinguishing types of delinquents, the JI was originally

developed to evaluate treatment for young male delinquents (Jesness, 1964). Third, as

previously discussed, the correctional classification findings on personality types are

discussed in relationship to treatment amenability but not criminological theory. By using

a classification system traditionally used in corrections and testing its applicability in the

area of prediction, it becomes relevant for both criminological theory and corrections.

Table 10 illustrates the distribution of the sample by the Jesness Inventory personality

types and I-levels. This study employs the four collapsed types shown at the bottom of

Table 10. As noted in Chapter 2, the nine types are grouped into four categories:

aggressives (comprised of Aa’s, Cfc’s, Mp’s), neurotics (comprised of Na’s and Nx’s),

dependents (comprised of Ap’s and Cfm’s) and situationals (comprised of Se’s and Ci’s).

Salient Factor Score (81). Prior record and the current offense variables have been

used in risk assessment measures since the late 1920s with the advent of the Burgess

score (Burgess, 1928). To measure risk, the present study utilizes a modified version of

9 The Ci and level 5

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Table 10. Percentage and frequency distribution of participants’ I-level and personality types.

_______________________________________________________________________ Characteristic N % ________________________________________________________________________ Jesness I-Level Types I – 2 12 4.3 I – 3 167 60.3 I – 4 98 35.4 Jesness Personality Types Unsocialized Aggressive (AA) 5 1.8 Unsocialized Passive (AP) 7 2.5 Immature Conformist (CFM) 37 13.4 Cultural Conformist (CFC) 67 24.3 Manipulator/Pragmatist (MP) 63 22.8 Neurotic Acting Out (NA) 30 10.9 Neurotic Anxious (NX) 21 7.6 Situational/Inhibited (SE) 38 13.8 Cultural Identifier (CI) 8 2.9 Collapsed Jesness Types Aggressive 135 48.9 (AA, CFC, MP) Neurotic 51 18.5 (NA & NX) Dependent 44 15.9 (AP & CFM) Situational 46 16.7 (SE & CI) ________________________________________________________________________

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the Salient Factor Score (SFS). The SFS was designed as an assessment device used to

predict the likelihood of recidivism among federal parolees (Hoffman and Beck, 1985).

The most recent version of the SFS, the SFS(81), has been in use by the United States

Parole Commission since 1981 (Hoffman, 1994). This assessment instrument “contains

six items that, applied together, produce a score from zero to ten points. The higher the

score the lower the likelihood of recidivism” (Hoffman & Beck, 1985, p. 502). Research

indicates that the SFS was predictive of recidivism in a 5-year follow-up study (Hoffman

and Beck, 1985). In addition, using three different samples ofparolees, another study

found that the assessment instrument retained its predictive accuracy over a 17-year

follow-up period (Hoffman, 1994). Finally, the SFS was also used successfully to predict

offending in a recent evaluation study of parolees in Georgia (Van Voorhis, Spruance,

Johnson Listwan, Ritchey, Pealer, & Seabrook, 2001).

The six items contained in the SFS (81) scale are: prior

convictions/adjudications; prior commitments of more than thirty days (adult or juvenile),

age at current offense; recent commitment free period; whether the offender is currently a

probation and parole violator; and history of heroin or opiate dependence. The risk

categories are defined as: 10-8 very good risk, 7-6 good risk, 5-4 fair risk, and 3-0 as poor

risk. The SFS risk calculation was modified for the current study given data on recent

commitment free periods and a history of opiate or heroin use were not available.

Commitment free periods were replaced with a calculation of whether the offender had a

prior record of probation or parole violations. History of heroin or opiate use was

replaced with a history of drug dependence. In the current study the SFS categories were

significantly correlated with the arrest at the .01 level (r = -.342).

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Table 11 illustrates the risk scores for the current sample. Thirty-three percent of

the sample scored as very good, 34 percent as good, 27 percent as fair, and 7 percent as

poor. Collapsed, it appears that 66 percent of the sample would be considered low risk

and 34 as high risk.

Race. Race is prominent in many criminological theories of crime ranging from

individual level explanations to macro-level theories. However, the impact of race on

criminal behavior is multifaceted. Race by itself does not provide us with an explanation

of crime, but rather is highly related to other risk factors of crime such as social class,

age, and gender (Hindelang, 1981). It is important to note, given the sample was selected

in the Federal system, the majority of the participants are Caucasian (66%).

Group Membership. The two groups, federal penitentiary and prison camp, were

combined to create the sample used in this analysis. Group is entered as a dummy

variable into the multivariate analysis to determine whether membership in the group

influences outcome.

Dependent Variables

The impact of personality on crime is assessed by examining recidivism rates

among the sample of federal penitentiary and prison camp inmates. The data collected

through NCIC reveal how many individuals in the sample recidivate, the frequency by

which they do so, and the type of offending behavior. Table 12 frequency distribution of

the participants arrest record for each of the dependent variables. By providing a long-

term follow up period, the current study allows for an in-depth examination of these

factors.

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Table 11. Percentage and frequency distribution of participants’ Salient Factor Scores.

________________________________________________________________________

Characteristic N % ________________________________________________________________________ Risk Score (SFS) Very Good 91 32.9 Good 93 33.6 Fair 74 26.7 Poor 19 6.9 Risk Score Collapsed Low 184 66.4 High 93 33.6

________________________________________________________________________

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The first measure of recidivism is any arrest10. The data collected includes both

whether the defendant was arrested as well as the arrest date. Maltz (1984) asserts that

arrest is more useful than conviction data because it is closer temporally to the crime and

is less subject to court processing biases. The second measure of recidivism is based on

the number of times the individual was arrested. The third measure is based on the type

of offense that the offender was charged with after release from either the penitentiary or

prison camp.

Data Analysis

The analysis will produce three models predicting different aspects of offending.

All three models will examine the relationship between the Jesness Inventory I-level and

personality classifications and outcome. The three outcome events are: (1) arrest, (2)

frequency of arrest, and (3) nature of the offense.

Model #1: Personality and Any New Arrest

In the first model, discrete time event history analysis was utilized to explore the

relationship between personality, I-level, and any new arrest. Event history analysis is

used as a means to study events by modeling the probability or likelihood of the event

occurring (Allison, 1995). Event history analysis is an analysis of changes over time. In

traditional statistical technique such as logistic regression, the analysis of change with

time simply inserted as a control variable will yield biased estimates. Specifically, “they

can severely misrepresent the structure of the process since they provide descriptions of

the associations over time among variables but offer no possibility of identifying the

parameters of the underlying processes that produce these associations” (Palloni and

10 The only contact that is excluded is non-DUI traffic offenses.

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Table 12. Percentage and frequency distribution of participants’ recidivism rates (Time 2 data).

________________________________________________________________________

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.

85

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

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Figure 1

Personality Type by Rearrest Rate

Statistically significant differences

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Aggressives 6.4 12.4 18.1 23.3 28.2 32.9 37.2 41.2 45 48.5 51.8 54.9Neurotics 8.5 16.2 23.3 29.8 35.8 41.2 46.2 50.7 54.9 58.7 62.2 65.4

Dependents 3.6 7.1 10.4 13.6 16.7 19.7 22.6 25.4 28.1 30.7 33.2 35.6Situationals 5.7 11.1 16.2 21 25.6 29.8 33.9 37.7 41.3 44.6 47.8 50.8

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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

________________________________________________________________________ Parameter Standardized Odds Ratio Estimate Estimate SFS Riska .559** .188 1.749 Raceb .636*** .185 1.888 Personalityc

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

93

Figure 2

Rearrest Rate by Personality Type:

Low Risk

Statistically significant differences

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Aggressive 4.4 8.6 12.6 16.5 20.1 23.6 27 30.2 33.3 36.2 39 41.7 Neurotic 5.8 11.3 16.5 21.4 26 30.3 34.4 38.2 41.8 45.2 48.4 51.4

Dependent 2.4 4.8 7.1 9.4 11.6 13.8 15.9 17.9 19.9 21.9 23.8 25.6 Situational 3.9 7.6 11.2 14.7 18 21.2 24.3 27.2 30 32.8 35.4 37.9

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Figure 3

Rearrest Rate by Personality Type:

High Risk

Statistically signficant differences

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100 Aggressive Neurotic Dependent Situational # & ∋ )

Aggressive 9.5 18.1 25.8 32.9 39.2 45 50.2 54.9 59.2 63.1 66.6 69.7 Neurotic 12.4 23.2 32.7 41 48.3 54.7 60.3 65.2 69.5 73.3 76.6 79.5

Dependent 5.4 10.5 15.3 19.9 24.2 28.3 32.1 35.8 39.2 42.5 45.6 48.5 Situational 8.4 16.2 23.3 29.7 35.7 41.1 46.1 50.6 54.8 58.6 62.1 65.3

95

In the model described above, the analysis explored the independent contributions

of personality while controlling for risk and race. Both race and risk were significantly

related to outcome. Specifically, those individuals classified as high risk had a higher

likelihood of recidivism than the lower risk group. However, Figure 1 does not show the

probabilities separated by those classified as high risk and low risk. Hence, Figures 2 and

3 illustrate the probabilities of arrest by personality for high risk participants and low risk

participants.

It should be noted that an analysis was conducted to determine whether an

interaction existed between personality and race. Although a significant difference did

exist between the additive and interaction models, we could not localize the significant

differences due to the concerns for the power of the interaction models.

Multivariate Analysis of Multiple Arrests: Model 2

Of the 277 individuals under study, 85 (31.0%) were arrested multiple times

during the follow-up period. By using repeated events event history analysis, it is

possible to estimate a model of the probability of multiple arrests during the follow-up

period and distinguish the relative influence of time varying and time invariant

predictors. The results of the repeated measures event history analysis, which takes into

account multiple offenses, are presented in Figure 4. In this model, personality was

significant even after controlling for race, time, and risk score.

As with the first model, a number of regression equations were estimated to

determine the impact of a variety of independent variables. After exhausting several

models (see Appendix C) 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

96

Figure 4

Personality Predicting Multiple Rearrest

Statistically Significant Differences

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Time1 Time2 Time3 Time4 Time5 Time6 Time7 Time8 Time9 Time10 Time11 Time120

20

40

60

80

100Aggressives Neurotics Situationals Dependents# & ) ∋

Aggressives 15.1 24.9 30.7 36 41 45.5 48.3 51 53.5 55.8 58.1 60.2Neurotics 18 29.3 35.9 41.9 47.3 52.3 55.2 58 60.6 63.1 65.4 67.5

Situationals 11.9 19.8 24.6 29.1 33.4 37.4 39.8 42.2 44.5 46.7 48.8 50.8Dependents 12.3 20.4 25.4 30 34.3 38.4 40.9 43.3 45.6 47.8 49.9 52

97

types (aggressive 1=yes; 0 = other; neurotic 1=yes; 0= other; etc) and four time variables

(year 1, year 2, year 3-6, year 7-12). Time was collapsed into four different variables:

Year 1, Year 2, Year 3-6 and Year 7-12 in order to create a more parsimonious model.

The model with all twelve time variables was not needed after the analysis indicated that

the differences among the parameter estimates were not significant between years 3-6 and

years 7-12 (see Appendix C, Table 1). The analysis indicates that risk, race, personality

and time were significantly related to the probability of multiple failures (see Table 18).

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 multiple arrests were among the neurotics

followed by the aggressives, situationals, and finally the dependents. The analysis

indicated that neurotics were significantly different from the dependents and situationals

but not significantly different from the aggressives. In fact, the aggressives were not

significantly different from any of the other types (see Appendix C). Finally, I-level was

not significantly different in any of the models.

Multivariate Analysis of Offense Type: Model 3

Drug Offenses. Of the 277 participants in the study, 34 (12% of the total sample)

were arrested for a drug related offense during the follow-up period. The influence of

personality, race, risk, and group were assessed using discrete time event history analysis.

After exhausting a more inclusive model (see Appendix D), 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 5. As indicated in Table

98

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

99

Figure 5

Rearrest Rate by Personality Type: Drug Offenses

Statistically significant differences

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Year1 Year2 Year3 Year4 Year5 Year6 Year7 Year8 Year9 Year10 Year11 Year120

10

20

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40

50Aggressives Neurotics Dependents Situationals# & ∋ )

Aggressives 0.9 1.9 2.8 3.7 4.6 5.5 6.4 7.3 8.2 9.1 9.9 10.8Neurotics 2.5 4.9 7.3 9.6 11.9 14.1 16.2 18.3 20.3 22.3 24.2 26.1

Dependents 0.6 1.2 1.7 2.3 2.9 3.4 4 4.5 5.1 5.6 6.2 6.7Situationals 0.7 1.5 2.2 2.9 3.6 4.3 5 5.7 6.4 7.1 7.8 8.5

100

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

101

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

102

Figure 6

Rearrest Rate by I-Level Type: Drug Offenses

Statistically significant differences

+

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Year1 Year2 Year3 Year4 Year5 Year6 Year7 Year8 Year9 Year10 Year11 Year120

10

20

30

40

50I - 2 I - 3 I - 4∃ ∗ +

I - 2 1.4 2.8 4.1 5.4 6.7 8 9.3 10.6 11.8 13 14.2 15.4I - 3 0.8 1.6 2.4 3.2 4 4.8 5.5 6.3 7.1 7.8 8.6 9.3I - 4 1.7 3.3 4.9 6.4 8 9.5 11 12.5 13.9 15.3 16.7 18.1

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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

104

Figure 7

Rearrest Rate by Personality Type: Property

Offenses

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Year Year Year Year Year Year Year Year Year Year1 Year1 Year10

10

20

30

40

50 percentage

Aggressive Neurotic Dependent Situational # & ∋ )

Aggressive 1.5 2.9 4.4 5.8 7.2 8.5 9.9 11.2 12.5 13.8 15.1 16.3 Neurotic 1.6 3.2 4.8 6.3 7.8 9.3 10.8 12.2 13.7 15.1 16.4 17.8

Dependent 1 2 3 4 5 6 7 7.9 8.9 9.8 10.7 11.6 Situational 0.9 1.8 2.7 3.5 4.4 5.3 6.1 7 7.8 8.6 9.5 10.3

105

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

106

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.

107

Figure 8

Rearrest Rate by Personality Type: Violent Offenses

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0

10

20

30

40

50Aggressive Neurotic Dependent Situational# & ∋ )

Aggressive 0.3 0.7 1 1.4 1.7 2.1 2.4 2.7 3.1 3.4 3.7 4.1Neurotic 0.4 0.8 1.2 1.6 1.9 2.3 2.7 3.1 3.5 3.8 4.2 4.6

Dependent 0.5 1 1.5 2.1 2.6 3.1 3.6 4.1 4.6 5.1 5.6 6Situational 0.2 0.5 0.7 0.9 1.2 1.4 1.6 1.8 2.1 2.3 2.5 2.7

108

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

109

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

110

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

113

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

recidivism (Forth, Hart, & Hare, 1990; Hare, & McPherson, 1984; & Harris, Rice, &

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.

116

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,

117

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

118

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.

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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

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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.

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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;

Caspi & Roberts, 1999; Eysenck & Eysenck, 1985; Sampson & Laub, 1990). Moreover,

123

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

for experiencing negative events (Watson & Clark, 1984; Bolger & Schillings, 1991;

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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140

APPENDIX A

141

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

142

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

143

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

144

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

145

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

148

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

156

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

163

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

167

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

168

APPENDIX E

169

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

170

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

171

APPENDIX F

172

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