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UNDERSTANDING THE RELATIONSHIP BETWEEN MATURATION AND DESISTANCE FROM CRIME: A LIFE-COURSE DEVELOPMENTAL APPROACH

A Dissertation Presented

by

Michael Rocque

to

The Graduate School

In partial fulfillment of the requirements for the degree of

Doctor of Philosophy

in the field of

Criminology and Criminal Justice

Northeastern University Boston, Massachusetts

September, 2012

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UNDERSTANDING THE RELATIONSHIP BETWEEN MATURATION AND DESISTANCE FROM CRIME: A LIFE-COURSE DEVELOPMENTAL APPROACH

by

Michael Rocque

ABSTRACT OF DISSERTATION

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Criminology and Justice Policy

in the Graduate School of Northeastern University September, 2012

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ABSTRACT Over the last twenty years, research in criminology has expanded beyond a focus on

adolescence to examine crime and deviance over the life-course. As a result, more attention has

been paid to desistance or the process of ceasing criminal behavior. This work has revealed a

large number of factors that are related to desistance, including marriage, employment,

psychosocial development and individuals’ identity. To date, these explanations for desistance

seem to have been perceived as mutually exclusive and/or competitive.

Interestingly, while research on desistance from crime has been a recent focus in

criminology, certain work had examined crime over the life-course as far back as the early 20th

century. In particular, Sheldon and Eleanor Glueck offered one of the earliest ‘theories’ of

desistance, arguing that maturation causes individuals to settle down and cease offending. Their

“maturation theory” was somewhat tautological and not well-specified, which is a large part of

why it has generally been relegated to the criminological dustbin.

However, the Gluecks were clear that further work was needed in order to specify what

maturation meant and how it possibly related to crime. In this dissertation, I articulate five

domains of maturation, drawing on the literature in the life-course and developmental fields. In

the first set of analyses, an examination of crime and maturation over time is conducted, using

empirical growth curves. These analyses show that crime follows the classic age-crime curve,

while maturation increases over time though not always linearly. Second, in the main analyses, I

examine how maturation relates to crime over time, focusing specifically on desistance. The

analyses reveal that three of the five domains (as well as the average maturation measure) predict

crime over time. Third, I examine varying specifications of the maturation-crime relationship,

including maturation gaps and possible conditional relationships between maturation domains.

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The results show that adult social role maturation (employment, romantic relationships) has a

larger effect when other maturation levels are low. In sum, maturation has a generally strong and

complex relationship to crime. The implications of the findings in terms of theory and policy are

discussed in the final chapter.

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© Copyright by Michael Rocque

2012

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ACKNOWLEDGEMENTS Being something of a dissertation connoisseur, I have stumbled across many in my

graduate career. Perhaps none have struck me as much as the one written in 1995 by Chris Uggen. What stands out immediately (aside from the keen scholarship and overall quality) is the acknowledgments section. This section, which on average runs from about a half a page to one page in most dissertations, is a full 3 pages, single spaced in Professor Uggen’s dissertation. In fact, he has written a blog about it (here). While it is humorous at times, it expresses well the sheer amount of gratitude that he felt for those who helped him out along the way. So with that said, the acknowledgments observed here are meant to thank, sincerely, all those who have in no small way helped me out along the way. If I forgot anyone that is my own fault—know that I am grateful to you anyway.

First, I want to thank my committee, Drs. Ineke H. Marshall, Chet Britt, Helene R. White, and Ray Paternoster. Each have played a different and important role in the completion of this project and is worthy of recognition. Ineke was the theory seminar professor for my cohort, teaching us both sociological and criminological theories from Fall 2009 to Spring 2010. For those who know her, they understand her humor, kind spirit, and never-ending support were just some of the reasons we, as a cohort, forced ourselves under her wing. Of the six individuals in this cohort, all but one have asked her to be on their committees. I’m sure the outlier would have too, but she left the program. Ineke has helped me in more ways than I can recount here—from allowing me an active role in her own research (the International Self-Report of Delinquency study), to talking about my own ideas, to lending an ear when I had a personal situation that I couldn’t figure out. You have been an ideal chair, and I am so glad to have had the privilege of being your student. I owe you so much more than chocolates or vino. Thank you.

I did not begin working with Chet Britt until my second year at Northeastern. After having a rough second semester, I found out that I was assigned to be a Teaching Assistant for the Dean of our School. I asked if this was a punishment—a comment which, I’m told, led to quite a few faculty snickers. Instead it turned out to be a blessing. Being the dean most likely deters some students from approaching Chet to work with them, but those that do not approach him are missing out on one of the nicest, funniest, and brightest professors that I have ever had the privilege to know. Chet handled my countless emails and requests with humor and grace, made me realize I could take on a new method, and along the way taught me a lot about theory. The phrase, “it’s just a little algebra” will forever remind me that statistics are often less daunting than they seem at first.

I met Ray Paternoster in 2006 at the University of Maryland. I had nearly given up on an idea I had been working on for a year when I decided to see what Ray thought. In our first meeting, I explained what would become my master’s thesis, expecting his eyes to glaze over. Instead, he smiled and said “makes sense to me”. Since then I have continued to work with Ray and have never stopped learning. Ray allowed me to sit in on his doctoral level theory class and I learned more there than in any classroom setting before or since. Alex Piquero is absolutely right when he says taking a class with Ray is an experience that to understand you “just have to be there”. Ray actually is the first person with whom I shared the maturation idea that underlies this dissertation, and his unfailing support and sharp insight (along with much needed comic relief) are major reasons why it is completed.

A couple of years ago, after having thought and worked long and hard on the theoretical perspective for the dissertation, I was faced with now finding data to test it. This was no easy

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feat, as the data had to be longitudinal, and include a wealth of measures not generally found in datasets. After striking out various times, I asked Helene about her data and without pause, she gave consent. It is safe to say that without the Rutgers Health and Human Development Project data, this dissertation would not have been completed—or at least would have been much weaker. In two trips to Rutgers to examine the data, I learned a tremendous amount about longitudinal studies and got to know Helene. I imagine it took some convincing to gain Helene’s trust but once that happened, I was the beneficiary of her insight, eagle eye editorship and incredible support. The entire experience, from my first trip to Rutgers to now has been a great adventure.

It should go without saying that faculty who were not on my committee also played a huge role in helping me reach this stage. In fact, though I did not think it possible, my experience at Northeastern was an incredible, eye-opening, educational experience. For that I am eternally grateful. In terms of faculty, first I would like to thank Simon Singer, who was also assigned me after my rough second semester. Simon helped to build my confidence and taught me how to think outside the box. Simon is also an avid swimmer and his dedication to health has helped push me to take up the activity. I now consider Simon a friend and colleague and am much enriched for it.

I began to work for Brandon Welsh my second year at Northeastern. He is, quite simply, a joy to work with. His organization and fountain of ideas are things that I hope to carry on with me wherever I end up. Brandon is the quintessential unselfish scholar, always bringing students into his work, and offering his advice and support whenever possible. There are very few people, let alone scholars, who are willing to go as far out of their way to help as Brandon. Not only that, but I’ve learned much about how to appreciate a good college hockey game—what is and is not a penalty—and how to be a fan of life from working with Brandon. He, like Simon, I now consider a friend and colleague.

I want also to thank Nicole Rafter for her incredible support and assistance along the way. Nicky always makes students feel as though they are worthy of her time and invariably any project she touches becomes more insightful and enjoyable.

Steve Barkan, Steve Cohn, and Amy Blackstone also deserve mention at the University of Maine, Orono. I took my first ever criminology course at the University of Maine in 2002. Steve Barkan showed me what the discipline is all about and how many questions still remain to be asked. He has now been a colleague for the last several years and always surprises me with his seemingly never ending reserve of ideas and the fact that he and Steve Cohn often published studies years before others ever thought about such questions. Steve Cohn might be the most brilliant of scholars I’ve ever known. His insights and unique views on sociology helped to get me excited about the field. Amy Blackstone, now the chair of the UMaine sociology department has not only provided me with employment the last few years, but has lent me her ear on many occasions. The way she models her life as a productive scholar and beloved teacher is one that I aspire to—though will likely fall short of.

With respect to non-faculty individuals deserving of thanks, there are probably too many to name here. With respect to non-faculty individuals deserving of thanks, there are probably too many to name here. Matthew Dolliver, Diana Summers Dolliver, Kristin Rose, Jen Ross, and Kitty Peel all were fun colleagues who leaned on each other at various points, always interested in helping one another out, rather than competing. We’ve been through a lot together, two name changes, classes, comps, and the normal stress of grad school. I couldn’t have asked for a better cohort.

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One colleague however, deserves special mention. I met Chad Posick on our first day of orientation at Northeastern. Something about the inquisitive look and even keel demeanor let me know that this was a person with whom I could work. I was right. Since I met Chad four years ago, we’ve discovered that our ideas about criminological issues often mesh, resulting in sharper analyses and overall better products. He and I helped to edit each paper we had to write for classes and papers we’ve worked on for publication. Most importantly, Chad read every single page of this dissertation well in advance of it passing under anyone else’s eyes. Whatever errors remain are, of course, my own, but the dissertation is what it is, in no small part because of you, Chad. Thank you. I am looking forward to continuing our collaboration.

Jeff Nowacki, Amber Beckley and Dave Mazeika have also remained close friends and confidants since we first met in 2005 on a hot and humid University of Maryland, College Park day. The three of us were initiated into the world of grad school together and helped one another survive it. Today Jeff and Dave are there for me to bounce ideas off of, to listen to my complaints, and to share in this experience. Friends like these, I think, are an essential part of the support system that is necessary to “make it”.

Finally, I would like to thank my family, for whom I owe any and all success I’ve ever had. My parents, David and Jeanne Rocque, never once waivered in their support of my choice to pursue a seemingly endless degree. My mother’s constant encouragement and my father’s assistance in becoming a more effective writer will always be appreciated. In fact, I remember that my father somehow knew I would pursue a Ph.D. even before I did, telling me with a coy smile that I will go for it, even after I unconvincingly argued that I did not want one.

Last, and most importantly, my wife, Andrea. We have been through all of the struggles that go along with relationships in which one person chooses a life of academia. Throughout it all she has been my rock, the one constant. I know it hasn’t been easy, but I hope you know how much a part of this you are and have been for the last 7 years. I couldn’t have done it without your unending support.

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Table of Contents

ABSTRACT ........................................................................................................................ 3

ACKNOWLEDGEMENTS ................................................................................................ 6

CHAPTER I. INTRODUCTION & RATIONALE FOR STUDY ................................... 13

CHAPTER II. MATURATION & CRIME: A HISTORICAL REVIEW ........................ 28

CHAPTER III. THE STUDY OF DESISTANCE IN CRIMINOLOGY ......................... 35

CHAPTER IV. A MULTI-DIMENSIONAL CONCEPTION OF MATURATION ....... 65

CHAPTER V. DATA & RESEARCH METHODS ......................................................... 84

CHAPTER VI. RESULTS: DELINQUENCY & CRIME OVER TIME ...................... 130

CHAPTER VII. RESULTS: MATURATION OVER TIME ......................................... 139

CHAPTER VIII. RESULTS: THE RELATIONSHIP BETWEEN MATURATION &

DELINQUENCY/CRIME .............................................................................................. 153

CHAPTER IX. DISCUSSION & SUMMATION .......................................................... 171

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List of Tables

Table 5.1. Age and Sample Size for the Youngest Cohort ......................................................... 223 Table 5.2. Mean Distribution of Delinquency: T1-T5 ................................................................ 224 Table 5.3. Descriptive Statistics for Covariates .......................................................................... 225 Table 6.1. Unconditional Growth Models for Crime/Delinquency ............................................ 226 Table 7.1. Scale and Item Information for Domain Construction (Time 1) ............................... 227 Table 7.2. Scale and Item Information for Domain Construction (Time 2) ............................... 228 Table 7.3. Scale and Item Information for Domain Construction (Time 3) ............................... 229 Table 7.4. Scale and Item Information for Domain Construction (Time 4) ............................... 230 Table 7.5. Scale and Item Information for Domain Construction (Time 5) ............................... 231 Table 7.6. Social Maturation Growth Models ............................................................................ 232 Table 7.7. Civic Maturation Growth Models .............................................................................. 233 Table 7.8. Psychosocial Maturation Growth Models.................................................................. 234 Table 7.9. Identity/Cognitive Transformation Maturation Growth Models ............................... 235 Table 7.10. Neurocognitive Maturation Growth Models ............................................................ 236 Table 7.11. Average Maturation Growth Models ....................................................................... 237 Table 8.1 Bivariate Relationships Between Maturation Domains and Crime/Delinquency ...... 238 Table 8.2. Effect on Delinquency of a Standard Deviation Change in Maturation .................... 239 Table 8.3. Growth Models of Social Maturation on Crime (Variety) ........................................ 240 Table 8.4. Growth Models of Social Maturation on Crime (Dichotomous) ............................... 241 Table 8.5. Growth Models of Civic Maturation on Crime (Variety) .......................................... 242 Table 8.6. Growth Models of Psychosocial Maturation on Crime (Variety) .............................. 243 Table 8.7. Growth Models of Psychosocial Maturation on Crime (Dichotomous) .................... 244

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Table 8.8. Growth Models of Identity Maturation on Crime (Variety) ...................................... 245 Table 8.9. Growth Models of Identity Maturation on Crime (Dichotomous) ............................ 246 Table 8.10. Growth Models of Neurocognitive Maturation on Crime (Variety) ........................ 247 Table 8.11. Growth Models of Average Maturation on Crime (Variety) ................................... 248 Table 8.12. Growth Models of Average Maturation on Crime (Dichotomous) ......................... 249 Table 8.13. Overdispersed Binomial Regressions of Maturation Gaps on Crime ...................... 250 Table 8.14. Overdispered Binomial Regressions of Social Role Maturation on Crime ............. 251 Table 8.15. Overdispersed Binomial Regressions of Social Role Maturation on Crime ........... 252

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List of Figures

Figure 3.1. Illustration of Moffitt’s Taxonomic Theory ............................................................. 253

Figure 4.1. Maturation Domain Schema ..................................................................................... 254

Figure 5.1. Graphic Illustration of Delinquency Over Time in the HHDP................................. 255

Figure 6.1. Delinquency Over Time with Expanded T4-T5 Items ............................................. 256

Figure 6.2. Delinquency Over Time by Sex ............................................................................... 257

Figure 6.3. Delinquency Over Time by Race ............................................................................. 258

Figure 7.1. Maturation Domains Over Time .............................................................................. 259

Figure 7.2. Identity/Cognitive Transformation Maturation Over Time by Sex .......................... 260

Figure 7.3. Neurocognitive Maturation Over Time by Race ...................................................... 261

Figure 8.1. Fitted Values of the Variety Score over Time in the Average Maturation Growth

Curve Model (Model 2) .............................................................................................................. 262

Figure 8.2. Fitted Values of Average Maturation, Within and Between Individual Model (Model

4) ................................................................................................................................................. 263

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CHAPTER I. INTRODUCTION & RATIONALE FOR STUDY He got older and slowed down. Age changes people.

~James Dixon, Sr. speaking about his son, who is currently serving 15 years to life for accessory to murder

Introduction

The notion that there are two types of people in the world—those that have the capacity

for evil and those that do not—remains ever-present in today’s society (Maruna, 2001). While

the belief in the criminal “other” has long been held by the public, it appears to have increased

during the Reagan “tough on crime” era (Melossi, 2008). Many view criminals as “no longer a

human being similar to us….On the contrary, they are dangerous. They are either bad or saddled

with some kind of personal deficit that makes them act as bad people” (Melossi, 2008: 221).

Much as the criminologist James Q. Wilson had assured people in the 1970s, people tend to

assume that “wicked people exist” (Wilson, 1975: 209). Further, it seems as if an increasing

number of people in the public believe that not only do they exist, but they do not change. The

idea of the life-long criminal, such as the recently caught James “Whitey” Bulger, is as popular

as ever (see, for example, McPhee, 2011).

However, recent criminological research, exploiting a growing body of longitudinal data

that follows the same individuals over time has questioned this logic. To be sure, there are those

who offend later in life than others. Yet the vast majority of people who commit crimes

eventually stop engaging in crime and antisocial behavior after adulthood is attained. Even

Whitey Bulger appears to have lived his last years of freedom relatively peacefully, writing his

memoirs (The Associated Press [AP], 2012). Criminologists have come to term this process of

slowing down and eventually ceasing to engage in antisocial behavior as “desistance.” While

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research is accumulating on what desistance is and how it occurs, the reasons and mechanisms

undergirding the process are still not well understood.

The purpose of this dissertation is to describe and test a theoretical model of maturation

as an explanation of desistance from crime. The primary research focus of this dissertation is to

explore whether a multifaceted, integrated conceptualization of maturation, from a

developmental perspective, can further our knowledge of why individuals who previously

engaged in crime eventually stop offending (i.e., ‘desist’)?

Therefore, in this dissertation, I will seek to contribute to our knowledge of the process of

desistance. It will examine desistance as a ‘normative’ process (that is, it happens for nearly all

offenders) while seeking to understand why it occurs earlier rather than later for some. Along the

way, the study will seek to identify distinct “domains” of maturation that may allow for a

nuanced understanding of the transition to adulthood and how the developmental processes

involved in maturation impact behavior. In what follows, I attempt to set the stage for the current

research by describing the use of “maturation” as an explanation of desistance and arguing that

desistance as an area of study is still in need of further research. I suggest that much of the

current ‘life-course’ research on desistance is directly relevant to a revitalized conceptualization

of maturation that may improve our understanding of why and how offenders stop committing

crimes.

Criminology and Desistance

In recent years, criminologists have grown increasingly interested in what is referred to as

life-course or developmental criminology. In part, this new focus has turned the spotlight away

from a primary concern with explaining why individuals begin to commit crimes or antisocial

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acts and has recognized the need to examine individuals throughout life. This work, while not

without controversy, has generated a large amount of knowledge about what makes some people

more or less likely to stop offending. For example, a near universal finding is that as offenders

age, they are less likely to commit crimes. This finding has been consistent across research at the

individual and aggregate level (Sampson and Laub, 1993; 2005a; Laub and Sampson, 2003;

Hirschi and Gottfredson, 1983) and has led some to argue that desistance need not be

theoretically explained (indeed cannot be explained) with variables used by criminologists

(Hirschi and Gottfredson, 1983; Gottfredson and Hirschi, 1990). Others argue that desistance

experiences vary substantially enough to warrant explanation into what makes offenders “go

straight” (Laub and Sampson, 2001; 2003; Maruna, 2001; Sampson and Laub, 1993; Savolainen,

2009; Warr, 1998; Uggen, 2000).

In the mid-20th century, criminologists often referred to the gradual movement away from

crime with age as maturation or maturational reform (Glueck and Glueck, 1937; 1940; 1943;

Hirschi, 1969; Matza, 1964). While this term was not well-specified, for some researchers, it was

distinct from age itself (Glueck and Glueck, 1937; 1940), which implied several things. First, the

use of maturation as a cause of desistance suggested that crime and antisocial behavior are

inversely related to individual development. Thus, as people develop, they begin to settle down

and engage in more conforming behaviors. Second, this concept implied that most individuals

eventually stop committing crimes. That is, they desist. Finally, the use of the term maturation to

explain onset and desistance suggested that these processes involve social, biological and

psychological factors—all factors implicated in individual development (Cauffman and

Steinberg, 2000; Gove, 1985).

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Criminologists, recognizing the vagueness of the term ‘maturation’ in relation to crime

and its seeming ontological character, began to reject maturation as an explanation of change in

criminal behavior over time (Maruna, 2001; Shover, 1985; Shover and Thompson, 1992). Crime,

some have argued, is a social behavior and needs to be explained by social factors (see Abbott,

1992; Hammersley, 2008; Dannefer, 1984; Sampson and Laub, 1993). Thus to the more

sociologically-oriented criminologists—past and present—maturation was not viewed as an

integral factor in understanding criminal (re: social) behavior (see Geis, 1970; Gibbons, 1970;

Laub and Sampson, 2001; 2003; Maruna, 2001; Shover, 1996; Sutherland, 1937; Wooton, 1962).

As a result, maturation is not used as a main explanatory variable in the recent criminological

literature, and is referenced generally either in vague terms or as a relic of the past (see Farrall

and Calverly, 2006; Laub and Sampson, 2001; Sampson and Laub, 2003; Shover and Thompson,

1992).

The criminologists who utilized the concept of maturation and maturational reform as an

explanation of desistance in the early to mid-20th century understood that future research would

have to clarify and explicate the term. Sheldon and Eleanor Glueck were perhaps the most

prominent researchers to discuss maturation as a cause of desistance. They argued that

maturation involved “biologic and psychologic processes” needed to successfully navigate social

roles (Glueck and Glueck, 1937: 15; 1940; 1945; 1974). While some might have accused them of

committing an “ontogenetic fallacy” because of the implication that aging out of crime is a

natural process that is the similar for everyone (Maruna, 2001; Sampson and Laub, 2003), they

argued only that maturation is correlated with, but not directly caused by age (Glueck and

Glueck, 1968: 176-177). They suggested that future researchers “dissect maturation into its

components” (Glueck and Glueck, 1940: 270; Maruna, 2001; Sampson and Laub, 2003).

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However, few researchers have sought to further develop a multifaceted concept of

maturation at least in the criminological literature. For the most part, it appears that researchers

in life-course criminology have not treated maturation as a central concept in explaining

desistance. Instead, theories of desistance have used the claims of maturation and ignored the

term.1 Theories of desistance have focused on unspecified aging processes (Gottfredson and

Hirschi, 1990), increases in rationality of offenders (see Shover, 1983; 1985; 1996) implying an

improvement of self-control (one of the components of maturation identified by the Gluecks

some 70 years ago), changes in the way offenders view themselves (cognitive transformations)

(see Giordano, Cernkovich and Rudolph, 2002; Maruna, 2001; Paternoster and Bushway, 2009;

2011), changes in social situations (Laub and Sampson, 2003; Sampson and Laub, 1993), and

changes in the amount of adult privileges or status markers that may lead to less crime (Agnew,

2003; Haynie, Weiss and Piquero, 2008; Massoglia and Uggen, 2010; Moffitt, 1993; 2003;

Piquero, MacDonald and Parker, 2002). Often, these factors have been considered largely in

isolation from one another and not as parts of a larger developmental process.

At the same time, an increasingly nuanced literature in development psychology has

continued to explore the ramifications of maturation on behavior (Baltes, Reese, and Lipsitt,

1980; Cauffman and Steinberg, 2000; Galambos and Tilton-Weaver, 2000; Greenberger and

Steinberg, 1986; Greenberger and Sørensen, 1974; Greenberger et al., 1975; Iselin et al., 2009;

Monahan et al., 2009; Mulvey et al., 2004; Steinberg, 2005). This literature has refined our

understanding of the processes associated with the transition to adulthood. For example, the

pioneering work of Greenberger and Sørensen (1974; Greenberger, 1984) on “psychosocial

maturity” argues that maturation consists of three main domains: 1) individual independence or

1 With apologies to Hirschi (1979) who argued that integrated theorists often borrow the claims of

particular theories while ignoring the terms.

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autonomy; 2) interpersonal or communication skills; and 3) communal values. Research has

shown that elements of psychosocial maturity, and revisions of the concept, are related in

expected ways to antisocial behavior (Cauffman and Steinberg, 2000; Galambos and Tilton-

Weaver, 2000; Iselin et al., 2009; Monahan et al., 2009). In addition, recent developmental work

on neurological maturation has suggested that the brain does not reach full maturity until the

early 20s, a finding that may be related to desistance from crime after adolescence (Giedd et al.,

1999; Gotgay et al., 2004; Paus, 2005; Steinberg, 2005)

It appears that for the most part, the developmental maturation literature and recent life-

course criminology have advanced largely along separate tracks. Nonetheless the sociological

life-course work has helped clarify how the process of aging, settling down and the transition to

adulthood relate to desistance from crime. Yet in much of criminological work on desistance, the

term maturation is used either to reference past work or in ambiguous terms. In none of the

leading life-course criminology works from the last two decades is maturation clearly specified

or delineated in reference to desistance from crime (see Blumstein et al., 1986; Laub and

Sampson, 2001; Kazemian, 2007; Sampson and Laub, 2003). More specifically, I argue that

recent work in life-course criminology along with advances in other fields (e.g., developmental

psychology and neurocognitive sciences) have identified major domains of maturation (while not

always using that term); however, for the most part, researchers have not examined these

domains in an integrated maturation framework in relation to desistance. This suggests that a

major gap in the desistance literature exists with respect to defining maturation and exploring its

impact on crime in emerging2 and later adulthood. In a sense, an integration of existing

2 Emerging adulthood is a term that was first developed by Arnett (2000) and refers to the time when

youths transition from high school into post-secondary education or the work force (usually between the ages of 18-25). Currently, emerging adults are more dependent than similarly aged individuals from previous historical periods (Arnett et al., 2011).

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theoretical perspectives might provide a better understanding of the process of desistance. In

what follows, I present the case for continued examination of desistance from crime.

Why Continue to Study Desistance?

Several researchers have pointed out that desistance from crime (i.e., the right hand tail of

the age-crime curve) remains the least studied part of the criminal career (see Blumstein et al.,

1986; Bushway, Thornberry and Krohn, 2003; Kazemian and Maruna, 2009; Laub and Sampson,

1991; 2003; Maruna, 2001). Consequently, less is known, empirically and theoretically, about

why people stop offending than why they start. Whether the factors that predict initiation in

criminal activities are the same (in reverse) as those that predict exiting from a criminal career

has been the subject of some debate (see Laub and Sampson, 2003; Uggen and Piliavin, 1998).

Longitudinal research on offending has been conducted since the earliest days of

criminology (see Glueck and Glueck, 1937; 1940; 1945; 1968; Kazemian and Maruna, 2009;

Laub and Sampson, 2001; Maruna, 2001; Piquero et al., 2003). Yet, because of the near

ubiquitous finding that offending is concentrated in the juvenile years the discipline has

disproportionately focused on why youths commit crimes (Hirschi and Gottfredson, 1983;

Sampson and Laub, 1992; 1993). Thus, with the exception of a few isolated studies, the

examination of offending throughout the life-course was not a prominent theme in criminology

during much of the 20th century (in other words, not only were the ideas of the Gluecks ignored,

but their research methods were as well—see Cullen, 2011).

However, in the last twenty to thirty years, research interest in age and crime has

increased (Mulvey et al., 2004). This makes the topic of desistance as a separate area of

criminological study a relatively recent development. The ‘newness’ of desistance research

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means that there are numerous unsolved puzzles that remain for researchers to solve. These

puzzles are relevant to theoretical and empirical issues in criminology. First, while there has

recently been an influx of explanations for desistance from crime (to be reviewed more

extensively in the next chapter), there remains a relative shortage of theoretical work, and the

theories that do exist are relatively new. Researchers have posited that several factors impact

desistance from crime, including prosocial relationships (Sampson and Laub, 1993; 2005a);

cognitive transformations or identity shifts (Giordano et al., 2002; Paternoster and Bushway,

2009; 2011); reinterpretations of one’s life (Maruna, 2001); increasing rationality (Cusson and

Pinsonneault, 1986; Shover, 1996) and the direct effects of aging (Gottfredson and Hirschi,

1990; Hirschi and Gottfredson, 1983). Few researchers have sought to explore the ways in which

all or most of these explanations may form the basis of a more comprehensive theory of

maturation and desistance.

In addition, with so few longitudinal datasets that incorporate the requisite information to

test life-course and developmental theories of crime extant explanations of desistance have not

been extensively tested across a multitude of samples. Piquero, Farrington, and Blumstein (2003)

list only 17 studies in the last 100 years of criminological research that have been used to

examine criminal careers and desistance. Much of the literature has been dominated by few

datasets, including the National Longitudinal Study of Youth (1979 and 1997); the National

Youth Survey; the Gluecks’ Unraveling Juvenile Delinquency dataset; and more recently, the

National Longitudinal Study of Adolescent Health (Add Health). As Sampson and Laub (1992)

stated, life-course research is in need of a “fresh infusion of data we can use to address key

limitations of past research. The first step is to counterbalance the dominance in criminological

research of cross-sectional designs, and, to a lesser extent, short-term panel studies” (1992: 79).

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In order to determine, with more confidence, which theories have the most potential to help us

better understand crime across the life-course, it is essential to locate and examine new datasets.

To that end, this dissertation seeks to utilize a dataset that has not been extensively explored

within the larger life-course criminology literature.

Empirically, key methodological debates have emerged over the last twenty years that

raise interesting questions. One particularly salient debate concerns the rise in popularity of

Nagin’s Semi-Parametric Group Based Methodology in criminology and related fields. This

method, which builds on previous work, aims to identify trajectory groups based on the level and

form of offending through time. The method has been adopted by an increasingly large number

of researchers (see Nagin, 2005) but is not without controversy. Some question whether the

method may lead researchers to “find” distinct groups of offenders where there are none (Ezell

and Cohen, 2005) and whether it has led to an improper focus on methodology over theory

(Marshall, 2009; Sampson and Laub, 2005a). It is unclear 1) whether this approach leads to a

false sense that distinct groups of individual development exist and 2) to what extent multiple

methods lead to different conclusions regarding key outcomes.

In addition, the definition and measurement of desistance varies tremendously from study

to study. Some researchers define desistance from crime as the absence of offending during one

time period if the individual had offended previously (Loeber et al., 2008; Kazemian, 2007).

Others use measures of within-individual change to determine whether particular transitions

(e.g., employment) correspond to periods of non-offending (Bushway et al., 2001; Horney,

Osgood and Marshall, 1995; Warr, 1998). Qualitative studies use more subjective classifications,

often relying on the offenders to determine whether desistance is occurring (Maruna, 2001;

Shover, 1985; 1996). Thus it is unclear, to some extent, whether particular findings are a

22

function of methodology or reflect the “real” processes underlying desistance. For this reason

alone, additional desistance research is necessary.

The study of desistance also has significant policy implications with respect to reducing

offending (see Uggen, 1995). As of 2009, there were 7,225,800 individuals under the supervision

of the US correctional system (Glaze, 2010). Additionally, approximately 95% of the 2,292,133

offenders currently incarcerated will be released and recidivism (the analogue of desistance)

rates remain high (up to 67% within three years) (Glaze, 2010; Hughes and Wilson, 2004). Thus,

information pertaining to facilitating the transition to a conventional life is important in terms of

public safety as well.

Thus, desistance remains a key area in need of further research (see Piquero, 2011). New

frameworks or interpretations of why people stop committing crime may help researchers and

practioners develop ways to help individuals abstain from offending. In the next section, I

describe the perspective that underlies the current dissertation, which serves to show that

maturation may be an appropriate framework for understanding desistance as a consequence of

attaining adult status.

Developmental or Life-Course Criminology?

The study of lives through time (Block, 1971) has been described in several ways across

multiple disciplines. In some sense, all of the terms used refer to a focus on the use of

longitudinal research that examines how individual outcomes either vary or stay consistent

across different time periods of the life-course. However, the use of language is not entirely

interchangeable, and may point out key differences in focus or emphasis between fields.3

3 It should be noted, however, that at times, the terms are used interchangeably (see Elder, 1998;

Farrington, 2003; 2007; Sampson and Laub, 2005a).

23

Psychological work often refers to the longitudinal study of individuals as “life-span” research

(see Baltes and Nesselroade, 1984). In addition, more psychologically-oriented studies are

sometimes discussed as “developmental” (Farrington, 2007; Loeber and Le Blanc, 1990).

Sociologically-inclined research is often referred to (increasingly) as “life-course” work (see

Laub, 2006; Sampson and Laub, 2003). In addition, the samples studied in developmental and

life-course frameworks often vary, with developmental work examining largely children through

adolescence and life-course studies focusing more on adulthood.

In general, life-course or sociogenic4 views emphasize the impact of larger social

structures and social institutions on individual lives (see Elder, 1994). Developmental work calls

attention to the relatively “orderly way” in which lives and behavior change over the life

(Thornberry, 2004: 1).5 Developmental work also emphasizes the relative stability in traits or

behaviors over the life-course (Farrington, 2007; Loeber and LeBlanc, 1990; Mulvey and

LaRosa, 1986; Sampson and Laub, 2005b). This perspective helps us to understand why

desistance is the norm rather than the exception (i.e., development is often orderly rather than

stochastic) (Thornberry, 2004; Piquero et al., 2007).

In this dissertation, I will draw on both developmental and life-course perspectives to

understand desistance from crime. In my view, it is difficult to see human development as taking

place outside of social structures. Indeed social structures and institutions are a part of

psychosocial development in today’s society (Foa, 2008; Furstenburg et al., 2004; Giordano et

al., 2002; Massoglia and Uggen, 2010). I argue that changes taking place during late adolescence

4 In general, “sociogenic” refers to factors that are influenced by social relationships or structure. This

perspective does not, however, completely discount the effect of individual or psychological factors on behavior. 5 This does not mean that maturation happens at the same time for everyone. In my view, maturation is

comprised of social (external) as well as internal (biological, psychological) factors (see Farrall et al., 2011). To the extent that these factors do not all occur at the same time (or at all) for each individual, we would expect within-individual differences in maturation.

24

and emerging adulthood (e.g., ‘maturation’) involving both internal and social factors can be

considered developmental processes that play a large role in desistance. There is still much work

to be done regarding the examination of individual development into and during adulthood in

relation to crime. As Adams (2004: 344) argues, “Among the issues that stand out in the study of

adult criminality is the need to take a long-term perspective of development.” This work may

help us to understand how “age changes us”, as Robert Dixon states in the quote that opens the

chapter.

Dissertation Roadmap

The purpose of this dissertation is to undertake a comprehensive examination of what

comprises ‘maturation’ in terms of biological, sociological and psychological factors. I do not

argue that recent criminologists have ignored maturation entirely—rather that the definition of

maturation and how it is related to desistance remains somewhat unclear. In the analysis I will

then attempt to determine whether maturation helps to understand desistance from crime. Part of

this analysis will entail developing “domains” of maturation (as the Gluecks suggested over 60

years ago). Conceiving of development and maturation in this way is “integrative” in a sense.

That is, maturation, in this dissertation, is viewed as comprising elements of many extant

desistance theories. Additionally, an interesting question in the developmental literature pertains

to whether “gaps” across various domains leads to maladaptive behavior (see, e.g., Galambos

and Tilton-Weaver, 2000; Moffitt, 1993; Newcomb, 1996). This research will be able to explore

whether disjunctions between different types of maturation predict crime or desistance.

To address these questions, I use data from the Rutgers Health and Human Development

Project (HHDP) (see Pandina et al., 1984; White, Pandina, and Chen, 2002). The HHDP is a

25

longitudinal study of 1,380 youth from 16 counties in New Jersey. While it does not include a

large number of “serious” offenders, it is particularly well-suited for this analysis because it

includes a wealth of measures that may be used to examine domains of maturation (e.g., social

relationships, work, education, identity, neuropsychological, psychosocial measures). It is also a

multi-cohort study, with three age-groups (12; 15; 18) followed since 1979. The current study

will utilize the youngest cohort (N=447 at Time 1), in which subjects were studied for a larger

portion of the life-course (ages 12 to 30/31) than the other two cohorts.

Chapter II focuses on historical criminological work on maturation and crime. Because

the biggest proponents of “maturational reform” were Sheldon and Eleanor Glueck, who

advocated maturation as an explanation of desistance from the early 1930s until the 1970s, the

chapter focuses on their work. After discussing several critiques of the Gluecks’ approach, the

chapter argues that their theory was essentially “unfinished.” A multi-faceted, integrated

conceptualization of maturation has yet to be developed in the criminological literature—this is

the goal of this dissertation.

The next chapter (Chapter III) reviews the relevant life-course and developmental

literatures on offending and desistance from crime. I begin this chapter by outlining the

measurement/definitional issues associated with studying desistance from crime. The chapter

ends with a brief discussion of recent neurological/brain maturation work showing that key areas

of the brain (that relate to decision-making) continue to develop into emerging adulthood.

Chapter IV presents an updated view of the domains of maturation. Here, I identify five

domains (or components) of maturation from the literature reviewed in the chapter. In addition, I

propose several possible measures of each component. Because this conceptualization of

maturation has yet to be tested in the criminological literature, the research questions/hypotheses

26

that close Chapter 4 relate to whether maturation “domains” can be identified and whether they

help us understand desistance from crime in a more nuanced way.

Chapter V presents the data and methods that will be used in this dissertation. The data

analysis will rely on primarily quantitative methods. The quantitative analyses draw on an 18

year study with information on a wealth of personality, psychological, neurocognitive and social

factors. The Rutgers Health and Human Development (HHDP) was initiated in 1979 in order to

examine the developmental processes associated with alcohol and drug use (Pandina et al.,

1984). It also contains information on delinquency and crime from childhood into adulthood (age

12 to 30/31), making it ideal for the purposes of this dissertation. This is followed by measures

identified in the HHDP that may be used to operationalize each maturation component.

Covariates and delinquency/crime items are also discussed in this section. This chapter also

provides the framework for how the analysis will unfold in the dissertation.

Chapters VI through VIII contain the results of the analyses conducted to explore

maturation over time as well as its relationship to crime. Chapter VI describes the results of

crime by sex and race as well as over time. Chapter VII focuses on the relationship of maturation

over time, showing that maturation tends to increase over time. The main analyses are presented

in Chapter VIII, which describes growth curve analyses predicting crime over time with

maturation. This chapter will explore, in a comprehensive manner, how changes in levels of

maturation as well as overall maturation levels influence behavior over the life-course. The

analyses will mainly use growth curve models to examine how changes in levels of maturation

influence changes in criminal behavior. However, exploratory analyses will also seek to examine

how different domains of maturation impact crime. Finally, Chapter IX provides a discussion

27

and conclusion, summarizing the dissertation and pointing to areas in which the results have

implications for policy and theory.

28

CHAPTER II. MATURATION & CRIME: A HISTORICAL REVIEW

Maturation is a complex process and concept. It embraces the development of a stage of physical, intellectual, and affective capacity and stability, and a sufficient degree of integration of all major constituents of

temperament and personality to be adequate to the demands and restrictions of life in organized society (Glueck and Glueck, 1968: 176)

Introduction

In this chapter, I first trace early to mid-20th century work on the theory of maturational

reform. Prior to the 1970s, maturation represented the major explanation for why individual

offenders, by and large, stop committing crimes over the life-course. The most prominent

proponents of this perspective were the Gluecks who provided perhaps the only theoretical

explanation of desistance at this time. However, a lack of interest in aging and crime along with

a somewhat vague conceptualization of maturation by the Gluecks and others (e.g., Winick,

1962) led to the demise of this theory. Since this time (especially in the last 20 years), work on

desistance from crime has increased tremendously. This work is the subject of the next chapter,

which discusses definitions/measurement of desistance and theories of crime over the life-course,

which may serve as a foundation for an updated concept of maturation.

Historical Perspective: The Gluecks

The concept of maturation as an explanation for desistance was used by Sheldon and

Eleanor Glueck (1974, chapter 13) in an earlier era of criminology. They were clear that

maturation did not just mean aging, but their attempts to explain exactly what it did mean

were not completely successful. In fact, the principal evidence for maturation appeared to

be the reduction of offending, and - as many people have pointed out - that is unhelpful

29

because it is tautological. So maturation as an explanation for desistance lost credibility

within criminology.

Anthony Bottoms, interview, 20116

In work that spanned over 40 years, Harvard criminologists Sheldon and Eleanor Glueck

advanced the notion that the decrease in crime with age was a result of maturation (see Glueck

and Glueck, 1937; 1940; 1945; 1970; Glueck, 1964). The Gluecks’ main argument was that after

a certain period of time, criminal behavior slows down naturally and that it is not due primarily

to ‘environmental influences’ (Glueck and Glueck, 1974; Sampson and Laub, 2003). The idea

was that as the individual matured, he or she began to make more responsible decisions and

understand that “crime does not lead to satisfaction” (Glueck and Glueck, 1974: 170). The

Gluecks rejected the notion of “ontological” or developmental, law-like maturation (Lewontin,

2000; as cited in Sampson and Laub, 2005b). That is, maturation is not something that happens

according to a pre-defined process, in which, for example, at a particular age, individuals become

‘adults’. Rather, people can (and do) mature at different ages and stages of the life-course and

some fail to mature—in the full sense of the word—at all. As will be argued below, it is not clear

that the Gluecks’ critics recognized these nuances in their theoretical framework.

For example, they stated: “not age per se, but rather the acquisition of a certain degree of

what we have called ‘maturation’ regardless of age at which this is achieved among different

groups of offenders, is significantly related to changes in criminalistic behavior once embarked

upon” (Glueck and Glueck, 1945: 84). Thus, depending on when the individual begins to

develop markers of adulthood, he or she may persist or desist at different ages. Perhaps most

6 Interview text can be found here: http://crimelink.nl/analyse/groot-interview-met-anthony-bottoms-over-

desistance

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controversially, the Gluecks argued that age of onset of delinquency plays a role in the point at

which maturation occurs. They suggested that the ‘criminal career’ generally has a similar length

or duration for offenders. Thus, the earlier the onset of a delinquent career, the earlier maturation

into adulthood occurs. However, the evidence seems to contradict this assertion; those whose

delinquent behavior begins earlier in life tend to have longer careers, on average (see Farrington,

1992; 2003; Laub and Sampson, 2001; Piquero et al., 2003; 2007; Wootton, 1962).

Of interest for the purposes of this dissertation, the Gluecks concept of maturation was

multifaceted, involving more than biological changes. The Gluecks viewed maturation as

consisting of “physical, intellectual and affective capacity and stability, and a sufficient degree of

integration of temperament, personality and intelligence” as well as an ability to function in

society (Glueck and Glueck, 1968; 1974: 170). In their view, biological development as well as

social relationships (e.g., marriage)7 each contributed to the maturation process (Glueck and

Glueck, 1937). With respect to social factors, when discussing the possible reasons why aging

impacted offending, the Gluecks argued that as individuals aged, their ‘environmental

conditions’, family relationships, and work habits improved. In addition, their recreational time

was spent in more structured activities (see Glueck and Glueck, 1937: chapter X). Thus,

maturation did not, for the Gluecks, simply imply biological processes (or the ‘inexorable aging

of the organism’—Gottfredson and Hirschi, 1990) that occur in the same way for every

individual (Laub and Sampson, 1991; but see Maruna, 1997). But they did suggest that

maturation was “normative” for most people (see Sampson and Laub, 2003: 300). They also

speculated that people may not fully mature during the normative years (e.g., early 20s) because

of inadequacies in early development in the family and in school as well as mental deficiencies

7 This is a key argument and one that will help tie together the strands of desistance theory (e.g., Sampson

and Laub, 1993; 2003; Giordano et al., 2002; 2007; Maruna, 2001) into a maturation argument.

31

(Glueck and Glueck, 1968). As will be discussed below, their theory, while not without

significant shortcomings, seemingly anticipated many of the recent advances in the

developmental sciences.

The use of maturity as an explanation of crime and desistance was not confined to the

work of the Gluecks during the mid to late 20th century. For instance, Banay (1943) analyzed a

sample of prisoners and concluded that they suffered from what he called “emotional

immaturity” with several characteristics commonly found in “pre-adolescent children” (1943:

173). Similarly, Roper (1950) noted that “[c]rime is essentially the solution of personal problems

at a childish level of conduct” and that “it is apparent that crime is something that people tend to

grow out of as people mature and lose their childish attitudes” (1950: 18-19, emphasis added).

Thorsten Sellin (1958) reviewed the literature on aging and crime and concluded that maturation

was a factor in the decline of antisocial behavior over the life-course. In addition, Winick (1962)

applied maturation theory to the aging out of narcotics use. He argued that part of the maturation

process involved growing out of problems that led to antisocial behavior (as a method of coping)

and ‘emotional homeostasis’ (p 5). None of these writers, though, advanced our understanding of

what maturation entails and how it is related (independent of age) to desistance.

Criticisms

Despite the seemingly increasing support for a ‘maturational reform’ explanation of

desistance, the Gluecks were criticized for their theory (see Greenberg, 1977; Laub and

Sampson, 1991; Shover, 1985; Sutherland, 1937; Wootton, 1962; Wilkins, 1969). It seems

reasonable to suggest that these critiques, along with its vagueness and outwardly ontological

32

character8, led to the demise of this theory. Perhaps the most vocal critic of the maturation theory

was Barbara Wootton (1962). In essence, Wootton’s critiques boiled down to two points: the

Gluecks maturation theory a) added nothing to the literature on age and crime and b) was circular

or tautological (see also Laub and Sampson, 1991). She argued that the Gluecks had posited a

law-like or mechanical process of the criminal career (see Sampson and Laub, 1993). If the

Gluecks’ theory was not ontologically oriented, then it lacked meaning:

If, however, the maturation theory does not imply a roughly constant process of maturation which is irrespective of the offender’s chronological age, what meaning can it be said to have at all? The discovery that ageing ‘turned out to have played a significant role in the process of improvement with the passage of the years’ (Glueck and Glueck, 1945: 78) then becomes merely a rather pompous way of saying that with the passage of the years the subjects both grew older and behaved better. This, however, we knew already: indeed, the fact that people tend to reform as they grow older is just what we are out to explain (1962: 163). More damaging to the Gluecks’ theory, however, she also argued that their explanation

was circular (e.g., only argued that once a person stops offending, they have reached maturity)

and that it is not an explanation, but a description of something that needs to be explained.

Again, in her words (1962: 164):

The maturation theory of criminality is thus reduced to nothing more than a high falutin’ way of saying what has all along been obvious—viz: that a minority of young criminals become recidivists, while the majority do not. It is in fact one of the –unhappily not infrequent—occasions when a label has been mistaken for an explanation. While certain of Wootton’s criticisms were well-founded, in my view the Gluecks’

theory is more viable than she and other critics argued. For example, the notion of “maturational

reform” is not necessarily tautological (they did attempt to define maturation independently of

criminal behavior). In addition, their notion of maturation seemingly foresaw several

8 The Gluecks’ explanatory framework is, for example, often lumped together with that of Gottfredson and

Hirschi’s (see chapter II of this dissertation) which implies that maturation is mostly a biological or ontological phenomenon (see Laub and Sampson, 2001; Maruna, 2001). I argue this is a misconception.

33

developments in criminology, cognitive psychology and neurological sciences that have recently

helped to advance our understanding of behavioral change in adulthood.

Rehabilitating Maturational Reform Theory

As will be argued, the dismissal of the Gluecks’ use of maturation to explain desistance

may have been premature.9 It is true that the theory was somewhat vague, tautological, and

needed clarification. Yet, the Gluecks argued that more work needed to be done to better

conceptualize the meaning and measurement of maturation. They specifically suggested that

future researchers take up where they left off and “dissect maturation more deeply into its

components,” possibly creating an ‘M.Q.’ (maturation quotient) (1940: 270; Glueck and Glueck,

1943). While this instrument was to be used to determine whether an individual had reached age-

appropriate stages of maturation, it also suggested that maturation is multifaceted and in need of

further clarification.

To date, few, if any, researchers have heeded this call. Part of this reticence may be

attributed to the peculiarly American notion of the immutability of criminality. Maruna (2001)

describes a US publisher’s discomfort with Anthony Burgess’ last chapter of A Clockwork

Orange, in which Alex, the very picture of criminality, matures after age 21. This chapter was

deleted in American versions of the book because of a fear that the public would not accept that

9 As Laub and Sampson (1991: 1426-1429) argue, there may have been numerous reasons that the Gluecks’

work and theory were dismissed nearly wholesale by criminologists. Among the reasons they list are 1) the Gluecks did not have graduate students to continue and promote their research tradition; 2) the Gluecks were “antitheoretical” in their research; 3) the Gluecks downplayed (largely) sociological variables; 4) the Gluecks were extremely awkward socially; 5) the Gluecks’ concern with social policy rather than sociological criminology and 6) perhaps most important for the purposes of this dissertation, “the Gluecks had a tendency to infuse their work with moral statements that reflected a middle class bias” (p. 1426-1427). The idea that delinquents or criminals were less “mature” than non-criminals might have appeared to mainstream criminologists, especially at a time when the ‘normalization of deviance’ was increasing in popularity, to be a biased and moralistic view. However, if maturation is meant to refer to individual development and attainment of traditional adult status, the concept does not have to maintain the moralistic connotations.

34

such a change could occur to a hardened deviant. In any case, as Shover argues (1985: 77; see

also Maruna, 2001), research on maturation and crime has “not progressed appreciably beyond

[the Gluecks] work.” For the most part, recent scholarship only mentions maturation in reference

to previous perspectives, or in a limited sense (see, e.g., Graham and Bowling, 1995; Laub and

Sampson, 2001; Maruna, 2001; Kazemian and Maruna, 2009; Sampson and Laub, 2003);

researchers have not attempted to fully flesh out the concept in a theoretically and empirically

meaningful manner to explain desistance. Other work outside of criminology has examined

maturation and deviance but in a somewhat narrow way (see Cauffman and Steinberg, 2000;

Monahan et al., 2009). Thus, the study of desistance from crime has been “re-discovered” in

recent years, with scholars developing and testing isolated theories without exploring possible

connections to the Gluecks’ early work. No work has attempted to delineate the domains or

“components” of maturation. The next chapter discusses more recent work on desistance from

crime, including definitional, measurement and theoretical perspectives. This work, as will be

argued, may provide a foundation for rehabilitating maturational reform theory using a

multidimensional perspective (that is, they identify possible “domains” of maturation). That is,

while most of the theories or perspectives have been offered as competitive or mutually

exclusive, they may profitably be seen as part of a larger developmental framework—one that

may be more powerful as an explanation of desistance from crime than any one theory in

isolation.

35

CHAPTER III. THE STUDY OF DESISTANCE IN CRIMINOLOGY

Defined as ceasing to do something, "desistance" from crime is commonly acknowledged in the research literature. Most offenders, after all, eventually stop offending. Yet there is relatively little theoretical

conceptualization about crime cessation, the various reasons for desistance, and the mechanisms underlying the desistance process

(Laub and Sampson, 2001: 5).

Introduction

This chapter will review recent criminological explanations of desistance as well as

related developmental work. These literatures serve as the foundation of an updated, multi-

faceted conceptualization of maturation and its impact on crime. As mentioned in the previous

chapter, the decline in crime in adulthood and eventual desistance did not become a specific

research concern until the late 20th century. This increasing focus on desistance was, in no small

measure, a consequence of the criminal career “great debate” (Bernard, Snipes and Gerould,

2010; Paternoster and Bushway, 2009).

In the 1980s, the criminal career debate took place within criminology regarding the

importance of examining lives over time. On one side of the debate, researchers argued that the

‘criminal career’ is comprised of distinct elements (e.g., onset, frequency, duration, desistance)

that needed to be examined separately in order to maximize the policy relevance of criminology

(see Blumstein et al., 1986; 1988; Farrington, 1992; Marshall, 2009). This approach virtually

requires the use of longitudinal data (Blumstein et al., 1988) and suggests that desistance from

crime is a topic that warrants special study (Paternoster and Bushway, 2009). On the other side

of the debate were the “population heterogeneity” advocates (a term popularized by Nagin and

Paternoster, 1991) such as Gottfredson and Hirschi (1990) who argued that the correlates of the

various components of the criminal career are the same, and thus longitudinal research is not

necessary (Hirschi and Gottfredson, 1983; Gottfredson and Hirschi, 1986; 1987; 1990).

36

While this debate has continued in criminology with no clear victor, one thing is

indisputable: it helped to spark new and important research, both empirical and theoretical in

criminology. In particular, research examining why and how individuals involved in crime

eventually stop offending (i.e., desistance) has increased dramatically over the last 20 to 30

years. This chapter will begin with a brief, but necessary, discussion of what desistance means

and how it has been operationalized. The chapter then examines criminological explanations of

desistance from crime. It next discusses developmental research related to adult crime and

desistance and includes a brief review of recent brain maturation research. It concludes with a

discussion of how the various explanations of desistance are related and less competitive than

their supporters have argued.

What Is Desistance?

Definitions and Measurement

In recent years, researchers have begun to seriously consider the definition of desistance

in their work. Desistance is not an observable phenomenon that is amenable to empirical study

(see Maruna, 1997; 2001). As Laub and Sampson point out (2001; see also Maruna, 2001),

desistance is not as easy to operationalize as other outcomes because it is not the presence of

something but rather its absence that defines it. According to Kazemian (2007), the differing

definitions of desistance, and differing types of data (e.g., relying on self-report or official

records) used to measure desistance often lead to inconsistent results (for a list of different

definitions used in the literature, see Kazemian, 2007: 9; see also Massoglia and Uggen, 2007).

Researchers have used differing definitions of desistance. For example, Maruna (2001)

defines desistance as “the long-term abstinence from crime among individuals who had

37

previously engaged in persistent patterns of offending” (2001: 26). In this sense, desistance is not

something that can be adequately measured in the short-term or by using cross-sectional data.

How much time is required to “see” desistance is not a settled question (Laub and Sampson,

2001; 2003; Maruna, 2001).

Others have defined desistance as a decline in the level of offending over time (Massoglia

and Uggen, 2007; Mulvey et al., 2004). Paternoster and Bushway (2009) formally define

desistance as the point at which offending reaches a level that is “not significantly different from

zero” (2009: 1110; see also Bushway et al., 2001). This suggests that desistance occurs when the

likelihood of crime for offenders is indistinguishable from that of non-offenders.

The measurement of desistance has varied in published work, from somewhat subjective

“cut-points (e.g., five or ten years without an offense), to assessments of whether one’s offending

has decreased over some time period (see Laub and Sampson, 2003; LeBel et al., 2008;

Kazemian, 2007; Kazemian and Maruna, 2009; Massoglia and Uggen, 2007). While the majority

of desistance research uses official records (e.g., arrests; see Kazemian, 2007), self-reports may

be better able to avoid possible biases associated with making inferences about behavior based

only on incidents known to the criminal justice system. According to Stouthamer-Loeber et al.

(2008) using official records may bias estimates of the overall amount of offenses downward

while self-reports are likely to undercount more serious offenses. Thus, the decision to use self

versus official records should, in part, be influenced by the offense under study and the research

question.

Desistance as a Process

38

The work of Bushway and colleagues in particular, has helped to formally and

statistically define and measure desistance using longitudinal trajectory models (see Bushway et

al., 2001; 2003). Bushway and colleagues (2001; 2003) use poisson-based semiparametric

models which examine offending trajectories for groups of individuals. Desistance, then, is

modeled as a reduction (to a near zero level) in offending over time. The method was based on

that of Nagin and Land (1993), which makes no assumptions about the shape of the trajectories

and allows an explicit modeling of factors related to change and to group based differences. This

provides an empirical method of modeling desistance rather than devising arbitrary cut-off points

and assigning those crime-free for that period as desisters.

Bushway and colleagues view desistance as a “developmental process that unfolds over

time rather than a static state that is achieved” (Bushway et al., 2003: 133). This method

essentially removes the problem of how long one must be “crime free” to be considered a

“desister.”10 Bushway and colleagues are also clear that desistance involves decreases in

‘criminality’ (the individual’s potentiality of committing criminal acts) rather than just incidents

of criminal behavior. In other words, something about the person changes over time, leading to a

cessation of offending. Examining desistance as a process also highlights the importance of

understanding what changes are occurring at the same time as desistance.11 Thus it is essential to

study offenders as they desist rather than solely after the fact (Kazemian, 2007; Maruna, 2001).

By doing so, we can better understand desistance as a process, including the ability to examine

10 Bottoms and colleagues (2004) point out that dictionary definitions of “desistance” often include the term

“abstain”, which implies that using a significant time period to gauge desistance (e.g., crime free for 10 years) may be appropriate. I follow the Bushway method because criminologists, not dictionaries seem to have reached an agreement that desistance is conceptually (and theoretically) different from periodic termination.

11 According to Paternoster and Bushway (2009) and Bushway et al. (2001), it was Fagan’s (1989) insight that led to researchers considering desistance as a process rather than an event or singular point in time. Fagan (p. 380) suggested that desistance is the “process of reduction in frequency and severity” of offending, “leading to its eventual end.” It is interesting to note that developmental theorists have long considered the “transition to adulthood” as a process also (see Hogan and Astone, 1986).

39

between-individual variation in how and when individuals move away from crime. Thus, the

way in which desistance is defined also has implications for the type of analysis used (Bushway

et al., 2001; 2003).

Why Do They Stop? Criminological Theories Of Desistance

In the criminological literature, several researchers have published comprehensive

reviews of desistance research. Many of these works have come since Laub and Sampson (2001)

lamented the lack of theoretical focus on the topic (see chapter opening quotation). These

reviews cover extant explanations or theories of the age-crime curve or desistance from crime

(see Laub and Sampson, 2001; Maruna, 2001; Kazemian, 2007; Kazemian and Maruna, 2009;

Sampson and Laub, 2003). This section will discuss criminological explanations of desistance

from crime, focusing on those proffered in the last twenty years. In doing so, I group theories or

explanations into several categories. This exercise follows the lead of prior work (e.g., Laub and

Sampson, 2001; Maruna, 2001). The categories of desistance theory discussed below are as

follows: pure-age and biological theories, psychosocial theories, and sociological theories.

Within each category are specific explanations of behavioral reform. The purpose of this

discussion is to briefly review extant theories of desistance which, I argue, serve as a basis for an

integrated perspective on maturation and crime.

Pure Age or Biological Theories of Desistance

Pure age and biological theories suggest that individual change in behavior is due either

to unspecified processes associated with aging or physiological changes that occur as people

grow into adulthood and old age. Pure age theories suggest that age itself is the reason that

40

desistance occurs—it is a “law” of nature. Biological theories focus on physiological factors

often to the exclusion of social or psychological processes. These perspectives have different

assumptions than other, more social or psychological theories. For example, it is reasonable to

suggest that they are more deterministic than other theories in positing a direct link between

physiological factors and behavior. Many also imply that antisocial behavior results from

neurological or physiological abnormalities. In this section, pure age-based theories are reviewed

(which suggest that the change in behavior over the life-course that researchers have observed is

simply due to age itself) followed by neurological/physiological work.

Pure Age-Based Theories

Some researchers have offered explanations of the age-crime curve that are based solely

on age itself. Although it may be easy to misinterpret earlier work by Sheldon and Eleanor

Glueck as suggesting that age is a direct cause of crime (for example, their statement that “aging

is the only factor which emerges significant factor in the reformation process”12 (Glueck and

Glueck, 1937: 105), these scholars did not view age as solely causing desistance (but see

Maruna, 1997; 2001; Shover and Thompson, 1992). As is argued in the subsequent chapter, their

notion of maturation was correlated with age; however, they argued that “it is not the arrival at a

certain age” but other personal and social changes that influenced behavioral reform.13

More explicit age-based explanations have been provided by Wilson and Hernnstein

(1985) and Gottfredson and Hirschi (1990; Hirschi and Gottfredson, 1983; 1995). Gottfredson

12 The Gluecks followed this line, on the next page, with the assertion that they did not know what aging

meant but that it likely involved “biological or psychological or social” factors. In some sense, the early writings of the Gluecks can be interpreted to equate aging with maturation and they often used the two words interchangeably. My argument, however, is that they did not suggest that aging was the cause of desistance but rather the “maturation that accompanies it” (1937: 106).

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and Hirschi, for example, argue that because of the overall similarity of the age-distribution of

crime across place and time, no social or cultural variables can explain it. Gottfredson and

Hirschi (1990) suggest that the decline in crime over the life-course is due to the “inexorable

aging of the organism” (1990: 141). According to these authors:

An…interpretation of maturational reform or spontaneous desistance is that crime declines with age. This explanation suggests that maturational reform is just that, change in behavior that comes with maturation; it suggests that spontaneous desistance is just that, change in behavior that cannot be explained and change that occurs regardless of what else happens (1990:136, citations omitted, italics added). Similarly, Wilson and Herrnstein (1985: 145-146), while recognizing the impact of

changing social situations across the life span, write: “[w]hy does age affect crime? It is not hard

to find or invent explanations by the dozen.” Yet, according to them, none of these explanations

(e.g., education, marriage) are sufficient to account for the age effect on crime. In the end, they

state, “[a]ge, like gender, resists explanation because it is so robust a variable.” Thus, age

directly impacts crime (e.g., has a non-spurious relationship with crime), a conclusion that has

been met with considerable controversy by scholars who wish to see the relationship of age and

crime as an indirect effect (see Greenberg, 1977; 1985).

The implication of the ‘pure-age’ perspective on desistance is that maturational reform is

“normative” in that it happens for everyone and it happens at generally the same rate. Thus,

factors that vary across individuals (such as social relationships) do not have a significant impact

on behavioral reform. The perspective suggests that offending is a natural phenomenon, just as is

desistance. There appears to be an element of a “burn-out” effect in these theories, in which it is

argued that people begin to be less physical with age and thus engage in less physical activity

(such as baseball or criminal behavior). However, the notion that age has a direct impact on

behavior does not preclude the argument that changes caused by or coincident with aging

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directly affect crime. Hirschi and Gottfredson (1983) for example, argued that the age

distribution of crime cannot be accounted for by any variable or combination of variables

currently available to criminology (p.554). Thus age is not, in their perspective, a mystical

concept that is not explainable. Unfortunately, without specifying what factors change with age

that lead to desistance, age as a causal variable lacks clear meaning (see Maruna, 1997).

Biological Perspectives: Cognitive and Neurological Work

In recent years, an emerging body of research has indicated that, rather than the brain

being fully mature before adolescence, cognitive/neurological development occurs through

adolescence and emerging adulthood. Contrary to previous belief, the brain appears to be

continuing to develop beyond childhood and into the early 20s (Geidd et al., 1999). The major

changes in brain maturation appear to be occurring in the prefrontal cortex of the brain, which is

responsible for controlling impulses and decision-making (Steinberg, 2008; 2010). Researchers

have found evidence of increasing myelination of synapses in the brain, linear increases in white

matter through adolescence and non-linear decreases in grey matter through an individual’s early

20s (Geidd, 2008; Gotgay et al., 2004; Paus, 2005; Sowell et al., 2001). All of these changes

appear to be associated with improved brain functioning, including increased speed of

information transfer leading to better decision-making and impulse control (Spear, 2007;

Steinberg, 2008).

The evidence, mostly from functional MRI studies, indicates that several (possibly

related) structural changes continue to occur in the brain throughout adolescence. The

importance of white and grey matter relates to the speed of information processing which assists

decision-making. Paus et al. (1999: 1908) explain:

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[t]he smooth flow of neural impulses throughout the brain allows for information to be integrated across the many spatially segregated brain regions involved in these functions. The speed of neural transmission depends not only on the synapse, but also on structural properties of the connecting fibers, including the axon diameter and the thickness of the insulating myelin sheath.

Thus, an increase in white matter (which is a fatty substance that coats the neuronal tracts) helps

to improve cognitive functioning.

However, to date, this cognitive or neurological work has not been incorporated into the

desistance literature (Collins, 2004). Indeed, as Byrne and Lurigio (2008: 321-322) argue:

Advocates of life-course theory have not fully considered the implications of recent neuroscience research showing that the brains of adolescents and young adults are still developing, especially in the region that governs the executive function and contains the instrumentality that controls impulses and calculates risk and future consequences. These changes in brain maturation are very likely to be implicated in behavioral reform

over the life-course (Blonigen, 2010; Steinberg, 2008; 2010; Spear, 2007). For example,

according to Restak (2001: 76, quoted in Walsh, 2008: 161), “the immaturity of the adolescent’s

behavior is perfectly mirrored by the immaturity of the adolescent’s brain.” It is important to

consider cognitive changes as part of the maturation process that leads to desistance from crime.

Some have linked changes in brain functioning with changes in other physiological processes,

such as the production of testosterone (Collins, 2004; Walsh, 2008). That is, brain maturation

may be related to behavioral control due to associated changes occurring throughout the body, in

addition to improved executive function. While brain imaging technologies (e.g., functional

MRI) may not be available in most longitudinal studies of offending, proxy measures such as

neurocognitive and personality tests can provide valuable information concerning the effect of

brain development on behavior. Unfortunately, few studies have sought to measure cognitive

maturation in work on desistance from crime.

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Psychological and Psychosocial Theories

In this section, I review psychological or psychosocial theories. The latter term,

psychosocial, is preferable because it acknowledges the dual role that society and psychology

have in the unfolding of behavior. For example, psychosocial theories often discuss changes in

personality or attitudes toward social roles. These changes are likely to occur with exposure to or

adoption of new social roles. Three different types of theories are discussed in this section. First,

rational choice or changes in decision-making theories are reviewed. These theories assume that

as individuals age, they become better at cost-benefit analysis and thus more rational. Second,

cognitive transformation or identity theories are discussed. These theories attribute changes in

antisocial behavior with age to a transition to a more conventional adult conception of the self.

People come to view themselves as non-criminal and take action to bring their behavior in line

with their identity. Third, psychosocial maturation theories are discussed. These theories suggest

that changes in personality (from more to less impulsive, for example) lead to increasing

prosocial behavior.

Changes in Decision-Making and Desistance

Rational choice theories assume that individuals make decisions in a “rational” manner,

within structural constraints. Thus, the parlance of “decision-making” enters into explanations of

crime and desistance in that those who desist make a conscience choice to do so. In large

measure, it appears that theories of rational choice and desistance argue that psychological

processes change over time and this influences decision-making and cost-benefit analyses by

individuals (Shover, 1985; Shover and Thompson, 1992). This does not rule out the notion that

social structure or experiences impact decision-making, however, Rational choice theories (RCT)

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break down crimes into two separate decisions: 1) decision to commit crime (criminal

involvement) and 2) criminal event decisions (target selection). The decision to desist is also a

component of the perspective, suggesting that offenders come to a point at which they view

crime as no longer worth the costs and make a decision to stop (see Cornish and Clarke, 1986).

RCT explanations for desistance are less frequent but typically involve a similar process by

which the offender comes to see crime as “not worth the risks” (Cusson and Pinsonneault, 1986).

Theoretical work has pointed to two main reasons why an offender would give up crime: 1) as a

result of a shock and 2) delayed deterrence (Cusson and Pinsonneault, 1986: 74). Cusson and

Pinsonneault argued that over time, offenders come to view the negative consequences of a life

of crime as increasingly aversive. They realize that their chances of being caught and imprisoned

are greater than they once thought, and with advancing age come to feel that they can no longer

afford to spend years of their life behind bars.

Shover (1996; see also Shover, 1985; Shover and Thompson, 1992) offers an updated

model of desistance as a rational choice within the delayed deterrence framework. He argues that

with age, certain offenders begin to question their life track, start looking ahead rather than in the

moment and prison becomes something to fear (see also Cusson and Pinsonneault, 1986: 76). In

other words, according to Shover, decisions become more rational with age, they finally stop

“pretending” and attempt to lead a conventional life (Shover, 1996). Shover has argued “that

aging improves offenders’ ability and inclination to calculate more precisely and carefully…and

the result is an increased probability of desistance” (Shover and Thompson, 1992: 90).

In a sense, the rational choice perspective with respect to criminal desistance is related to

the work of Tversky and Kahneman (1974). These psychologists, over a number of years,

argued that decision-making is not as utilitarian as many economists might have assumed.

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Instead of behavior being determined by a combination of expected benefits and costs

(derived by the probability of success or failure multiplied by the expected payoff or

penalty), individuals bring their own biases and heuristic shortcuts to each action. They argue

that people may overestimate the representativeness or how frequently a given situation

would be to arise. These misconceptions lead to biases in judging the probability of success

or failure of a particular action (see also Kahneman and Tversky, 1996). Individuals who

engage in crime may experience such biases, but those biases may change over time. For

example, Shover and Thompson (1992) argued that individuals become more adept at

judging the relative pay off and potential penalties associated with crime as they gain

experience. Recent neurocognitive work (reviewed above) supports the notion that

adolescents are more prone to errors in decision-making relative to adults (Geier and Luna,

2009). That is, as individuals age into adulthood, their cognitive processing abilities allow

them to make better cost-benefit calculations that favor less risky behaviors.

The rational choice perspective on desistance implies that individuals change their

behavior due to changes in how behavior is seen to benefit the person or how costly it is. In other

words, when offenders are active, they (however unconsciously) view offending as worth the

risk that it entails. The pay-offs include thrills, social status, and material goods. As offenders

age, however, their notion of the cost-benefits of criminal behavior changes such that crime no

longer seems worthwhile. This line of work is interesting in that it suggests that individuals

become less impulsive and more future oriented over the life-course—that is, they gain

discipline. However, the main point is that rational choice theories center on the notion of choice

and purposeful action as the major factor in desistance and behavioral reform. Longitudinal

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research examining this proposition, however, is scarce and rational choice perspectives on their

own fail to adequately explain why individuals change in their decision-making over time.

Cognitive Transformation, Agency and Identity Theories

The use of qualitative or narrative data has led to theories of desistance suggesting that

changes in the “self” lead to changes in behavior. These theories argue that desistance occurs

when offenders no longer regard themselves as criminals. Identity explanations are related to

rational choice (see above) in that they view offenders as making a determination (decision) to

achieve a particular goal (new self). These theories are cognitive-based, subjective explanations

of how individuals change their outlook on themselves (Maruna, 1997; 2001; Proctor, 2009;

Paternoster and Bushway, 2009; Rumgay, 2004; Vaughn, 2007). For example, Maruna (2001)

argues that individuals who have given up crime essentially reshape their perception of their past

selves in order to conform to who they believe they are now—a sort of cognitive ‘rescripting’.

Identity changes or cognitive shifts may result in the actor purposely pursuing different lines of

behavior, ultimately leading to desistance.

Giordano and colleagues (2002; 2007) have offered perhaps the most compelling theory,

taking into account gender dynamics and structural changes to explain how identity constructions

are paramount in desistance stories. They suggest that the environment provides a “scaffolding”

or “hooks for change” that can facilitate desistance, but the individual ultimately must do the

work (Giordano, et al., 2002: 1000). According to Giordano et al. (2002) individuals can have

several types of cognitive shifts that make desistance more likely to occur: 1) they can become

more “open to change” in behavior and in lifestyle choices; 2) they may change in how much

exposure they have to prosocial institutions or “hooks for change”; 3) they may begin to see

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themselves in a different light—they envision a “replacement self” that virtually requires a

change in behavior; and finally 4) they may alter the way they come to view crime or deviance

(e.g., from acceptable to something unacceptable) (Giordano et al., 2002: 1000-1003).

Recent theories of identity and desistance support the view that desistance is caused by a

change in the way individuals view themselves (Paternoster and Bushway, 2009; 2011; Ward

and Marshall, 2007). These theories in essence suggest that the life event or structural condition

(work, marriage, etc.) is not what is important, but rather it is the individual’s “openness to

change.” This idea is related to psychological and personality life-span work that examines

changes in personality traits over time as a predictor of change in behavior (see Bloningen et al.,

2008; Bloningen, 2010; Caspi et al., 2010). A recent theory of desistance was advanced by

Paternoster and Bushway (2009). Their explanation suggests that at a certain point, offenders

begin to reflect on their lives and their current “selves” in a critical manner. The offender

imagines his/her future self as something unsatisfactory. The offender begins to recognize

several domains of life failure and this serves as a catalyst for change:

When these life dissatisfactions become linked to one’s criminal identity, they are more likely to be projected into the future, and the person begins to think of his or her “self” as one who would like to change to be something else. This perceived sense of a future or possible self as a nonoffender coupled with the fear that without change one faces a bleak and highly undesirable future provides the initial motivation to break from crime. Movement toward the institutions that support and maintain desistance (legitimate employment or association with conventional others, for example) is unlikely to take place until the possible self as non-offender is contemplated and at least initially acted upon (2009: 1105). Thus Paternoster and Bushway clarify their theoretical stance by suggesting that it is

identities which change first, and then prosocial institutions come into play. However, in the

desistance literature, it appears that the “chicken or egg” (is it the change in identity that leads to

say, the marriage effect on crime or marriage leading to changes in identity) problem has yet to

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be solved (LeBel et al., 2008; see also Farrall and Bowling, 1999; Vaughn, 2007). Further, if

identity change is the important theoretical process, we need to know why such shifts occur. To

date, research has been largely silent on this issue. Maruna (2001) for example, focuses on

“redemptive scripts” in which offenders make sense of their deviant pasts which allows them to

take the next step towards conformity. But this begs several questions: why did they feel the

need to remake their life script? Have they always wanted to go straight? If not, what changed?

The cognitive transformation and identity theories emphasize individual changes as the

major cause of desistance. They suggest that rather than external forces leading to behavioral

reform, psychosocial changes in how offenders see themselves as well as how they view criminal

behavior affects desistance. Interestingly, they incorporate a mixture of ontogenetic, individual

development and purposeful choice in their explanations of desistance. Thus, first individual

development occurs which changes how offenders view themselves and the world. Next, “human

agency” or the idea that behavior is the result of thoughtful decision-making (Paternoster and

Pogarsky, 2009) and will (Matza, 1964) are put into action allowing offenders to stop

committing crimes. That is, individuals take action so that their behavior is consistent with how

they view themselves (Maruna, 2001).

Psychosocial/Personality Theories

An increasing number of studies within developmental psychology have begun to explore

what is sometimes referred to as “psychosocial maturity.” This concept has multiple

interpretations, dating to its introduction in the mid-1970s (see Greenberger et al., 1975;

Greenberger and Sørenson, 1974; Greenberger, 1984; Greenberger and Steinberg, 1986).

Cauffman and Steinberg (2000) have argued that maturity of judgment (part of psychosocial

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maturity) is lower for young people than for adults (which could partially explain the desistance

phenomenon) (see also Steinberg et al., 2009a). They operationalize psychosocial maturity by

using three main constructs: responsibility (being able to rely on oneself),14 temperance

(impulsivity, controlling aggressive behavior), and perspective taking (taking other’s point of

view into consideration, future or present orientation). While psychosocial maturity is not often

examined as a “desistance theory,” some work has shown that it is negatively related to crime

over the life-course (Modecki, 2008; Monahan et al., 2009). However, as Mulvey and colleagues

(2004) state, “there, unfortunately, is no substantial body of literature about psychological or life

changes among serious adolescent offenders that promote positive adjustment to early adulthood

and a cessation of antisocial activity” (2004: 216). Nonetheless, researchers are increasingly

considering psychological and personality changes as having a role in the desistance process (see

Blonigen, 2010; Blonigen et al., 2008; Giordano et al., 2002).

With respect to basic personality research, recent evidence is accumulating that

personality traits are not necessarily fixed entities as was once believed. While there is rank-

order continuity in personality through the life-course, personality researchers have found mean

(or aggregate) level and within-individual changes in traits over time. For example, research has

shown that several of the “big five” personality traits (Openness to new experience,

Agreeableness, Conscientiousness, Neuroticism, Extraversion) change with age. People tend to

become more agreeable and conscientious over time. In addition, studies have shown that ratings

of neuroticism decline with age (Adams, 2004; Block, 1971; Blonigen et al., 2006; 2008;

Blonigen, 2010; Caspi et al., 2005; Walsh, 2008). These findings suggest that changes in

personality may help explain the “normative” desistance from crime and problematic behavior

14 Interestingly, this construct includes self-reliance, clarity of the self (‘I know who I am’), self-esteem and

work attitudes (Cauffman and Steinberg, 2000: 747-748).

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phenomenon (Hirschi and Gottfredson, 1983; Laub and Sampson, 2003). Blonigen (2010: 98)

raises an intriguing prospect when he argues that “the age–crime curve, specifically the

component of desistance from late adolescence to early adulthood, derives from normative

maturation in personality traits linked to antisocial behavior, and that changes in these constructs

should be conceptualized within a theoretical framework that emphasizes their co-development

during this critical stage in the life-course.”15 Certain work supports this contention, specifically

with respect to alcohol abuse (Littlefield, Sher, and Wood, 2009; Littlefield, Sher, and Steinley,

2010).

Interestingly, Caspi and colleagues (2005) have suggested that changes in personality

traits affect other domains of the life-course (see also Gottfredson and Hirschi, 1990). While they

focus on how personality differences lead to differences in social relationships, achievement, and

health, the implication is that changes in personality traits (increases in agreeableness and

conscientiousness, for example) may facilitate the development of long-term, meaningful

relationships (e.g., marriage) and stable employment, which can contribute to desistance.

Social Process or Sociological Theories

Sociological theories of desistance take a different perspective than those previously

discussed. Whereas biological and psychosocial theories focus on within-individual change that

occurs as part of a (largely) natural process of development, sociological or social process

theories emphasize the importance of the external world in shaping individual lives and

trajectories of behavior. Certain versions of these theories (e.g., social control) bring with them a

different set of assumptions as well, namely that individuals would, without the assistance of

15 To be sure, Gottfredson and Hirschi (1990) were consistent with much personality research when they

suggested that self-control was a relatively stable trait throughout the life-course. However, they did hint that self-control changes over time may contribute to desistance (see pg. 107). Few studies have examined this prospect.

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social interventions, be naturally prone to antisocial (or at least asocial) behavior. Thus, for these

perspectives, society (in the form of relationships or social roles) provides a sort of proverbial

“lid” to natural human impulses. This section reviews two classes of social process theories:

civic reintegration and social relationship/social tie theories.

Civic Engagement and the Transition to Adulthood

Life-course theories of desistance often stress social processes (e.g., participation in

society and social relationships). A recent explanation of desistance in the criminological

literature suggests that a growing concern for society as a whole (rather than the self) is a factor

in behavioral reform. According to Farrall and Calverly (2006), individual attitudes toward

‘citizenship’ and the government influence desistance. They found that those who stop

committing crimes tend to want to be “good citizens” (e.g., they respect the government, accept

diversity, etc.). Uggen and colleagues (Massoglia and Uggen, 2003; Uggen et al., 2004; Uggen

and Inderbitzen, 2010) have suggested that what they call ‘civic reintegration’ is part of

constructing and maintaining a conforming lifestyle and identity. Acts such as voting and

community service (along with roles such as parenting and employment) help solidify for would-

be desisters that they are part of society and that they have reached “adult status.”

Along these lines, Massoglia and Uggen (2010; Uggen and Massoglia, 2003) have

suggested that desistance represents part of the process of becoming an adult. This process

includes becoming independent or self-reliant and other adult roles (e.g., marriage). They argue,

from a symbolic interactionist standpoint, that delinquency and criminal acts are inconsistent

with adulthood. Thus, much like getting married or attaining self-sufficiency are “traditional”

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markers of adult status, so too is desistance from crime. In this sense, desistance is not caused by

adult maturity, but rather a part of that process. They state (2010: 571):

Our kernel notion here is that movement away from delinquency is a distinct dimension of the transition to adulthood. With the unique and perhaps expected exception of parenthood, those who fail to desist generally fail to attain the markers of adulthood in a timely fashion and are not accorded adult status by others. Internalizing these appraisals, they come to see themselves as less than adults. In this conceptualization, desistance precedes or “predicts” adulthood. This is the

reversed causal ordering of the argument to be advanced in this dissertation. That is, as will be

discussed below, I argue that maturation (or the attainment of adult status) predicts or causes

desistance from crime. Nonetheless, Massoglia and Uggen (2010) highlight an important

notion—that desistance and the transition to adulthood are “tightly bound up” (2010: 572). In

this perspective, however, it is unclear whether the psychological or attitudinal changes that

come with adulthood are required in order for a) individuals to want to engage in citizenship

activities and b) for civic activities to translate to changes in behavior.

Social Relationship/Social Role Theories

Perhaps the most theoretically developed and empirically supported category of

explanations of desistance involves social relationships or social ties. Theorists have argued that

for identity change to result in behavioral change, social support is necessary. Some have

suggested that social processes may result in behavioral change even absent changes in

cognitions or the self (see Becker, 1960; Laub and Sampson, 2003; Sampson and Laub, 2005b).

Arguably, the leading proponents of social process desistance theory are Sampson and

Laub. Beginning in the late 1980s, Sampson and Laub reconstructed data from the classic Glueck

and Glueck ‘Unraveling Juvenile Delinquency’ study (1950; 1968) and developed a theory of

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informal social controls to explain crime and desistance (Laub and Sampson, 1988; 2001; 2003;

Laub et al., 1998; Sampson and Laub, 1990; 1992; 1993; 1994; 1995; 2005b). The basic thrust of

their life-course oriented theory is that the same factors (e.g., social bonds) that explain

participation in crime also explain exiting from crime (Loeber et al., 1991; but see Uggen and

Piliavin, 1998). They placed a large amount of emphasis on social processes such as military

experiences, incarceration, work, and marriage as key turning points in the life-course. That is,

the social bond (see Hirschi, 1969) to conventional institutions varies throughout life and this

variation can explain fluctuations in offending (Horney et al., 1995). For example, Sampson and

Laub (1993) found that strong marriages and meaningful employment was negatively associated

with crime in adulthood. These arguments have been largely supported in US based samples

(Beaver et al., 2008; Horney et al., 1995; King, Massoglia, and MacMillan, 2007; Laub, Nagin,

and Sampson, 1998; Laub and Sampson, 2001; Uggen, 2000) and internationally (Bersani, Laub,

and Nieuwbeerta, 2009; Savolainen, 2009). However, as Sampson and Laub point out, the

mechanisms underlying the relationship between social ties and desistance are not well

understood; that is, why marriage and employment should reduce crime is unknown (Laub and

Sampson, 2003; Sampson, Laub, and Wimer, 2006).

Social relationship theories suggest that criminal behavior results when social bonds are

weak or non-existent. This is a classical social control perspective (see Hirschi, 1969; Sampson

and Laub, 1995) in which offending is seen as natural and must be restrained by external forces.

However, more recent versions of social relationships theories have relaxed the strict control

theory interpretation and allowed that the impact of relationships may be multi-faceted. Laub and

Sampson (2003) argue that some of the impact of social relationships on crime is due to

restructuring of routine activities.

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Warr (1998) has suggested that marriage may be related to desistance because it removes

offenders from their criminal/delinquent peers (see also Akers, 2009; Maume et al., 2005). Laub

and Sampson (2001: 47) state: “It may well be that friendships change as the result of spouses

exerting social control on their mates. For example, wives may limit the husband’s number of

nights out with the guys.” Thus it remains the case that the meaning of social relationships such

as employment and marriage or cohabitation is subject to multiple interpretations. In addition, it

is unclear whether social relationships on their own are enough to spark desistance among those

who have engaged in criminal lifestyles. Some, for example, argue that psychological changes

are necessary before certain individuals will even be open and receptive to adult social

relationships (Giordano et al., 2002; LeBel et al., 2008).

Developmental Perspectives on Behavior over the Life-Course

There is a long line of research in developmental psychology that bears directly on issues

of continuity and change in behavior. Much of this literature is relevant to, but has not been

incorporated in the more sociologically-oriented desistance literature reviewed above (but see

Moffitt, 1993). It is important to provide at least an introduction to this field in order to illustrate

the links between desistance research and the notion of maturational reform.

In general, developmental psychology, much like life-course sociology, takes the view

that in order to fully understand behavior, researchers must examine the entire life-span.

According to some, the interest in developmental psychology became pronounced in the late

1970s and early 1980s (see Baltes et al., 1980). Sampson and Laub (2004) view developmental

perspectives as implying a sort of “unfolding” of a script that was written at an earlier time,

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ontologically16 assuming that events are not important in how a person’s life plays out. They

suggest that life-course (or “sociogenic”) explanations are better suited to explain change over

time because they are not as wedded to individual differences and traits, although sociogenic

views do incorporate these factors (see Laub and Sampson, 2003).

A key debate between psychologically-oriented and sociologically oriented life-

course/life span researchers occurred in 1984. Dannefer (1984, foreshadowing Sampson and

Laub’s position) argued that the developmental literature emphasized individual factors at the

expense of sociological events in determining the course of a person’s life. In particular,

Dannefer suggested that developmental work implied a mechanistic script for individual lives

that failed to recognize the importance of context and how interpretation of events can structure

the life-course. Sampson and Laub (1993: 12) offer this explanation for the advantages of the

‘sociogenic’ perspective: “the contributions of sociological research and theory provide the basis

for understanding human development as socially organized and socially produced, not only by

what happens in early life, but also by the effects of social structure, social interaction, and their

effects on life chances throughout the life-course.”

However, proponents of developmental approaches contend that they do not ignore social

structure, but simply refuse to give it priority over other influences (e.g., biological, age-graded,

historical) (see Baltes, 1987; Baltes and Nessleroade, 1984). For example, this approach focuses

on within-individual continuity and change in such things as personality, cognitive ability and

behavior over time while also incorporating “life-changes” and context (Baltes et al., 1980;

Baltes, 1987; Blonigen, 2010). Developmental perspectives do not necessarily view the life span

16 The term “ontological” is often used to distinguish more individual-based psychological or biological

development perspectives from sociological life-course views. Ontology, however, simply refers to developmental change and continuity across the life—which incidentally is also what defines the sociological life-course approach (see Baltes, 1987; Sampson and Laub, 1993).

57

as unfolding in the same manner for every individual (see Sampson and Laub, 2005b). Instead,

the emphasis is on examining how development continues throughout life (Baltes et al., 1980). In

addition, according to Baltes and colleagues, developmental psychology recognizes the

importance of age-graded, historically-bound and non-normative changes (Baltes, 1987; Baltes

and Nessleroade, 1984). This approach takes the notion of maturation seriously—psychological

growth occurs in stages and eventually reaches a mature state (Baltes and Willis, 1977).

However, change continues even after maturation has been reached (Baltes et al., 1980).

As previously mentioned, research has shown that traits and personality characteristics,

which have been traditionally considered as relatively fixed, actually change throughout life as

well. For example, the study of self-control and self-regulation has indicated that individuals

increasingly are able to monitor their behavior and conform to expectations over time (Cauffman

and Steinberg, 2000; Kopp, 1982; Steinberg, 2008).

With respect to antisocial behavior and behavioral change, developmental perspectives

have concentrated on early experiences and how they shape individual trajectories. There is a

growing literature exploring the early childhood risk factors that predict later adult offending.

For example, it is now generally accepted that such things as childhood aggression, impulsivity,

neuropsychological deficits, lack of empathy, poor parenting practices, and lack of proper early

nutrition are associated with crime in adulthood (see Loeber and Farrington, 1998; LeBlanc and

Loeber, 1998; Farrington, 2007; Farrington and Welsh, 2007; Stattin and Magnusson, 1989). In

addition, researchers have examined the developmental sequences of offending, which appears to

become more serious over time (Cairns et al., 1989; Loeber et al., 2003). Because developmental

approaches often appear to focus more on continuity than change (see, for example, Cairns et al.,

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1989; Huesmann et al., 1984; Patterson et al., 1989), they may seem ill-suited to account for

behavioral change.

However, several desistance theories have taken a developmental approach (see Laub and

Sampson, 2001; 2003). One hallmark of these perspectives is the incorporation of childhood into

the explanatory scheme (e.g., what happens in childhood matters with respect to later behavior).

For example, Le Blanc and Loeber (1998) argue that trajectories of desistance are linked to the

type and rate of individual offending earlier in life—the earlier that antisocial behavior emerges,

the more persistent the criminal career (see Stattin and Magnusson, 1989). Some of these

developmental explanations of desistance combine social process and internal/individualistic

factors (Moffitt, 1993; Patterson and Yoerger, 1993; Patterson, 1996).The most well-known

taxonomic theory of crime is that of Moffitt (1993; 2003) who argued that there are generally

two classes of offenders that underlie the aggregate age-crime curve. First are “life-course

persistent” (LCP) offenders, who suffer from troubled childhoods as a result of

neuropsychological deficits and poor environments. These children grow up to be adult offenders

and offend throughout the life-course. “Adolescent limited” (AL) offenders do not begin to

commit crimes until around age 16, when their social maturity and physical maturity are

mismatched. In order to compensate for this “maturity gap”, these individuals commit acts of

risk to assert their independence (for a similar view, see Greenberg, 1977). Because of a socially

integrated upbringing, however, these individuals are able to stop committing crimes once other

acceptable pathways to adult status are opened. Thus, this theory accounts for the swell in

offending in adolescence and the decline thereafter not via a decrease in individual offending,

but rather through the entering and subsequent dropping out of offending by the ALs. For a

graphical illustration of the theory, see Figure 3.1.

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[Insert Figure 3.1 about here]

However, the taxonomic approach, while intuitively appealing, has been criticized on

several points. It is unclear whether the AL and LCP groups should be considered as

qualitatively distinct groups or whether the classification is better thought of as a heuristic device

(see Ezell and Cohen, 2005; Skardhamar, 2009). Some have cautioned against the use of such

grouping language, because of the potential for negative labeling (Sampson and Laub, 2005a). In

addition, the empirical support of Moffitt’s theory is equivocal (see also Paternoster et al., 1997).

For example, research has found that while groups do seem to underlie much of the longitudinal

offending data, the number of groups varies. The work of Nagin and Land (1993), who

introduced the use of semi-parametric, mixed model poisson methods to criminology, initially

found five distinct groups of offenders. In fact, it is very rare for studies that use analyses

specifically developed to find latent groups to uncover less than three such groups (see Ezell and

Cohen, 2005; Laws and Ward, 2011; Nagin, 2005; Piquero, 2008). Finally, the exact

mechanisms by which ALs transition out of deviant lifestyles is not altogether clear; maturation,

however defined, appears to play a role in the process.

One interesting aspect of Moffitt’s theory, for the purposes of this dissertation, is her

notion of the maturation gap. According to Moffitt, adolescent-limited crime or delinquency is a

result of individuals trying to achieve adult status. There is some evidence that adolescent crime

is associated with a desire for independence (Piquero and Brezina, 2001) and that individuals

who mature early are more likely to be deviant (Steinberg, 2008). The notion that delinquency is

the result of a ‘maturity gap’ between different components of adult status suggests at least two

things: 1) that there are multiple dimensions of “maturation” and 2) that an imbalance in the

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dimensions may lead to deviance. There is not a large amount of work testing this idea, but some

research has found that maturity gaps in adulthood do lead to increased criminal behavior (see

Barnes, Beaver and Piquero, 2010; Barnes and Beaver, 2011). The idea that maturation gaps

negatively impact life outcomes has also been posited by others. For example, Newcomb (1996:

477) argued that “premature engagement in adult activities and responsibilities during

adolescence interferes with the acquisition of psychosocial skills necessary for success in these

adult roles.” Greenberger and Steinberg (1986) refer to this situation as leading to an “adultoid”

status.

Desistance Theories: Mutually Exclusive Or Overlapping?

For the most part, the theories of desistance discussed above have remained separate and

often competing perspectives in the literature. Many of the authors argue that the processes they

have highlighted are the major factors in explaining desistance, much to the exclusion of other

factors that are ostensibly inconsistent with their theoretical view. Hirschi and Gottfredson

(1983) are quite clear on this front, as they suggest that no sociological theory can explain

desistance from crime. They maintain that social relationships do not causally impact behavior,

as life-course theorists argue (Hirschi and Gottfredson, 1995). For them, age—and changes

caused by age—are the only true causes of behavioral reform.

Similarly, while cognitive transformation/identity theorists have recognized the

compatibility of their perspective with others (see Giordano et al., 2002), they argue that what

really matters for desistance are internal changes and perceptions, not social forces. For their

part, social relationship theorists maintain that social connections and social capital are at the

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heart of desistance. They do not discount identity change, but they argue that these changes are

ancillary to the impact of relationships on crime.

Comprehensive perspectives should recognize that behavior is a function of biological,

psychological and social processes. Thus each category of desistance theory should not be

viewed as competing with others. While there are certainly differences between the theoretical

perspectives outlined above, they are all related in various ways (see also, Chapter IV, below).

This section will briefly highlight several of the numerous potential linkages or compatibilities

between theories of desistance (both within and across categories) discussed above.

The Links between Perspectives

Supporters of pure age-based theories are perhaps the least amenable to integration with

other perspectives (Gottfredson and Hirschi, 1990; Hirschi and Gottfredson, 1983). However, in

their 1983 piece, Hirschi and Gottfredson argued that the relationship between age and the

“tendency to commit crime” (rather than crime itself) was invariant (see footnote 9).17 In

addition, even though Gottfredson and Hirschi argued that crime may decline independently of

criminality (1990), their theory also suggests that criminality declines with age and these changes

impact behavior; they simply thought that criminological research had not uncovered these

processes at the time. Yet, internal processes of change associated with criminality are exactly

what rational choice, cognitive transformation and psychosocial theories center on. In this sense,

part of the changes in “the tendency to commit crime” that come with age may be related to

increasing rationality, decreasing impulsivity, and changes in identity. In a sense, then, the RCT,

cognitive transformation, and psychosocial theories may be viewed as describing the “black box”

17 For the most part, researchers have interpreted Hirschi and Gottfredson’s position to be that the

relationship between age and crime is invariant, which in my view has different implications than what they actually argued.

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age and aging (see Elder, 1999), and are not incompatible with pure age-based accounts. In

addition, with respect to recent neurocognitive work, part of the reason that people become less

impulsive and less ‘biased’ in their decision-making over time could be due to the brain

maturation that appears to be occurring into the 20s. For example, Paternoster and Pogarsky

(2009) suggested that the tendency for change in cost/benefit calculations with age may be

due to changes in brain maturation. They argue that “A maturing of the brain areas responsible

for executive functioning may lead to an improvement over the lifespan in [rational decision-

making] by decreasing the discount rate—the rate at which people discount the future” (2009:

105; see also Geier and Luna, 2009).

The links between psychosocial maturity and rational choice theories are perhaps the

most clear. It is possible to view increasing rational choice as simply decreasing impulsivity.

Thus, psychosocial maturation may imply increasing rationality. Interestingly, part of Shover’s

(1996) conceptualization of increasing rationality includes the ability to consider future

consequences. Future orientation, as discussed above, is a major facet of psychosocial maturation

(Cauffman and Steinberg, 2000).

Civic engagement and social relationship theories are distinct but similar to each other in

that they are both social process explanations of crime and desistance. That is, they both view

external factors and behaviors as important in facilitating cessation of crime. These theories are

less concerned with internal processes and thus may be seen by sociologists as more policy-

relevant. Nonetheless, they differ in exactly how social processes are said to change behavior. As

noted, social relationship theories are often couched within a social control perspective, in which

social ties are seen as restraining natural, deviant behavior (Sampson and Laub, 1993). Civic

engagement perspectives, especially the work of Uggen and Massoglia, are derived from a more

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symbolic interactionist framework in which social processes such as voting and participation in

volunteer work helps shape the offenders’ feeling that he or she is part of society. This helps

transform the offender’s identity, which marks a clear link between this work and cognitive

transformation/identity theories of desistance.

Social relationships and cognitive transformation/identity theories represent perhaps the

most popular perspectives on desistance currently in the criminological literature. Accordingly,

researchers have attempted to sort out how these theories are related or if they are incompatible.

The best evidence now suggests that both cognitive and social process factors are implicated in

desistance (LeBel et al., 2008; Mulvey and LaRosa, 1986). Even if identity change occurs before

social relationships are attained or strengthened, then those relationships are still a vital part of

desistance, as research has shown that merely wanting to desist may not be enough to actually do

so, without social support (see Shapland and Bottoms, 2011). An unexplored, but potentially

important linkage between theories may also involve psychosocial or neurological maturation

and adult social roles. It could be that changes in cognitive processes influence individual

preferences for and ability to fulfill these roles.

In sum, while the theories reviewed in this chapter have generally been presented in the

literature as competing, it is reasonable to view them all as identifying factors of a larger,

developmental process—one that may help better understand desistance from crime. Indeed, the

links between these perspectives are numerous; only a few were highlighted here. It is true that

certain factors may have a larger impact on behavior than others, but it seems that each theory or

framework in isolation is incomplete and can be profitably enhanced by considering its link with

other perspectives. In this sense, a maturation perspective may be integrative, incorporating parts

from extant theoretical explanations into a larger, more powerful whole. Unfortunately,

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integrated theories of desistance are not common in the criminological literature (Farrall et al.,

2011). The theoretical framework advocated in this dissertation is that each of the theories

identifies processes that play a role in desistance. It is possible that these theories may be used to

develop “domains” of maturation; domains that the Gluecks argued should be developed many

years ago. The next chapter takes a step in that direction, describing five different domains of

maturation, all derived from the literature reviewed above.

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CHAPTER IV. A MULTI-DIMENSIONAL CONCEPTION OF MATURATION

The next step in developing the theory of the relationship of maturation to delinquency and criminality is to dissect ‘maturation’ into its components. This task must be left to specialists

in psychiatry, psychology, physiology, medicine and related disciplines. (Glueck and Glueck, 1940: 270)

You’re only young once, but you can be immature forever

(Germaine Greer)

Introduction

All of the literatures reviewed in the previous chapter, I argue, are relevant to a

revitalized conceptualization of maturation. Unfortunately, recent criminological work has not

sought to explicate maturation and its relationship to desistance, or extend previous theories

(Maruna, 2001; Shover, 1985). In this dissertation, I will attempt to fill this gap, not only by

helping to explicate what maturation means but also by relating maturation to desistance in an

empirical framework. The purpose of this chapter is to integrate the work reviewed in Chapter III

by showing how each perspective is part of a multi-faceted conceptualization of maturation. For

the most part, it appears that criminological researchers have not sought to define maturation

according to separate domains, or to determine how maturation affects criminal behavior. Extant

theories of desistance contain parts of what maturation seems to represent, but remain

incomplete.

Before describing the domains of maturation, it is important to review the limited work

that is directly relevant to a multi-dimensional conceptualization of maturation. Gove (1985)

argued that the process of aging is accompanied by changes in sociological, biological, and

psychological factors (for a more recent, similar view, see Adams, 2004). These changes, in

combination, account for desistance from crime. While his framework was couched in

maturation (thus recognizing that maturation is a multifaceted concept), he mainly focused on

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psychological adjustment. He argued that as individuals age (or mature) they begin to become

more interested in the general community, experience changes in the self (becoming less self-

interested), become more accepting of social values and social relationships, and become more

concerned with the meaning of life. He called these changes “psychological maturation”,

drawing from the personality development literature in explaining how this process affects

behavior (see pg. 128).

Gove also gave brief attention to the possible role of biological factors (e.g., changes in

hormones, physical strength, need for stimulation), but did not include brain or cognitive changes

in his framework. While Gove did not test his explanation, his work points to several possible

indicators of maturation. These include the transformation from self-absorption to concern for

others and wider social values. And though he was critical of “vague maturation process”

theories of desistance (1985: 131), I argue that his paper is important because a) it suggests that

the transition to adulthood comprises changes across several domains (which I call maturation)

and b) it identifies a major component of maturation (e.g., psychosocial maturation, described in

the next chapter) and points to several (at the time) untested assertions about how biological

changes may affect behavior. Finally, Gove hinted that biological and social factors may interact

in leading to desistance (for a somewhat similar view, see Meisenhelder, 1977). That is, the

attainment of adult social relationships may have a larger effect on behavior if they are

accompanied by other types of maturation (see also Greenberger and Steinberg, 1986).

Shover (1985), in his qualitative study of aging offenders, suggested that changes in

several areas of life help facilitate the transition to a conventional life. What he called

“contingencies” include 1) changes in how offenders view themselves, 2) an increased future

orientation, 3) a feeling of “burning out”, 4) meaningful romantic relations, and 5) useful

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employment. Interestingly, Shover argued that future work was needed to uncover how “various

combinations of the contingencies” impact desistance (1996: 101, emphasis in the original).

An Updated View of the Components of Maturation: Possible Measures and Rationale

In this section, I describe maturation by “dissecting [it] into its components” (Glueck and

Glueck, 1940: 270). In doing so, I draw on literature from multiple disciplines, including

criminology, sociology, cognitive psychology, and the neurological sciences. The foundation for

each of the “domains” of maturation identified below is drawn from the desistance and

developmental literature discussed above. All of these literatures are fundamentally about

changes that take place during the process of becoming a fully-integrated adult. It is my

contention that maturation is comprised of many internal and external developments, including

what Massoglia and Uggen (2010) refer to as the attainment of “adult status markers.”

In what follows, I identify five dimensions or components of maturation, along with

possible indicators that may represent development in these areas. Instead of reiterating the

literature from Chapter III, the rationale provided for each domain includes how the domain

relates to becoming an adult and how it may be linked to other domains of maturation. The

notion of maturation or development in these areas, I argue, provides a reasonable explanation

for behavioral change over the life-course. A multi-factorial approach, in which maturation

occurs in several domains, helps to explain why not everyone who reaches “maturity” in one

domain (e.g., change in identity from a “hell raiser” to a conformist, see e.g., Hill, 1971) is able

to desist from crime. In addition, these factors may interact in their impact on behavior over the

life-course. For example, a change in identity may require a different social context (e.g., stable

job or marriage) in order to influence behavior (see Blonigen, 2010).

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I. Social Role Maturation: Key indicators of the social domain of maturity include the

following: The presence and quality of adult relationships such as marriage and children;

Markers of independence (not living with parents, being self-sufficient); Finishing school (high

school or college degree); and Satisfaction with and consistency of employment (see Giordano et

al., 2002; Horney et al., 1995; Laub and Sampson, 2003; Sampson and Laub, 1993; Siennick and

Osgood, 2008; Shover, 1996; Uggen, 2000; Yamaguchi and Kandel, 1985; but see Massoglia

and Uggen; 2010; Uggen and Massoglia, 2003).

Explanation: The basis for this domain of maturation derives from the social relationship

and social role theories reviewed in Chapter III. For the most part, researchers in the life-course

tradition have considered “social bonds” or “social ties” from a social control perspective. That

is, marriage to a good woman and job stability are seen as restraints to adult criminal behavior

(Sampson and Laub, 1993; 2003; 2005a). The best known theory in this tradition is Sampson and

Laub’s (1993; Laub and Sampson, 2001; 2003) age graded theory of informal social controls,

which demonstrates the importance of life events in effecting turning points with respect to

individual trajectories (see also Elliot, 1994; Graham and Bowling, 1995; Haynie et al., 2008;

Yamaguchi and Kandel, 1985). However, Massoglia and Uggen (2010) argue that marriage,

employment, and desistance are part of traditional adult status markers, or what Giordano and

colleagues (2002) refer to as a “respectability package” (see also Massoglia and Uggen, 2003).

Thus, marriage and employment may not be causally related to behavioral change but part of the

same process of becoming an adult.

My position, as it relates to social maturation, is that both arguments are valid—that is,

adult social ties represent restraints on behavior because they indicate adult status. Adult status

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brings with it normative expectations and different role oriented behaviors than those usually

associated with juveniles and these factors are part of maturation (see Adams, 2004; Yamaguchi

and Kandel, 1985). For example, Adams (2004: 338) states, “As a partnership, marriage works

against egocentric perspectives by creating pressures for less selfish outlooks in ways that range

from demands for simple courtesies to expectations of more altruistic behaviors.” Thus, part of

becoming an adult involves independence (economically) and adult social relationships (not

necessarily marriage). Without these components, it is difficult to claim full adult status in

today’s society. And I argue that rather than desistance being a stepping stone to adulthood, it is

the result of the attainment of adult status. However, as will be argued below, other components

of maturation (e.g., cognitive transformations, psychosocial maturation) may interact with the

relationship between social maturation indicators and criminal behavior. That is, social roles—on

their own—may not have a long-term impact, such that when/if the relationships are over, crime

increases (Horney et al., 1995; Yamaguchi and Kandel, 1985).

It should also be noted that the timing of social relationships is likely to matter with

respect to behavior. That is, those who marry or have full-time employment before they are

psychologically prepared may suffer adverse consequences from these “precocious transitions”

(see Moffitt, 1993; Mulvey and LaRosa, 1986; Thornberry et al., 2004). Additionally, late

transitions may be associated with poor adjustment. For example, recent work has indicated that

those who marry later (e.g., early or late 30s) than others may be more prone to poor

psychological functioning and may not receive the “protective” benefits of marriage (Theobald

and Farrington, 2011).

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II. Citizenship or Civic Maturation: Measures of civic integration may include: Voting

or taking part in government/political activities; Attitudes toward the state or government;

Community service or activity in community organizations; Payment of taxes; Volunteer work;

Tolerance of diversity; and ‘Concern with wider interests of the community’ (see Farrall and

Calverly, 2006: 135; LaFree, 1998; Uggen et al., 2004; Uggen and Massoglia, 2003).

Explanation: The notion of citizenship or civic responsibility is becoming more prevalent

in desistance research (see Chapter III). Civic responsibility implies that the individual feels a

degree of legitimacy toward the state which should lead to greater acceptance of

rules/regulations and laws. The idea is that when individuals reach adult status, they begin to

recognize duties (conforming behavior, paying taxes) that are associated with citizenship (Farrall

and Calverly, 2006). In a sense, citizenship is a relationship with the state much like a social

relationship. It involves sacrifices and obligations and also specialized benefits; that is, it

involves a degree of ‘give and take’ that requires the person to think of more than just

themselves. I include this notion as a part of maturation because it seems to capture a

developmental process whereby individuals come to think less about their own well-being and

begin to think more of others—even others whom they have never met. Certain work on the

transition to adulthood also argues that civic engagement is a part of that process (Finlay, Wray-

Lake, and Flanagan, 2010).

This concept of civic responsibility may be thought of as part of the process whereby the

individual comes to view social inclusion as increasingly important. Uggen and colleagues

(2004) show that desisting individuals voiced a desire to be ‘productive members of society’ and

‘good taxpayers’. They viewed themselves as good citizens and wanted to be able to live their

lives as such (see also Maruna, 2001). This is related to the notion of “generativity” in which

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individuals develop a desire to give back (McAdams et al., 1998). In a sense, being accepted and

identified as a good citizen is the opposite of being identified as a deviant. This type of role

reversal may be important in the desistance process (Uggen and Massoglia, 2003).

Civic responsibility or citizenship is viewed here as a component of maturation because it

entails a concern for the greater good, a reduction, perhaps, of self-interest and self-centeredness

that characterizes many young offenders (Gottfredson and Hirschi, 1990). To Uggen and

colleagues, a transformation in identity seemingly precedes involvement in civic responsibility

(e.g., the person views themselves as a good citizen and then acts as such). However, it is unclear

whether identity changes lead to more civic engagement, whether civic engagement leads to a

change in identity or whether these changes are part of a larger “maturation” process. As Farrall

and Calverly (2006: 137) state:

These processes of socialization and resocialization are important for [a] consideration of desistance and citizenship values as they suggest that common changes (i.e. from offender to non-offender) are associated with shifts in values. Quite why this ought to be the case, and, perhaps more importantly, the causal ordering of this relationship, remains something of an enigma.

Nonetheless, to the extent that civic responsibility or citizenship entails an increase in feelings of

legitimacy toward the state, one would expect a decline in antisocial behavior (see LaFree, 1998;

Tyler, 1990).

III. Psychosocial/Personality Maturation: Indicators that may represent psychosocial

and personality maturation include: Attitudes toward adult roles; Expectations of future adult

roles; Impulsivity; Present orientation; Responsibility; Inhibitions; Sensation-seeking;

Rationality or Rational decision-making; ‘Consideration of others’; Agreeableness;

Conscientiousness; Neuroticism (see Blonigen, 2010; Blonigen et al., 2006; 2008; Caspi et al.,

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2005; Cauffman and Steinberg, 2000; Cruise et al., 2008; Gove, 1985; Monahan et al. 2009;

Modecki, 2008; Shover, 1996; Shover and Thompson, 1992; Shover, 1985).

Explanation: The domain of psychosocial maturation derives from work in the mid-

1970s meant to explain changes in personality and social roles that accompany the transition

from adolescence to adulthood (see Chapter III; Greenberger and Sørensen, 1974; Greenberger et

al., 1975). Greenberger and Sorensen (1974) identified three categories of psychosocial

functioning, under which several subtypes were listed. The three categories were: individual

adequacy; interpersonal adequacy and social adequacy (see Greenberger et al., 1974). Steinberg

and Cauffman (1996; Cauffman and Steinberg, 2000) refined the concept of psychosocial

maturation, which they saw as comprising responsibility (self-reliance), perspective

(agreeableness and future orientation) and temperance (control/lack of impulsivity, constraint of

aggression). Among these changes are increases in independence and improvements in the

ability to communicate and relate to others. This more recent operationalization of psychosocial

maturity includes components of Gottfredson and Hirschi’s (1990) notion of “self-control.”

Inhibitions, consideration of others, impulsivity, and present orientation are all characteristics

these authors use to describe “typical offenders.” However, whereas Gottfredson and Hirschi

suggest that these traits are relatively stable and therefore do not represent ideal measures to

explain within individual change, recent psychological and cognitive research has shown how

impulsivity may help explain the increase in antisocial behavior in adolescence and its

subsequent decline (see, e.g., Cauffman and Steinberg, 2000; Monahan et al., 2009).

A growing body of literature surrounding the concept of psychosocial maturation

suggests its potential importance in explaining the differences in behavior between adolescents

and adults. However, it should be noted that much of this work is cross-sectional, exploring

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individual differences between adolescents and adults at one time point. Longitudinal research is

needed to understand whether within-individual changes in psychosocial maturity correspond to

changes in behavior over the life-course.

Related to the line of work reviewed in Chapter III under rational choice theory, Shover’s

(1985; 1996; Shover and Thompson, 1992) work over the last 20 years also indicates that some

offenders (he examines persistent thieves) become more rational over the years. From this

perspective, individuals begin to consider the consequences of their actions and their assessment

of risk associated with crime increases while their assessment of the payoff decreases (the

evidence appears to be mixed on these ideas—for example, Shover and Thompson (1992) find

that while age is inversely related to perceived payoff of crime, it is not related to perceived risk.

Shover (1996) argues that offenders begin to realize that their lives are time-limited and that the

“party-life” is no longer worthwhile. In other words, offenders at some point begin to look into

the future and consider the consequences of their actions—which may lead to a change in

behavior. Thus, rationality (or thoughtfully reflective decision-making) (Paternoster and

Pogarsky, 2009; Paternoster, Pogarsky, and Zimmerman, 2010) may be an important component

of psychosocial maturity, representing the inverse of impulsivity. That is, as individuals (and

offenders) mature, they begin to more carefully consider the consequences of their actions and

take steps to ensure their decisions are appropriate. Recent work has called for more research on

changes in how offenders view crime from a rationality standpoint (Adams, 2004; Mulvey et al.,

2004).

Chapter III also reviewed recent personality work that has shown certain traits may

change over time (Blonigen, 2010; Caspi et al., 2005). Some traits (such as agreeableness,

conscientiousness, openness to change) are likely to lead to more prosocial behavior. Blonigen

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(2010) argues that changing social roles may affect personality traits. Thus, this work suggests

another possible interaction (perhaps reciprocal) between psychosocial/psychological maturation

and social maturation. In any case, it remains that personality changes related to desistance are

poorly understood.

IV. Identity/Cognitive Transformation: Markers of identity maturation from the

literature reviewed above include: Attitudes toward deviance or crime; Views of the self; and

Openness to change (Giordano et al., 2002; 2007; Laub and Sampson, 2003; Maruna, 2001;

Maruna et al., 2003; Massoglia and Uggen, 2010; Paternoster and Bushway, 2009; 2011;

Vaughn, 2007).

Explanation: Research has long indicated that crystallization of identity (e.g., discovering

‘one’s true self’) is part of the maturation process and transition to adulthood (Arnett, 2000;

Hogan and Astone, 1986). In addition, research shows that individuals often undergo numerous

changes in outlooks toward social behavior, such as deviance (Giordano et al., 2002).

As reviewed in Chapter III, one of the major theories of desistance to emerge in recent

years involves cognitive transformations of the self.18 Scholars have argued that changes in how

offenders begin to view themselves and their world around them are integral in changes in

behavior over time. Theories of cognitive or identity transformation, suggest that Sampson and

Laub’s (1993) social control theory of desistance is not sufficient—that is, marriage and stable

employment alone are not enough to change individuals’ behavior. Interestingly, certain of the

orientational changes associated with cognitive transformation (e.g., changes in attitudes toward

18 Massoglia and Uggen (2010) present a symbolic interactionist perspective on the transition to adulthood.

According to this view, attaining adult social roles “on-time” (that is, at the expected point in the life-course) leads others to view individuals as more mature. These “reflected appraisals” influence how individuals view themselves; thus those who are “off-time” with respect to adult roles feel subjectively less like adults.

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crime) are labeled ‘maturation’ by Farrall and Calverly (2006: 179). Giordano and colleagues

(2002) viewed their theory as complementary to Sampson and Laub’s. Thus, cognitive

transformations are likely to have a stronger impact on crime when “hooks” for change

(marriage, jobs, religion) are available.

Other researchers have also offered theories of desistance relying on the social-

psychological concept of the “self” (Maruna, 2001; Paternoster and Bushway, 2009; 2011).

Maruna argues that desisting offenders begin to see themselves as different than who they were

when they were involved in crime. They are more optimistic about their futures, blaming their

past (indiscretions) to external influences that are now under control. Maruna’s cognitive

transformation/self theory is related to civic reintegration/citizenship and psychosocial

maturation. For example, he argues that desisters’ begin to think about ‘making a difference’ in

the lives of others (e.g., becoming concerned with the well-being of other people) and society.

As discussed in Chapter III, Paternoster and Bushway (2009) recently articulated a theory

of the “feared self”, which suggests that at a certain point offenders come to view a criminal

lifestyle as unrewarding. They do not wish to see themselves as antisocial any longer and “fear”

a future self that is associated with crime or an illicit lifestyle. This realization occurs after a

series of negative events (leading to what they call the “crystallization of discontent”).

Paternoster and Bushway’s (2009) theory may be related to the psychosocial maturity domain

described above in that it is based on a rational choice foundation. Part of what leads an offender

to question his/her current lifestyle is “a sense that being an offender is no longer financially

beneficial, that it is too dangerous, that the perceived costs of imprisonment loom more likely

and greater, and that the costs to one’s social relationships are too dear” (2009: 1105). Thus,

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increasing rationality leads to a change in how one views oneself, changes in one’s preferences,

and ultimately desistance.

Identity and cognitive transformations are also linked to personality traits and

psychosocial maturation. For example, Giordano et al.’s (2002) concept of openness to change is

similar to the “Big Five” trait of openness to new experience. In addition, researchers examining

personality trait (as opposed to more flexible ‘states’) changes over time have suggested that

changes in identity may lead to changes in personality (Caspi et al., 2005). Early definitions of

psychosocial maturity included a solidification of identity and specified a process by which

individuals come to anticipate the responsibilities, requirements, and expectations associated

with new roles before assuming them (see Greenberger and Sørensen, 1974; Greenberger and

Steinberg, 1986). Thus identity transformation has long been considered (at least in the

developmental literature) a part of the maturation process.

V. Cognitive/Neurological Maturation: Direct measures of cognitive or neurological

maturation include: Increasing neurological development; Decrease in frontal cortex Grey Matter

(GM) density; Increase in Cortical Myelination; and Increase in White Matter (WM) density.

Indirect measures include: Neuropsychological measures of executive functioning, memory,

vocabulary proficiency, and abstract reasoning (Geidd et al., 1999; Iselin et al., 2008; Luna et al.,

2004; Steinberg, 2004; 2005; 2010; Paus, 2005).

Explanation: Recent advances in neurological and cognitive sciences have indicated that

the brain continues to grow and develop during adolescence and into adulthood (see Chapter III,

above). These changes have been associated with more rational thought and socially acceptable

behavior. Steinberg, in particular has argued that neurological development leads to more

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impulse control and hence less risky behavior on the part of adults. Steinberg argues that in

adolescence, dopamine activity increases which promotes “reward-seeking” behavior, but is not

accompanied by development of regulatory control systems of the brain until adulthood (2009:

216). This maturation imbalance leads to risk taking and impulsive behavior.

Interestingly from the point of view of desistance studies, work by neurocognitive

scientists has indicated that the brain (especially the prefrontal cortex) reaches full maturity (on

average) around age 25 (Casey, Giedd, and Thomas, 2000; Giedd et al., 1999). The brain

maturation appears to involve increasing myelination and density of white matter and decreases

in grey matter (see Gotgay et al., 2004; Paus, 2005; Sowell et al., 2001). The age of brain

maturity corresponds to the right hand side of the aggregate age-crime curve and to findings that

social maturation domains begin to show an impact on crime after the mid-20s (see Hirschi and

Gottfredson, 1983; Uggen, 2000). Giedd et al. (1999) find that grey matter increases before

puberty followed by a decline after adolescence (which might account for both the increase in

crime in adolescence and the subsequent decrease in such behavior in adulthood). In addition,

recent research has shown that intelligence, rather than being a fixed entity, often changes during

emerging adulthood—both positively and negatively (Ramsden et al., 2011).

Evidence is also accumulating that neurological maturation may play a role in desistance

from crime. According to Blonigen (2010), the three main neurotransmitters that have been

implicated in crime and deviant behavior, noreprenephrine, dopamine, and serotonin appear to

undergo changes in adulthood. Blonigen calls this phenomenon “neurobiological maturation”

(2010: 96). While the evidence remains somewhat unclear, it is likely that neuropsychological

functioning (a proxy for neurological changes) also improves with age. Certain research has

found that executive function and working memory increase through adolescence (Iselin et al.,

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2008; Luna et al., 2004). This line of work is related to psychosocial maturity, because improved

cognitive or executive functioning is linked to a reduction in impulsive and sensation-seeking

behavior (Casey, Jones and Hare, 2008; Steinberg, 2010). It is also possible that neurobiological

maturation is linked to the other forms of maturation discussed above, for example, leading to a

greater openness to change and receptivity to social relationships (see Chapter III above).

Theoretical Framework of the Dissertation

The theoretical framework advocated here as an explanation of desistance is multi-

faceted and integrative. It suggests that we can best understand why and how desistance occurs

through the lens of a complex, integrated notion of maturation rather than by examining isolated

processes. Desistance from crime is likely to be related to changes in social relationships,

changes in attitudes and identity, changes in views of the self, and biological processes. All of

these factors form what I see as maturation in terms of behavioral change. They all, importantly,

represent changes that occur during the transition to adulthood. The maturation domain schema

is presented in Figure 4.1.

[Insert Figure 4.1 about here]

Becoming an adult is not a simple transition, comprised of one or two salient events.

Instead, it entails multiple, complex processes, both internal and external. Focusing on one or

two of these processes (as I argue most desistance theories have done) is not likely to capture the

entire experience of becoming an adult—that is, maturation. Some of the domains posited above

have yet to be empirically validated, and they have not been examined in relation to

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delinquent/criminal behavior. Thus the purpose of the dissertation will be to validate domains

while also examining their relationship to crime over time. In terms of the generality of the

theoretical framework underlying the dissertation, it should be noted that maturation and

normative development is more pertinent to an understanding of desistance among those without

psychological abnormalities and those whom Moffitt (1993) has called “life-course persisters.”

The framework is more relevant for normative desistance. Finally, structural impediments may

interfere with the processes involved in maturation. For example, stints of imprisonment may, in

Moffitt’s terminology, be developmental “snares”, delaying the attainment of adult status and

thus desistance from crime (Farrall and Calverly, 2006; Massoglia and Uggen, 2010).

Next, I present the research aims and associated hypotheses of the present dissertation.

Then, in Chapter V I discuss how I will attempt to measure and test maturation and crime as well

as their potential relationship(s).

Research Aims and Hypotheses

The research aims of this study involve examining the various “components” of

maturation and their possible relationship to crime. Each aim is derived from the literatures

reviewed and discussed in Chapters II and III of this dissertation. The aims first, center on

developing and analyzing a multi-faceted definition of maturation. Next, the research aims

address the possible relationship(s) between maturation domains and crime over the life-course.

1. Previous research has suggested that maturation may be thought of as

multifaceted. At this point, what those domains or components are remains

somewhat unclear. Thus, the first research aim is: To develop empirically valid

domains of a multi-dimensional definition of maturation. The techniques that will

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be used to answer this question are described in the next chapter. There are no

hypotheses associated with this research aim, which will be addressed by seeking

to derive empirically valid measures of the domains of maturation described in

this chapter. That is, while ideal-typical measures have been identified above, the

purpose of this aim will be to determine whether valid measures can be developed

using the HHDP data. Each domain is expected to consist of multiple dimensions

and part of the analysis will be focused on identifying items/dimensions that

comprise each domain. Thus, the description of the domain construction will be

extensive, to illustrate how each measure is created.

2. The major contention of this dissertation is that maturation impacts criminal

behavior over the life-course, particularly desistance. Thus, the second set of

research aims is: to examine whether maturation influences desistance from

crime. Additionally, the dissertation will seek to assess whether changes in levels

of maturation affects changes in criminal or antisocial behavior over time.

Hypotheses: it is hypothesized that maturation will impact longitudinal

sequences of crime over the life-course. That is, it is expected that those with

higher levels of maturation (in various domains) will be less likely to commit

criminal acts. This aim will require the construction of “overall” maturation

levels, at each time period (and averaged over the life-course) which will allow a

between-individual analysis of the effect of maturation levels on crime. Those

with higher levels of maturation should also be more likely to desist from crime at

earlier ages.

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There is also a (perhaps more important) within-individual component to

this aim. Maturation in the identified domains can be analyzed over time with

respect to changes in levels between time points (e.g., decreases in impulsivity,

increases in adult relationships, increases in prosocial identity, etc.). It is

important to note that only changes in levels of maturation at each time point are

examined here. Thus, because maturation implies a process that is constantly in

flux, it is unclear whether maturation is fully captured by this method.

The analysis of maturation levels as they relate to crime/delinquency over

time can be conducted using a random-effects multi-level model, incorporating

between individual factors (e.g., mean level maturation) and within-individual

factors (e.g., deviations from the mean levels of maturation). Such an analysis will

be able to identify changes in maturation levels over time and the corresponding

relationship to changes in crime. Thus, it is also hypothesized that maturational

changes should lead to within-individual changes in criminal behavior. That is

changes observed in levels of maturation over time should be negatively

correlated with changes in criminal behavior over time. In sum, not only levels

(between individual differences) of maturation but also changes (within

individual differences) are expected to impact antisocial behavior.

Additionally, it is possible that only certain of the maturation domains

identified in the dissertation are relevant to crime; however, the hypothesis is that

each domain will be important in explaining desistance. That is, without

information on each of the domains of maturation, a complete picture of

desistance will not be achieved. In addition, overall maturation (a combination of

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the domains of maturation) is expected to be related to crime over time. Even if

particular domains separately are not statistically related to crime, each is viewed

as a piece of the maturation puzzle and thus important to measure when

considering the total maturation effect.

3. The third major research aim involves the possible effects of “maturity gaps.”

This question will address how disjunctions between maturation domains impact

behavior. Thus, drawing on the developmental literature regarding maturation and

behavior, the dissertation proposes: To assess whether gaps or disjunctions

between domains of maturation are related to desistance, and To examine the

effects of “various combinations” of maturation levels (Shover, 1985)

Hypotheses: The third set of research aims is, like the first one, more

exploratory in nature. That is, it is unclear how, if at all, disjunctions between

maturation domains will impact behavior. However, it is hypothesized that

individuals with equitable levels of maturation in all domains will be less likely to

commit criminal acts. It is also hypothesized that gaps—for example scoring high

on certain domains (e.g., social relationships) relative to other domains (e.g.,

identity or psychosocial maturation) will be positively associated with crime. This

hypothesis derives from the work of Steinberg and Cauffman (1983), Moffitt

(1993), Galambos and Tilton-Weaver (2000) and Giordano et al. (2002), all of

whom argue that disjunctions (in various combinations) lead to some sort of

maladjustment and ultimately, misbehavior or crime. As explained below, the

maturation gaps to be analyzed focus on adult social role maturation relative to

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identity and psychosocial maturation. It is anticipated that those with his adult

social role maturation but low identity or psychosocial maturation will be more

likely to engage in criminal behavior.

With respect to the maturation gap analysis, there is a competing

hypothesis. Maturation domains may be “compensatory.” That is, a deficit in one

domain may be compensated by a high level in another. Thus, maturation gaps

may have null or even negative effects on crime. This would be the case if those

with low identity or psychosocial maturation levels relied more on the protective

effects of adult social roles than others.

Finally, it is hypothesized that certain domains will have an interactive

effect on behavior. For example the effect of “social maturation” (e.g., marriage,

employment, education) on crime may vary by the level of “identity maturation”

(e.g., the extent to which one has a prosocial, adult identity). This latter

hypothesis implies conditional relationships between domains of maturation.

Once again, this effect could be cumulative (e.g., social role maturation matters

only for those with high levels of identity maturation) or compensatory (e.g.,

social role maturation predicts crime only for those with low levels of identity

maturation).

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CHAPTER V. DATA & RESEARCH METHODS

The Rutgers Health and Human Development Project is conceptualized as an observational study of the developmental emergence and unfolding of alcohol, marijuana and

other drug use behaviors in interaction with the individual’s physical, psychological and social development during the transition from adolescence to early adulthood.

(Pandina, Labouvie, and White, 1984: 257)

Introduction

The purpose of this chapter is to review the data and research design that is implemented

to address the questions posed in the last chapter. First, I will introduce the dataset utilized in the

analysis. Next I will describe the measures for the dependent variables and independent variables

(e.g., covariates and domains of maturation). Finally, I discuss the analytic strategy used in the

examination of the relationship of maturation to crime and desistance. Each of these sections is

detailed in order to make clear what data and analysis are used to assess the research aims

discussed in the previous chapter.

Research Design and Dataset

In order to address the research questions and hypotheses outlined at the end of the

previous chapter, the dissertation will utilize data collected as part of the Rutgers Health and

Human Development Project (HHDP). The HHDP is a prospective, longitudinal study of three

cohorts of individuals (N=1,380), initiated in 1979 by researchers at Rutgers University. The

subjects were followed from age 12 (youngest cohort), age 15 (middle cohort), or age 18 (oldest

cohort) until their late 20s or early 30s. The data set includes key measures of maturation and the

transition to adulthood from adolescence. The dissertation will exploit the richness of these data

to 1) propose measurement and operationalization of several “domains” of maturation and 2)

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examine the relationship(s) between maturation domains and desistance from crime and/or

delinquency.

The HHDP began as a life-span developmental study of alcohol, drug use, and other

problem behaviors from childhood to young adulthood (Hancock, 1996; Pandina, et al., 1984;

White, Pandina, and LaGrange, 1987). As such, it includes a wealth of data regarding the types

of substances used, the circumstances under which substances were used and timing/sequencing

of use. In addition, the study includes detailed information on the participants’ environments,

relationships with parents, friends, and partners, attitudes toward the self and toward deviance,

personality, neurocognitive functioning and delinquency (in addition to drug/alcohol use).

Previous analyses of the HHDP data have identified heterogeneity with respect to trajectories of

problem behavior over time (see Barker et al., 2007; White et al., 2002). Thus, the dataset is

ideal for an examination desistance from delinquency and less serious crime from a

developmental perspective.

Design and Data Collection: The HHDP

The HHDP consists of five separate time assessments. The subjects (all three cohorts)

were recruited from 16 of the 21 counties in New Jersey, using a random telephone number

selection procedure. The researchers used a quota sampling design to achieve equality in terms

of the number of males and females recruited within each cohort. Exclusion criteria included

individuals who did not meet the age requirements, were institutionalized, were physically or

mentally handicapped, and did not speak English. The initial screening took place over the

telephone and researchers then surveyed the subject at his/her home. This round of data

collection involved interviewing the parents/caretakers as well. Following the home surveys, the

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subjects were invited to the testing center to complete an array of tests, including blood work

(Pandina et al., 1984).

The first time period (T1) included three waves (W1-W3) of data collection, which took

place over the course of three years (1979-1981) in order to recruit enough subjects to fulfill the

desired sample size. The final sample recruited at T1 included 1,380 subjects, split relatively

evenly by sex (698 males, 682 females). The subjects enrolled in the study at T1 were mostly

white (89%), about half Catholic (30% Protestant, 9% Jewish, 11% other) and nearly all (90%)

lived with their birth parents. The subjects’ families in terms of socio-economic status were

primarily working to middle class (median income at T1: between $20,000 and $29,000). The

project researchers concluded that overall, the sample succeeded in faithfully representing white

middle-class youth in New Jersey at the beginning stages of the study (Hancock, 1996).

The original sample included three distinct birth cohorts (one aged 12; one aged 15; and

one aged 18 at T1). These individuals were followed up at least three times with the youngest

cohort being followed up a fourth time. For this dissertation, only the youngest cohort (aged 12

at T1) will be used. This decision was made for several reasons. First, one of the purposes of a

multi-cohort sequential design is to extend the age-range of the subjects without having to

interview each subject at all ages. In an accelerated multi-cohort design, cohorts with

‘overlapping’ ages are interviewed sequentially and statistical techniques are used to “estimate a

single growth trajectory” across the cohorts (Collins, 2006: 513; Uggen and Wakefield, 2008).

However, the youngest cohort in the HHDP was interviewed a fifth time (compared to only four

times for the other cohorts) at age 30/31. Thus, the youngest cohort remained in the study up to

at least the same ages as the other cohorts and utilizing the other cohorts would limit information

from early adolescence. Second, another purpose of sequential multi-cohort longitudinal designs

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is to be able to parse out cohort and age effects (Raudenbush and Chan, 1992). The dissertation

is not intended to examine cohort effects, however. Additionally, most work using the HHDP has

used one or two of the cohorts in order to avoid complexity of considering all three cohorts in

each analysis (H. R. White, Personal Communication, May 7, 2011; see Warner, White, and

Johnson, 2007).

The youngest cohort in the HHDP includes 447 subjects at T1 (230 males, 217 females),

who were born in 1967 (wave 1), 1968 (wave 2), or 1969 (wave 3). At T5, 374 subjects remained

in the study (retention of 84%). Previous analyses have shown that attrition did not significantly

bias the sample (White et al., 2002). In the present dissertation, analyses showed that those

present at T1 but not at T5 were more likely to be male, have a lower SES and higher parental

attachment. These will be used as control variables in the main analyses. There were no

differences by school performance (e.g., grades), race, T1 maturation domains (see below) or

delinquency between those who remained in the study at T5 and those who did not.19

The youngest cohort was interviewed sequentially at ages 15, 18, 25, and 30/31. Table

5.1 provides an illustration of the time periods and varying ages throughout the study. As can be

seen, the subjects were followed from age 12 to age 30/31, which provides a meaningful window

into the transition to adulthood as well as outcomes at full-adulthood.

A number of methods were used by the research team to ensure continued participation in

the study, thereby reducing attrition. The research team made continual contact with the subjects,

even in between data collection periods, provided incentives for address changes, and also

maintained contact with “holiday greeting” cards. Overall, the attrition rate from the inception of

19 In terms of the maturation domains, described below, at T2, social role, civic, and identity maturation

were significantly higher for those present at T5 versus not present at T5. T3 and T4 neurocognitive maturation was higher for those not present at T5 than those who were present. That there are few baseline and no differences in offending between these two groups, however, suggests that attrition is not likely to be a problem.

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the study through T4 (for all cohorts) was roughly 9%, which is quite low given the length of the

study (over 15 years). The attrition rate for the youngest cohort through T5 was slightly higher

(16%).

[Insert Table 5.1 about here]

Items have been identified across all time periods as potentially useful for the

measurement of key variables to address the research questions. The items are located on various

surveys administered at all time periods.

It is unlikely that ideal measures of the five domains of maturation discussed earlier may

be found in one dataset. Longitudinal studies in criminology are typically focused on relatively

narrow research questions and do not include a wealth of data on factors not specifically related

to whatever theoretical framework is guiding the study. The HHDP is somewhat unique because

it includes data on individuals from psychological, sociological, biological, and physical

perspectives. Nonetheless, because it was not intended to measure the five maturation domains

listed here, certain of the domains are not as well represented as others. Below, I describe the

measures from the HHDP that are used to operationalize the five maturation domains.

HHDP Measures for Analyses of Crime Trajectories

Dependent Variables

As stated above, the HHDP was designed to examine developmental pathways of drug

and alcohol use. These measures will not be included in the analyses. However, the HHDP does

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contain measures of delinquency and/or crime at time period, with the particular items varying

between time periods. These include:

1. Minor Theft (less than $50)

2. Major Theft (more than $50)

3. Vandalism

4. Assault

5. Rape

6. Breaking and entering

7. Pick pocketing

8. Using a weapon in a fight

9. Arson

10. Prostitution or solicitation

11. Avoiding payment

12. Fenced goods

13. Were involved in gang fights

14. Armed robbery

15. Embezzlement

16. Forgery

17. Used others’ credit card

These items are coded as either dichotomous (yes/no) or as ordinal categories. The

ordinal categories are: 0=0 times; 1=1-2 times; 2=3-5 times; 3=6-10 times; and 4=more than 10

times.

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At T1-T3, 9 items (ordinal response format) were available (avoid payment, breaking and

entering, used a weapon in a fight, auto theft, armed robbery, assault, vandalism, petty theft and

major theft). Rape, pick pocketing, arson, prostitution, gang fights, forgery, used others’ credit

card, and embezzlement were not included until T4. In order to maintain consistency, only

common items will be used for the purposes of analyses. At T4 and T5, ‘used a weapon in a

fight’ was no longer available. However, following prior research, ‘gang fights’ will be used in

its place (White, Bates, and Buyske, 2001).

It should be noted that the HHDP is composed of a “community sample” and as such

does not contain many (if any) serious, chronic offenders (see White et al., 2001). Therefore, the

rates of serious crime/delinquency are somewhat low. However, this is not necessarily

unexpected, as many longitudinal studies under-represent serious delinquents (see Mulvey et al.,

2004). In addition, as Siennick and Osgood (2008) argue, desistance studies should not be

restricted to only high-risk, high-delinquency studies. There is value in expanding life-course

research beyond high risk samples. Much of the work to date has examined serious offenders

(see Giordano et al., 2002; Laub and Sampson, 2001; 2003; Loeber et al., 2008; Piquero et al.,

2002). Thus it is unclear whether the findings from these studies translate to the more common

problem of minor delinquency and crime. For example, less serious offenders likely need fewer

major “turning points” to encourage desistance from crime. Siennick and Osgood (2008: 166)

put it well:

Whether the findings (from general and serious offender samples) do match is an empirical question, and if they do not, the divergence between types of studies would give direction to the search for better explanations. Furthermore, even if crimes meriting long prison sentences are rare in general population samples, lesser offenses such as shoplifting, writing bad checks, and minor assaults have considerable societal costs precisely because they are so common…. and we cannot limit our attention to either group alone if we wish to explain it.

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Few criminologists would argue that research on female delinquency or crime is unimportant

simply because females commit fewer crimes than males. Finally, it is the purpose of this

dissertation to offer a comprehensive explanation for normative desistance rather than delayed

desistance, which is perhaps more characteristic of serious offenders. The maturation framework

guiding this study is more appropriate for general populations than for rarer, more serious

offending samples.

For the purposes of analyses with the HHDP, summary scores using the 9 common items

are constructed. This is done to maximize the number of subjects who have positive scores on

the delinquency measures. Preliminary analyses indicate that there is little variation on more

serious items (e.g., rape and serious larceny). The most common form of delinquency or crime

committed by the sample was ‘avoided paying for things’. The least common at each time period

were auto theft and armed robbery (ranging from 0-2.3% with scores above 0). Combining the

delinquency items at each time period into one dichotomous score provides sufficient variation

(e.g., >=15% of the sample scoring above 0 on the delinquency measure at each time) for reliable

analyses (see Table 5.2).

[Insert Table 5.2 about here]

Because the original delinquency and crime items are measured on an ordinal scale, a

simple sum of the items would not provide a meaningful count measure (to be analyzed with a

Poisson or negative binomial model). Therefore, the main dependent variable is represented by a

variety score. Research has shown that a variety score tends to be more reliable than a simple

frequency score (Hindelang, Hirschi and Weis, 1981; Huizinga and Elliott, 1986). The variety

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score is calculated in the following way. First each delinquency/crime item is dichotomized, such

that 1 represents engagement in the offense. Next, a sum of the dichotomous items is calculated,

which is the number of unique offenses engaged in at each time period.

In addition, another version of the dependent variable is represented by a dichotomous

indicator, scored 1 if the individual engaged in any of the 9 behaviors at the time point in

question. As would be expected the proportion of individuals engaging in any of the offenses

rises from age 12 (38%) to age 18 (56%) at which point it declines to a low of 16% at age 30/31.

See Table 5.2 for a distribution of both the dichotomous and variety scales at each time period.

As can be seen, the delinquency/crime data follow the classic age-crime curve, peaking around

age 18 (with the highest proportion committing at least one crime in the last three years).20

Figure 5.1 displays these data in graphic form, illustrating the non-linear nature of the

relationship between delinquency/crime and time.

[Insert Figure 5.1 about here]

Covariates

As described in the previous chapter, there are 7 main covariates that are used in the

MLM analyses presented in the subsequent chapters. Two of these are time invariant and one is

measured only at T1. The others are measured at T1-T3. Race and sex are coded with White

(Asian, Black and Other were the “non-white” categories) and females as the reference category.

As can be seen in Table 5.3, only 9% of the sample is nonwhite. In addition, just over 50% of the

20 Other methods of delinquency scaling are available (e.g., IRT). These methods have certain benefits,

such as accounting for differential item seriousness and person ability/propensity. However, research has shown that IRT scales are somewhat complex, not easily interpretable and do not perform better than variety scores in terms of representing the latent trait of offending (Sweeten, 2006).

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sample is male. In terms of socioeconomic status, which is based on parental education and

occupational status at T1, the average score is about 52 out of a possible 77. It was noted earlier

that the socioeconomic status (SES) of the full sample was similar to that of the state of New

Jersey at that point in time.

Average grades is measured by asking respondents what their grades were on their last

report card. This measure ranges from 1-4 at T1 and 1-5 at T2-T3. Here, lower scores represent

higher overall grades (e.g., 1=A and 5=F). At all three time periods, the average grades

translate—roughly—to between an A and a B. Interestingly, the average grade appears to

decrease (poorer grades) over time.21

Parental bonding is measured at T1-T3. This scale represents the sum of 5 items that ask

how respondents feel about their parents. Higher scores indicate a greater level of attachment.

Interestingly, the average of this scale decreases from T1 to T3. This could possibly be capturing

greater independence sought during the late teens.

Finally, friends’ deviance is a measure of the level of delinquency of the respondent’s

peers. In the original HHDP data, a battery of 20 questions was used, each on a likert scale from

none to all. These items were summed to create the scales, which were available here as scales

rather than individual items. Because the T3 scale was on a different metric than the T1-T2

scales (e.g., 1, which represented “none” was recoded to 0 at T1 and T2 but not T3), at all three

times the measures were standardized such that they all had a mean of 0 and a standard deviation

21 Note that the response categories differed between Wave I and Waves II-III of Time 1. The response

categories at Wave I were 1=A, 2=B, 3=C, 4=D, 5=F. During Waves II and III, they ranged from 1=A, 2=between A and B, 3=B, 4=between B and C, 5=C, 6=between C and D, 7=D, 8= F. To make the response categories equivalent, for Waves II and III, categories were recoded such that 1 and 2=A, 3 and 4=B, 5 and 6=C, 7=D and 8=F. This was also done at T2 and T3, in which the grading coding was similar to Waves II and III of T1. Thus, for all Time periods, the range of possible grades will be 1 (A) to 5 (F).

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of 1. These three scales were significantly and positively correlated (p<.01) at all three time

periods. See Table 5.3 for more information on these items/scales.

Most of the above scales and items have been shown to be associated with problem

behavior in previous analyses of the youngest cohort of the HHDP (see Hancock, 1996). In

addition, preliminary analyses indicated that these covariates were significantly related to

crime/delinquency in the present sample, with the exception of nonwhite and the variety score.

[Insert Table 5.3 about here]

Domains of Maturation

For the purposes of the dissertation, five potentially distinct domains of maturation have

been identified. Possible measures of the domains as well as theoretical and empirical

justification were provided previously in the last chapter. Items representing certain of the

domains of maturation will be taken from all time periods of the HHDP. Some of the measures

used to represent maturation exist at earlier time periods but it would not be appropriate to

consider these as indicators of adult status, especially at ages 12 and 15. Nonetheless, the

theoretical framework of the dissertation is that indicators of maturation increase over time into

emerging adulthood and become entrenched around age 30. Thus it is necessary to demonstrate

that measures of maturation increase with age and, to an extent, covary with delinquency and

crime.

Adult Social Role Maturation

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For the most part, measures of social maturation, following the literature reviewed in

chapter III, involve adult social roles (e.g., romantic relationships, work/employment). While

some have considered adult social relationships to be a result or consequence of maturation (see,

e.g., Farrall and Calverly, 2006), there is sufficient evidence that such relationships are a part of

the transition to adulthood which implies they are a part of the maturation process.

Measures to represent social maturation are derived from the T3, T4 and T5 data

collection periods. Prior to these time periods, the individuals were aged 15 and younger, and

thus the numbers of those working and/or married were very small. Even at T3, however,

marriage or cohabitation was rare, with only 7 respondents reporting being in such a relationship.

Thus, the analyses of marriage and cohabitating relationships will primary utilize data from the

T4 and T5 surveys. Overall, adult social roles are non-existent in the data at ages 12 and 15.

Thus, the measurement of this domain of maturation predominantly begins at T3. In terms of

individual items, the following are included:

Advanced Beyond High School-Advanced beyond high school is a dichotomous indicator

of whether or not one has completed high school and/or enrolled in higher education. The

measure captures those who have continued onto post-secondary education compared to those

who either dropped out of high school or stopped their education upon high school graduation. It

represents a basic marker of independence and preparation for a skilled workforce. In the post-

modern economy, entrance into higher education is arguably a better indicator of at least

preparatory steps toward adult status than simply high school graduation. However, because at

T3, the sample was around 18 years old, a measure of graduated high school or enrolled in post-

high school education is used. At T3, the percentage of the sample who had graduated high

school or were enrolled in college was 44.2% (n=194). At T4, the percentage advancing beyond

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high school was 74.6% (n=312). At T5, this percentage was 79.9% (n=298). Using a simple high

school graduate measure would not be as meaningful as this measure at T4-T5, in part because of

a lack of variation. For example, at T4, only about 5% of the sample had not graduated high

school.

Job (Full Time)-Several items in the HHDP ask about employment (part-time, odd jobs,

etc.). For the purposes of this dissertation, full-time, stable employment is most relevant to an

adult social role. Thus, this measure consists of a dichotomous indicator of whether the

individual had full-time employment since the last time period, and also an indicator of whether

the person is currently employed full-time. Once again, at the first two time periods, the numbers

are very low (at T2, only 2.5% of the sample had worked full-time in the last 3 years and less

than 1% was currently working full-time), and thus at T1 and T2, part (steady) and full-time

work are measured. At T1, 9% had ever worked part or full-time (n=38) and 6% were currently

working full or part-time (n=25). At T2 22.7% had worked part or full-time in the last three years

(n=99) and 12.2% were currently doing so (n=51).

At T3, 21.9% (n=96) had worked full-time within the last 3 years and 21.5% (n=94) were

currently employed full-time. At T4, 41.4% (n=173) had been consistently employed full-time

(e.g., not fluctuating between part-time and full-time employment) and 78.2% (n=327) were

currently employed full-time. Finally, at T5, 64.6% (n=241) had been consistently employed

full-time in the last 7 years and 79.4% (n=296) were currently employed full-time. This increase

over time is consistent with the theoretical framework of the current dissertation.

Relationship-This measure focuses on marriage but also includes cohabitation (defined

here as living with a partner “as if” married). No individuals were in such relationships at T1,

only 5 reported being married or cohabitating at T2 and 7 reported these relationships at T3. In a

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modern society, in which adult social roles are not fully engaged in until the mid to late 20s, this

is perhaps not unexpected; however, it does restrict variation until the last two time periods. At

T4, 33% of the sample were in marital or cohabitating relationships (13.4% (n=56) cohabitating

and 19.6% married (n=82)). This excludes those who were divorced or separated. At T5, this

figure increases to 64.4% (n=240) (with 12.3% engaging in cohabitation).

Attachment to spouse/partner-Attachment is measured by a variety of items asking the

individuals with cohabitation or marital relationships about how satisfied they are with these

relationships. Because only 7 respondents had valid scores on these items at T3, reliability and

factor analyses cannot be stably performed. Utilizing these scores, and entering ‘0’ for those

without relationships is not done here because doing so would produce an item that is not

substantially different than the dichotomous relationship item described above. Thus, the

attachment to spouse or partner scales will rely on T4 and T5 data. Even at T4, however, there

are 280 missing cases, meaning no relationship was declared (67%). At T5, there are 134 missing

cases (36%).

The ‘attachment to partner’ items are identical at T4 and T5. They ask the respondent

how much they can count on their partner, whether he/she gets on their nerves, whether he/she is

disapproving, whether they quarrel and so on. Fourteen items are used, constructed such that

higher scores represent greater attachment. At both T4 and T5, alpha reliabilities were high (.92

at T4 and .90 at T5), and a factor analysis produced one major factor (eigenvalue over 6, with

other factors less than 1.3. This provides evidence of a psychometrically sound scale. The

average score on the partner attachment scale increases from a mean of 4.22 at T4 to a mean of

4.25 at T5.

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Attachment to work- Several items were identified in the HHDP data as potentially

representing feelings of attachment or security toward work. These include items asking about

satisfaction with work, whether one wishes to change their career, and whether the respondent is

making progress toward career goals. At T3, only 50 respondents had valid scores on the items,

and conducting intercorrelation or factor analyses on this low of a sample size would likely

produce unreliable results.22 Thus, prior to T4, the full-time work item are used. At T4 and T5, 7

items were identified that were intercorrelated. Reliability analyses indicated that 4 of these

items formed one scale (T4 α=.74, n=304) at T4.23 Factor analyses confirmed this, showing one

major factor, with all 4 items loading at higher than .6. These items asked whether the

respondent felt frustrated at work, were advancing quickly to his/her goals, had ample

opportunity to advance, and how satisfied he/she was with their job. At T5, two items were

added, producing similar psychometric properties (α=.74, n=255) with one factor extracted with

an eigenvalue over 1. The additional items were whether the respondent would ‘change their job’

if he/she could and whether she/she felt they had ‘made the right decision about your career

choice’. At T4 and T5 the items were averaged and then standardized using POMP scoring

(without multiplying by 100) to form the T4work_scale and the T5work_scale. Thus, each scale

score represents the proportion (rather than the percentage) of the highest possible score on the

work scale. The work scales have a max score of 1, with higher scores indicating greater work

attachment.24

22 Analyses indicate that five T3 questions asking about how individuals feel about work, whether they

have enough responsibility at work, etc. are not consistently correlated at p < .05.

24 Note: because traditional factor analysis assumes items have the same response category, at T5, the items were standardized and then analyzed again; the results were substantively the same. The Cronbach’s alpha reported here is for the unstandardized results.

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Children is a dichotomous indicator of whether the individual had one or more child. At

the first 2 time periods, no individual reported having a child. At T3, 3 individuals had children

(.7%), at T4, 56 individuals had children (13.4%), and at T5, 136 individuals had children

(36.4%). This increase over time is to be expected and theoretically relevant, suggesting that

adult social roles such as parenting increase over time. Tables 7.1-7.5 below provide full

descriptive information for these measures.

Civic/Communal Maturation

Civic maturation is measured by a series of items that represent whether the respondent

was involved in a variety of “in school” and “out of school” clubs and activities. In school

activities include organizations such as service, athletics, and school government. Out of school

activities include such organizations as scouting, service, religious and social groups. School

activities were only relevant for those in high school or lower, and so are not available after T3.

In addition, two activities (political and social) were not included in the first wave of T1, which

results in an increase in missing cases on those categories.

For the purposes of the analyses, these items were combined to create a sum of activities

or organizations the individual engaged in at each time (both in school and out of school). The

major interest is in activities or organizations that measure some sort of engagement with

generative behaviors (e.g., service, political groups). However, individual items tended to have

low positive responses (especially at T1 and T2, when .3% and 1.2% of the sample had engaged

in political activities. Even at T5, however, only 3.7% of the sample engaged in these activities).

It is interesting to note that the items were not consistently intercorrelated at each time period to

suggest that combining them into a scale was an adequate measure of a single underlying trait. In

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some sense, the activities may represent different aspects of communal or civic engagement.

Nonetheless, the purpose of the current analysis is not to determine types of communal or civic

maturation but rather to develop an overall level of communal or civic maturation. Thus a

summative score (number of different clubs or activities engaged in) serves the current

dissertation well by representing the degree of engagement in activities.

Number of school activities-is a summary measure of the number of different activities

the individual engaged in at school. In this way, it resembles the delinquency variety score,

described above. The options were: At T1, Wave I, scouting, service, sporting, recreational, and

religious groups (an ‘other’ category was not offered for the other waves or time periods and is

thus excluded). At the other waves and times, scouting, service, sporting, recreational, religious

groups, social and political groups were offered as options from which the respondents could

choose. This variable is available at T1-T3. At T1 and T2, the range is 0-6; at T3, the range is 0-

8. The alpha reliabilities are low for the school activities items, but this may be expected when

considering that each different group or activity is not an alternative indicator of an underlying

trait. Rather, the way they are used here is that an accumulation of groups/activities represents

more of a willingness to engage in volunteer work or behavior that requires some degree of

cooperation with others. The average number of school clubs or groups at T1 was 1.43. At T2,

the mean was 1.61, and at T3 it was 1.93. Note, however, because there were a sizable

proportion of individuals not in primary school at T3, there are only 290 subjects who responded

to the school clubs questions.

Number of out of school activities-Much like the in-school activities measure, the

number of out of school activities is a simple additive scale that represents the number of

different activities or groups the individual engaged in at each time period. The alpha reliabilities

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and psychometric properties of this scale varied considerably over the course of the study. The

number of possible groups or activities was 7 at T1-T3 and 8 at T4-T5.25 In terms of

descriptives, at T1, the mean number of groups was 1.38, at T2 the mean was .92, at T3 the mean

was .85, at T4 the mean was .91, and at T5 the mean was .94. It is interesting that this pattern

matches criminal behavior in an inverse fashion (i.e., the peak in offending corresponds to the

lowest engagement in communal or civic activities—age 18).

Finally, one item, which was available at T4 and T5, satisfaction with civic activities is

used. This item asked respondents how happy they were with their civic or church related duties.

‘Satisfaction with civic activities’ ranged from 1-5 with a mean of 3.29 at T4 and 3.25 at T5.

Nearly half of the sample responded “not-applicable” on this measure and are coded as missing.

Thus, this item was trichotomized such that if the score was “not-applicable”, they received a 0,

if the score was 1, 2 or 3 (very dissatisfied through neutral), they received a 1, and if the score

was 4 or 5 (somewhat satisfied and very satisfied) they received a 2. This ensures that the

majority of the sample has a score on the satisfaction with civic activities item. The mean of this

recoded item at T4 and T5 was .88 and .92, respectively.

Psychosocial Maturation/Personality

Because Cauffman and Steinberg’s (2000) conceptualization has been shown to be

related to crime over time (see Monahan et al., 2009), the measurement of psychosocial

maturation will rely on their work (focusing on Temperance, Responsibility and Perspective—as

described in Chapter IV). Conscientiousness and agreeableness, two traits that Blonigen (2010)

25 The number of possible out of school activities increased by two in the survey from Wave I to Wave II at

T1. Rather than lose information, these items are included in the summative measure, which means the Wave I respondents’ scores may be biased downward. However, the mean number of activities increases by only .02 by including the additional items, thus likely not significantly impacting the measure.

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argued increase over the life-course will also be measured. (Note that there is some overlap

between the personality characteristic of agreeableness and the psychosocial maturation category

of perspective—especially the notion of ‘consideration of others’. Conscientiousness appears to

overlap with temperance, described below. Thus, these constructs may be measured by the same

items).

Ideal measures of psychosocial maturation do not exist at each time period. Therefore,

the measurement of this domain varies over time; however, every attempt was made to ensure

psychometric and theoretical consistency. As mentioned above, psychosocial maturation is

measured using Cauffman and Steinberg’s (2000; Steinberg and Cauffman, 1996) three

components: Responsibility, Temperance, and Perspective.

Responsibility- Responsibility, in Cauffman and Steinberg’s (2000) scheme, refers to the

ability to think for oneself, to take care of oneself and relative independence. This construct

includes self-reliance, self-esteem, and independence. Measures used by Cauffman, Steinberg

and colleagues in their various publications are not available in the HHDP, but reasonable

proxies exist. To measure responsibility, several items/scales are used. First, at T1-T5, a set of

two items (on a 1-5 scale) asks individuals how “independent” and “confident” they are. As

expected, independence increases over time. Interestingly, confidence decreases until age 18, and

then increases thereafter.

In addition, at T3-T5, the 16PF includes a self-reliance scale (Q2 factor). According to

the 16PF manual, this scale represents whether an individual is resourceful, makes his/her own

decisions and is self-sufficient. This scale was recoded into low, middle and high scores (scores

on a 1-10 scale were considered high if over 7 and low if under 4, see Cattell and Schuerger,

2003). The mean score on this scale increases from T3 to T4, as theoretically expected, but then

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plateaus from T4 to T5.26 At T3-T5, there is a battery of 7 items that ask about how much the

individual feels they control their own outcomes (or their life is a matter of luck). The luck scales

are created by analyzing, at each time period, which items hung together, producing

psychometrically sound scales. At each time, 1 item was deleted from the battery of 7. The

Cronbach’s alpha was somewhat low (.65 at T1, .64 at T4 and .66 at T5), but the factor analyses

all showed the items formed one factor (as demonstrated by one eigenvalue over 1, all loading at

above .4 and the scree plots). The luck scales showed very little change from T3-T5, with a dip

at T4. In general, it appears that most of the sample feels that luck is not a large part of what

happens to them.

Temperance-As mentioned above, temperance refers to ability to control impulses and

avoid aggressive behavior. This component of psychosocial maturation is related to the

personality trait of conscientiousness. The measurement of this component of psychosocial

maturation focuses on impulsivity. Impulsivity is measured at all time periods, with the PRF at

T1 and T227 and the 16PF at T3-T5.

The HHDP researchers used 17 of the original 22 subscales, which were modified to

include 12 of the original 16 dichotomous items per subscale. This was done to speed up the

process of testing, and items were dropped in random fashion. Analyses by HHDP researchers

showed that the shortened scales were comparable psychometrically to the full scales (Bates and

Labouvie, 1997; Labouvie and McGee, 1986). Previous research with the HHDP has also

indicated that the PRF predicts substance use (Labouvie and McGee, 1986). In addition, research

26 Interestingly, this scale was not associated with the ‘independence’ item at any time period. It is possible

that these items represent different components of self-sufficiency. 27 The PRF was available at T5 but only in raw form. The manner in which these scales were constructed at

T1 and T2 was somewhat unclear and so they were not included at T5.

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has shown that certain of the PRF factors may change over time as earlier measurements of

impulsivity did not predict substance abuse into adulthood (Bates and Labouvie, 1997).

The PRF measure of impulsivity is a battery of 12 dichotomous items, coded so that

higher responses indicate less impulsivity such that higher scores represent more maturation. The

PRF measure of impulsivity decreases from age 12 to age 15, indicating increased impulsivity in

the teens (mean at age 12: 7.30; mean at age 15: 6.32). In addition, impulsivity, as measured by

the PRF, decreased by age 30/31 (mean 7.59). Lending support to the psychometric properties of

this scale, however, research has demonstrated high internal reliability of the impulsivity scale

(.92; White et al., 2001). The reliability scale for the present sample was lower at T5 (.71).28

Factor analyses also indicated one major factor (eigenvalue over 3; however, two other factors

were extracted with eigenvalues ranging from 1.3 to 1.2). Only the major factor is utilized here—

thus, there is one impulsivity scale at T5.

Another personality instrument, the 16 Personality Factors (16PF) (Cattell Eber, and

Tatsuoka, 1970; Cattell and Shuerger, 2003), also includes impulsivity as well, measured as a

second order factor entitled “self-control.” This second-order factor combines scales measuring

“compulsion”, “assertiveness”, and “inhibition.” This was given T3-T5. Previous analyses with

the HHDP have shown the 16PF subscales, as was the case with the PRF, to be related to

substance abuse (Labouvie, 1990). It should be noted that the 16PF factors are “bipolar” which

means that rather than representing more or less of one trait, scores on either the high end or low

end have distinct meanings. For example, with respect to the self-control factor, high scores (8-

10) represent high control and ‘inhibition of urges’ whereas low scores represent someone who is

28 Note that with respect to the 16PF and PRF scales, PRF individual items are available only at T5. For

other scales and other time periods, only the full scales are available, thus precluding reliability and validity analyses. The internal reliability estimate of .92 is from Jackson (1968), originator of the PRF (as cited in White et al. (2001).

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unrestrained (Catell and Shuerger, 2003). Analyses indicate that the 16PF self-control scale and

the PRF Impulsivity scale are significantly correlated at T5 (p<.05).

The 16PF includes a measure of “self-control”, which is a factor score combining raw

sten (standardized ten) scales from the questionnaire, ranging from 1-10.29 The self-control scale

includes the “sober”, “practical” and “controlled” factors (Catell et al., 1970). In this dissertation,

the equation from the latest edition of the 16PF was used, which is valid for data from earlier

versions (Steven Conn, 2011, personal communication). These scales were recoded into low,

middle and high scores (0-2, with “high” being a score over 7). Interestingly, the self-control

scores dip a bit from T3 to T4 (from a mean of .93 to .88) but then increase at T5 (mean=.99).

However, as expected, the number of individuals scoring “high” increases from 9 at T3, to 20 at

T4, to 36 at T5. Yet, the number of individuals scoring “low” also increases from T3 to T4.

Finally, a battery of items was identified as possibly representing control or impulsivity at

T4 and T5. They also may represent what Paternoster and Pogarsky (2009) call “thoughtfully

reflective decision-making.” Analyses indicated that of the identified items, five formed a

psychometrically adequate scale at both times, which I have labeled “behavioral restraint” (or

thoughtful decision-making). These items also seemingly reflect a rational choice orientation.

For example, the items all capture a tendency to think before acting and to have a plan set in

place. This is a close approximation of what Paternoster and colleagues (2009) refer to when

describing thoughtfully reflective decision-making. There are five dichotomous items in all,

which produce one factor (according to one eigenvalue over 2, factor loadings over .55, and

scree plots), and a Cronbach’s alpha of .67 at T4 and .68 at T5. Interestingly, there is

29 The equation for self-control is as follows: 3.85-.2F+.4G -.3M +.4Q3

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considerable stability between T4 and T5 in this measure (the mean at T4 is 1.66 and 1.63 at

T5).30

Perspective-As previously noted, perspective refers to the ability to think about and plan

for the future, as well as the tendency to think about others. In many ways this component of

psychosocial maturation overlaps with the personality trait of agreeableness. To measure

perspective or agreeableness, a battery of items from T1-T5 is used which asks the respondents

to describe themselves. These include how “helpful”, “understanding”, and “kind” the

individuals feel that they are. These items appear to form psychometrically sound measures at

each time period, forming one major factor (eigenvalue over 2 without another eigenvalue over 1

and all factor loadings above .6) and adequate Cronbach alpha’s (T1=.74; T2=.76; T3=.82;

T4=.79; T5=.80). These items were averaged into a scale called “Agreeableness.” The scores on

this scale increase from T1 (mean 3.95) to T4/T5 when it plateaus around 4.20.

Finally, the PRF includes a subscale entitled “cognitive structure”, which represents the

tendency to desire clear plans and a lack of ambiguity about the future. While this construct has

not been used in prior work to measure perspective, it appears, on its face, to be related to the

essence of what Cauffman and Steinberg (2000) mean by perspective (e.g., the tendency to

consider the future before taking action). Much like the impulsivity scale, the cognitive structure

scale includes 12 dichotomous items, which are summed. The cognitive structure scale of the

PRF includes 12 items such as “I very seldom make careful plans” and “I don’t like to go into a

situation without knowing what I can expect from it.” All items were recoded such that high

scores indicate more cognitive structure. Individual items are not available at T1 and T2. The

average scores on cognitive structure scale decrease from T1-T2 (T1=6.95; T2=6.46). Individual

30 It should be noted that in terms of face validity, a non-trivial indicator of how well particular items

represent a latent trait, the PRF items for impulsivity seem to a more direct measure of the trait than the 16PF scale.

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items were available to create a cognitive structure scale at T5, but the psychometric properties

were not sufficient (α=.60) and thus the scale is not used after T2 (see tables 7.1-7.5 for more

information).

Identity/Cognitive Transformation Maturation

Linked to the communal maturation processes described above, individuals often become

more conformist in their attitudes and come to view crime/delinquency in an increasingly

negative light and adhering to norms or rules in a more positive light (Caspi et al., 2005;

Giordano et al., 2002; Kins and Beyers, 2010; Paternoster and Bushway, 2009). The domain of

Identity or Cognitive Transformation Maturation draws from the literature that changes in how

individuals view themselves over time as well as how they view social institutions and antisocial

behavior lead to changes in actual behavior. This literature was reviewed extensively in the

preceding chapters. Instead of simply measuring how a person views him/herself, the interest

here is in measuring whether one views him/herself as a conformist (e.g., a non-criminal) and

whether one views crime or antisocial behavior as morally wrong. Thus the constructs used to

represent this domain of maturation are meant to capture the tendency for individuals to

increasingly view themselves as conventional adults and crime/antisocial behavior as something

to be avoided. It should be noted that nearly all of the literature on identity/cognitive

transformation and desistance is qualitative or theoretical in nature (see, e.g., Giordano et al.,

2002; Laub and Sampson, 2001; Maruna, 2001; Shover, 1985; 1996; Paternoster and Bushway,

2009; Vaughn, 2007). In this sense, the construction of quantitative measures of identity is

somewhat exploratory.

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To measure identity, a battery of four items, identical across all time periods, is used.

These items ask the individuals to rate themselves on a scale of 1 to 5, with respect to whether

they are “good”, “mean”, “dishonest”, and/or “delinquent/troublemakers.” All items were coded

such that higher scores indicate a more prosocial orientation. For example, those scoring high on

this measure would see themselves as “not” mean or dishonest, but rather “good.” These four

items were associated with low alpha reliabilities and inconsistent factor analyses. However, the

items are nearly all significantly intercorrelated (‘good’ is not significantly correlated with

‘delinquent’ at T1 and T3). Unfortunately, at T1-T3, they are the only measures of identity or

cognitive transformation. However, as expected, the items nearly all increase from T1 to T5.

Interestingly, the reversed item asking whether the individuals feel they are delinquent or

troublemakers is highest (indicating they do not feel they are delinquents) at T3. In some sense,

these somewhat inconsistent results are not surprising given the literature suggesting that

individuals can maintain more than one identity at one time (Paternoster and Bushway, 2009;

2011; Vaughn, 2007). Thus, the identity items may not be expected to be highly consistent.

To measure cognitive transformations, two measures are used. The first represents views

toward criminal acts. It is comprised of 6 items asking how much guilt or remorse the individual

would feel if they did such things as stole something worth less than $50, used force to get

money or other things from other people, and attached someone with the idea of seriously

hurting or killing him/her. These items are scored Strongly Agree to Strongly Disagree. Again,

scores are recoded such that high values indicate more prosocial attitudes. These items were only

available at T4 and T5, and at both time periods analyses indicated that 5 of the items formed a

single unidimensional measure, view crime. One item, referring to attacking one’s spouse or

significant other, was deleted. The Cronbach’s alpha was .86 and .83 at T4 and T5 respectively,

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and factor analyses suggested a single underlying construct accounted for the majority of the

variance between the items. This measure increases from T4 to T5, as expected.

Finally, again at T4 and T5, a battery of 16 items were available asking about honesty

and how important it is to do things without cheating or lying. Example items comprising this

measure are “you can make it at work without having to cheat or lie” and “it’s ok to lie to your

parents in order to keep their trust.” Items were originally scored 1-5, with a 5 representing

complete disagreement. Analyses indicated that 10 of the 16 items represented an adequate scale

(honesty), with Cronbach’s alphas of .84 and .87 at T4 and T5 (one factor extracted with an

eigenvalue over 1). This scale was created by taking the average of the items such that higher

scores indicate a more prosocial orientation, in conjunction with the other identity/cognitive

transformation measures. This measure is largely stable but increases slightly from T4 to T5 (see

tables 7.1-7.5).

Neurological/Cognitive Maturation

In order to measure neurological or cognitive maturation, it would be ideal to have data

on structural and functional brain characteristics measured on the same individual over time.

This would allow an assessment of the degree of myelination that has occurred and whether that

correlates with behavioral change. However, MRI data were not available in the HHDP.

Neuropsychological tests may be the closest proxy to brain maturation in most longitudinal

datasets (L. Steinberg, Personal Communication, February 6, 2011; see also Steinberg et al.,

2009b).

In the HHDP, neuropsychological tests were given starting at T3. These tests were

designed to measure intelligence and cognitive impairment. As such, they are not ideal for

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measuring cognitive or neurological maturation. Nonetheless, certain tests may capture positive

change over time. Further, research has shown changes in neurological functioning measured

through neurological tests (e.g., working and spatial memory, processing speed) are related to

cognitive maturation (Geier and Luna, 2009; Luna et al., 2004). Neurological tests include the

Halstead, WAIS, and Shipley Institute of Living Scale. All of these tests are available from T3-

T5 (thus, they are used to assess change from late adolescence to mature adulthood). Many of the

tests include raw and scale scores, the latter of which are normed using population data and

adjusted for age. Because the major interest is in examining changes in cognitive functioning

with age, raw scores are used (M. E. Bates, Personal Communication, August 24, 2011). A

description of these tests follows:

Halstead Subtests: 1) Trail-Making A and B—Requires the subject to connect circles in

order. B test includes “task-switching.” The Trail-making tests measure visio-spatial aptitude.

One of their purposes is to identify brain deficits, but they also measure processing and executive

functioning (see Tombaugh, 2004). The Booklet Category Test measures executive ability. The

test involves the ability to find solutions to problems and adapting to new situations (White et al.,

2001). WAIS-R Subtests: 1) Digit Span—The WAIS is an intelligence test. The Digit Span tests

memory functioning and is a measure of verbal intelligence. 2) Block Design—The Block

Design tests visual and motor functioning. The test consists of arranging same colored blocks in

a pattern. 3) Digit Symbol—The Digit Symbol is a test of performance IQ, which measures brain

deficits or dementia. Subjects match symbols to numbers as quickly as possible (see Kaufman

and Lichtenberger, 2006). Shipley Institute of Living (SIL): The SIL is an intelligence test that

includes two subcomponents: 1) a Vocabulary test—consisting of measures of vocabulary

proficiency and 2) an Abstraction test—which requires the use of logic. These tests measure

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general intelligence and brain deficits or impairment (Szyhowski, 2008; White et al., 2001).

Finally, the Spatial Relations Test is available at T4-T5, which is derived from Thurstone’s

Primary Ability Test (Bates and Tracy, 1990). It is a distinct measure of intelligence and

involves the mental manipulation of objects in space (see Pellegrino and Hunt, 1991).

All of the measures used in the HHDP are coded such that higher scores indicate better

cognitive performance, with the exception of the Halstead Subtests (Trail Making A and B and

the Category test). Because each test is a meaningful measure, they are not combined into

subscales prior to the creation of the domain score. The following tests are used: At T3, the Digit

Span Total Raw Score, the Block Design Raw Score, Trail Making A and B (seconds), and the

Category Test. At T4 and T5, the Digit Span Raw Score, the Block Design Raw Score, the Digit

Symbol Raw Score, Trail Making A and B (seconds), the Shipley Institute of Living Total Raw

Score (which combines the abstraction and vocabulary scores) and the Spatial Relations Total

Score are used. The Halstead Subtests (Trail Making and Category) were recoded so that higher

scores indicate better performance.31 It should be noted that several subjects’ scores were

invalidated because they had recently used illegal substances. This included 4 individuals at T3,

and 2 at T4-T5. These individuals’ cognitive test scores were coded as system missing. All the

neurocognitive scores increase or improve from T3 to T5. See tables 7.1-7.5 in chapter VII for

full descriptives on all maturation measures.

Analytic Strategy

31 Generally, when there is an upper and lower limit to an item, reversing can be accomplished by creating

a new variable using the following formula Newscore=1+X-Oldscore, where X is the highest possible score on the item. In the case of the cognitive tests, reversing was accomplished by simply multiplying ‘-1’ by the original variable.

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For the purposes of the dissertation, the primary analytic strategy utilizes a growth curve

approach assessing change in maturation and crime over time. Latent variables (here, the

maturation domains) are analyzed in a longitudinal framework to assess the association between

levels of maturation and changes in levels of maturation and crime. The relationship between

maturation and crime is examined using multilevel, longitudinal models in which time periods

are nested within individuals. However, the analysis proceeds in several steps, the first of which

seeks to identify empirically supported domains of maturation at each time period. In order to do

this, the items described above are subjected to factor analyses to determine whether they cluster

together to form identifiable scales. For the most part this initial analysis is exploratory. I do not,

for the most part, factor analyze a battery of items from pre-constructed scales. It is somewhat

unclear whether reliable and valid scales can be created to represent all of the domains described

above. Therefore, several criteria are used to ensure scales created have adequate measurement

properties. In some cases it is anticipated that certain domains may be multidimensional—that is,

a domain may have several factors that comprise it. In this scenario, separate factors are

maintained for the purposes of creating domain measures. Each of the steps involved in the

analyses is described in detail below.

Part I. Domain Construction (Research Aim 1)

For the most part, the results of this research aim are presented in the domain measure

section (above). However, further information on the maturation domains is presented in Chapter

VII. Scales that were pre-constructed by HHDP researchers were not subject to data reduction or

validation analyses for the purposes of this dissertation. I chose to use the pre-constructed scales

rather than recreate them using raw data because these scales have been validated in previous

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analyses and were meant as stand-alone measures, rather than to be combined with other items

(for example, the PRF and 16PF scales). Where possible, reliability analyses (e.g., inter-item,

test-retest) were conducted to assess the psychometric properties of the pre-constructed scales

(see above). Unless the evidence strongly suggested amending the scales (e.g., particular items

are very weakly correlated with the other items), these are used as intended by the HHDP

researchers. In some cases, when the scales are available in summary format, detailed reliability

or validity analyses were not conducted (previous analyses have examined these measures, see

measures section, above). Scales that fit this category include the neuropsychological tests, the

T1-T2 scales (PRF, work orientation, attachment to parents and school), and the 16PF factor

scales. With respect to the 16PF, however, so-called ‘secondary factor’ scales were created using

regression-based equations with the primary factors. In this case, it is possible to determine

whether the primary factors comprising a secondary factor are related to each other in the

hypothesized manner.

For the purposes of constructing and analyzing the maturation domains, several methods

of analysis were utilized. As an initial step, items that appear on their face to belong to particular

domains, from a theoretical standpoint, are identified (see measures section, above). Then

subscales, or scales that comprise components of particular maturation domains, are created in

several cases. To do so, reliability (e.g., Cronbach’s alpha) and factor analytic methods were

applied to determine whether the particular items are consistent and valid indicators of

subcomponents of the domains to which they belong. Factor analysis is a well-known method in

scale development, in which the variances and covariances of items are examined to determine

the number and structure of the latent traits underlying the data. The primary use of factor

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analysis is to uncover the underlying latent (unobserved) structure associated with particular

items.

There are two main types of factor analyses, namely confirmatory factor analysis (CFA)

and exploratory factor analysis (EFA). Some use EFA techniques to conduct analyses that simply

aim to reduce the amount of data or items and no empirical relationships are specified (Albright

and Park, 2009). Principle components analysis, the default in some statistical software

packages, is not suitable for the purposes of examining theoretical constructs, as it incorporates

both shared and unique variance and ignores the underlying latent structure of the data (Costello

and Osborne, 2005).

In contrast, CFA is a way to test whether theoretical relationships specified provide a

good fit to the data. In a CFA, the researcher must have a theoretical model in mind before

conducting the analysis. The relationships between items and latent traits (as well as between

latent traits) are specified by the researcher rather than allowing the program to uncover such

patterns without any restrictions (DeCoster, 2000; DeVellis, 1991; Long, 1983). Traditionally,

researchers have conducted CFA using structural equation modeling programs such as AMOS

and Lisrel.

Both EFA and CFA are arguably appropriate methods for the factor analytic strategy to

be used to identify empirically valid measures. The strategy to be followed involves examining

theoretically expected relationships between items and latent variables, which would seem to

favor a CFA approach. However, if theory guides the analysis, then traditional factor analysis

programs utilizing an EFA framework, rather than specifying an SEM, may suffice for the

purposes of the dissertation. In this sense, I am testing whether items identified a priori have the

theoretically expected relationship to the latent variable(s) of interest but I will not be

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‘confirming’ previous results or models. This “theoretically guided EFA technique” is also more

appropriate for the dissertation as the examination of the identified constructs is somewhat

exploratory. In addition, CFA using SEM techniques often requires a model with constraints to

be tested. However, the purpose of the analysis is simply to determine whether particular items

“belong” to shared latent traits and there are no hypotheses of factor loadings or other

constraints. Further, it is common for researchers to use EFA methods to assess construct validity

of newly developed scales.

In order to create empirically valid scales, items identified as theoretically belonging to

the same latent construct (see above) were entered into a factor analysis (using principal axis

factoring or maximum likelihood depending on the distribution of the data) to determine whether

the latent construct does indeed relate to the items in the expected manner. Well established

criteria are utilized (e.g., eigenvalues, scree plot, and factor loadings) to assess how well

particular items represent a given construct.32 To create the full maturation scores, items and

subscales are combined in a standardized format. Reliability/factor analyses were not performed

on the full maturation domains, however, because each is anticipated to comprise multiple

dimensions and thus not represent one consistent factor. The purpose of the dissertation is to

simply identify and measure the overall domains and determine how they relate to crime over

time.

Because the HHDP was not explicitly intended to measure the maturation latent traits of

interest in this dissertation, the overall domain construction involved the combination of items

and/or scales that have varying response formats. To account for this issue, standardization is

used so that each item or scale is weighted equally in the construction of the domain measure.

32 Other methods, such as CFA within an SEM framework using specialized software (AMOS, LISREL,

MPlus) may also be used to confirm the results. Programs such as LISREL and MPlus are more flexible in terms of handling items of varying response formats.

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This was done using Percent of Maximum Possible (POMP) scores (Cohen et al., 1999). POMP

scores have certain advantages over other types of scale conversion, including the creation of

scale scores that have substantive and intuitive meaning. POMP scores are created using the

following formula:

[1]

POMP = [(respondent’s score)/(maximum score)]× 100,

In formula 1, which is slightly different than that advocated by Cohen et al. (1999), each

person’s score on the domain is divided by the highest possible domain score. This is multiplied

by 100 to derive the POMP. Using POMP methods will produce a type of standardized score that

is comparable across time periods. As an example, suppose that social maturation is comprised

of three items and two subscales. If the items are all likert (ranging from 1-5), and the scales

range from 1-12, with high scores representing the most “mature” responses, the total possible

for this domain score is 39. The domain would then be constructed by summing the three items

and two scales and dividing by 39, producing a score that indicates the percent of maximum

possible on that domain (from a minimum score up to 100). These domain measures are used in

the longitudinal growth curve analyses described below.

In certain cases, there is no absolute highest possible value. For example, in some

neuropsychological tests, the number of errors a person makes will vary. In these cases, the

upper limit for the POMP score is simply represented by the highest score in the HHDP sample.

In addition, for domain measures in which there the items do not have a base of 0 for a response

category, a 0 for the POMP score is not possible. Thus, the range for the POMP measures varies

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according to the items that comprise each measure. Nonetheless this method still places each

individual on the same metric with respect to the maturation domains.

That the items change from time period to time period unfortunately introduces some

limitations into the analyses because of the variation in measurement construction over time. For

example, the measurement of impulsivity in the psychosocial domain is represented by the PRF

at T1, T2 and T5 and the 16PF at T3-T5. Changes from T2 to T3 in the standardized measure of

psychosocial maturation, particularly the impulsivity component may be due to developmental

effects or partially due to measurement changes. Thus, while every effort is taken to reduce

measurement bias, the results should be interpreted with these limitations in mind.

Part II. Growth Curve Analysis: Baseline Models (Research Aim 2)

An important first step in longitudinal or multilevel analyses involves the exploration of

the dependent variable between and within units of clustering. The main dependent variable in

the analyses below is delinquency/crime modeled as a dichotomous as well as a variety score.

Before fitting statistical models, exploratory analyses are conducted to assess the dependent

variable at each time point as well as over time. This will involve simple descriptive statistics,

linear models and graphing procedures to visualize trajectories of delinquency (Maltz, 2009;

Singer and Willett, 2003).

Next, multilevel models are used. In the data for this dissertation, repeated delinquency

and crime measures are nested within individuals. This structure calls for a multi-level or

hierarchical linear modeling approach to longitudinal data (Raudenbush and Bryk, 2002;

Hedeker, 2004; Hedeker and Gibbons, 2006; Singer and Willett, 2003). In these designs

traditional approaches to modeling (e.g., ordinary least squares regression) are not appropriate

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because observations are not independent. This produces incorrect standard errors. In addition, in

recent years, powerful and flexible techniques have been developed and refined that allow a

more nuanced analysis of change over time (Singer and Willett, 2003). In longitudinal

hierarchical linear models, outcome Yij is modeled as a function of an intercept and independent

variables that vary within and between individuals. This is shown in equation 2 (without

substantive predictors).

[2]

𝑌𝑖𝑗 = 𝑏0𝑖 + 𝑏1𝑖𝐴𝐺𝐸𝑖𝑗 + 𝑏2𝑖𝐴𝐺𝐸𝑖𝑗2 + 𝜀𝑖𝑗

Equation 2 is what is referred to as an “unconditional” growth model, without covariates

or predictors at level 1. Level 2 models incorporate time varying and time invariant factors to

predict change between individuals. The level 1 model is generally used to determine the shape

of the growth curve. Some researchers estimate an unconditional intercept-only model prior to

the growth curve model. This allows an examination of how the variance components are

partitioned between and within individuals (with an intraclass correlation providing an estimate

of clustering). In equation 2, b0i represents the individual’s initial status on the outcome

(delinquency), for example, when AGE=0. The growth parameter, 𝑏1𝑖, represents the linear rate

of change over time. The error term, ɛij, is included to account for differences between the actual

trajectory and the fitted trajectory. In equation 2, delinquency or crime is modeled as a function

of time. In growth curve analysis, to explore the shape of the change function over time, the age

or time variable is squared by simply multiplying the variable by itself (Nagin, 2005). The b2i

parameter allows the model to assess whether the growth curve is curvilinear in shape (as seen in

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the “age-crime curve”). It is essential to include this quadratic term, otherwise equation 2 is a

linear model and cannot assess ‘curves’ in terms of how crime changes over time.

In addition, as Singer and Willett (2003) discuss, centering the age or time variable often

helps improve the interpretation of the intercept. Here, age is centered using the mean age over

the course of the study, which is 20.1. Thus the interpretation of the intercept becomes the

average delinquency/crime at the mean age of the sample. Centering can take on many forms,

but using the mid-point of 20.1 is desirable on several counts. Centering at age 20.1 creates an

orthogonal relationship between Age and Age2, an important consideration in regression models.

This modeling decision also transforms the interpretation of the age coefficient to be the average

rate of change over the study, rather than at the initial starting point. Because the anchoring of

age at 20.1 is at early adulthood, the models are more appropriate for analyzing desistance (see

Laub and Sampson, 2003). In the models to be used in this dissertation, since the centering will

take place at the average age, we would expect by Age and Age2 to be negative (thus indicating

decreasing criminal acts from adolescence into adulthood). If age was centered at age 12, we

would expect Age to be positive and Age2 to be negative, illustrating the age-crime curve.

The level 1 model is a within-individual model. In level 2, which is the between-

individual model, the growth parameters from level 1 are modeled as a function of population

parameters. Error terms may be added to the intercept and/or slope to allow the intercept and the

coefficient of age to vary between individuals (Raudenbush, 2001). This often produces a “more

realistic HLM” (Hedeker, 2006: 218). This is sometimes called a “random coefficient” or a

“mixed” model (including a combination of fixed—in terms of the growth parameters—and

random effects—in terms of the individual level error terms). Thus, not only does this random

effects model allow individuals to have distinct starting points in terms of the growth trajectory,

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but the shape of the trajectory also can vary by individual. The level 2 model (here a random

coefficients model) is given by the following:

[3]

𝑏0𝑖 = 𝛽00 + 𝑣0𝑖,

𝑏1𝑖 = 𝛽10 + 𝑣1𝑖

Where b0i is the individuals’ average level of crime or delinquency (with an associated error

term), and b1i is the rate of change (also with an error term). Here we see that the individuals’

average level of delinquency and rate of change are modeled to be a function of the population

average (β0) and the population rate of change (β1). The error terms in each line indicate that

these effects can vary across individuals. Removing the term 𝑣1𝑖 from the equation would

produce a random intercepts model, in which the average initial status of delinquency could vary

across individuals but not the rate of change (Singer and Willett, 2003).

In this study, rather than traditional linear regression, logistic regression models are used.

The first type of dependent variable to be analyzed is a variety score, indicating the number of

distinct offenses engaged in at each time. This is not a traditional count style variable but can be

considered as a binomial distribution, in which each type of crime is a bernoulli trial and the

individual can either succeed (engage in the crime) or fail (abstain from crime). This type of

variable can be modeled using a logistic regression model (for example in Stata) by specifying

the binomial option along with the number of trials (9). The mathematical model is:

[4]

𝑃𝑟(𝑌 = 𝑦|𝜋) =𝑛!

𝑦! (𝑛 − 𝑦)!𝜋𝑦(1 − 𝜋)𝑛−𝑦

In equation 4, the probability of success on any trial (π)—or the probability of

committing any one of the 9 distinct crimes—is determined by the logit link as well as the

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covariates in the model. Here, Y represents the number of crimes engaged in out of the 9

“Bernoulli trials” (see Apel and Kaukinen, 2008: 48). This specification, as used here, creates a

multi-level, multivariate binomial equation. It should be noted that for data with low

probabilities of successes and a large number of trials, the binomial distribution follows closely

the Poisson distribution (see Rabe-Hesketh and Skrondal, 2008). Thus models using Poisson

regression is used in sensitivity analyses.

The second type of outcome to be modeled as a growth curve is a simple binary variable

that indicates whether or not the individual engaged in any of the crimes at each time period.

Multilevel logistic regression analysis is used to examine trajectories of involvement in crime

over time. These types of models are extensions of linear multilevel models and are interpreted

in much the same way, with parameters for average values and rates of change. Growth

trajectories using the two dependent variables are analyzed both by examining parameter

estimates and graphically.

Part III. Assessing the Relationship between Maturation and Crime (Research Aim 3)

The longitudinal analysis of change over time may be conducted using any of a variety of

methods. Each has specific advantages and shortcomings. The primary analysis used here is the

multilevel model for change. An alternative that is closely related to the multilevel model for

change is latent growth curve analysis or longitudinal covariance structure analysis (Duncan,

Duncan, and Stryker, 2006; Singer and Willett, 2003). Covariance structure analysis (CSA) is

similar to multilevel modeling in that repeated measures are analyzed in relation to observed and

latent variables. CSA however, is based on a structural equation modeling approach involving a

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measurement and structural model (Long, 1983). One of the advantages of the CSA approach is

the ability to specify the measurement model and structural model at the same time.

Research has shown that the multilevel and CSA models are “blurring” (Stoel, van Den

Wittenboer, and Hox, 2003: 18; Singer and Willett, 2003) and identical parameter estimates can

be obtained from the same data using both methods. However, differences do exist between the

methods. For example, time is treated as a variable in multilevel modeling whereas it is

represented by specifying the time period associated with each measure in CSA (Singer and

Willett, 2003). CSA is more flexible for alternative model specifications and for group based

analyses, is perhaps more beneficial to models that have multiple outcomes (rather than simply

crime) and for nonrecursive models in which the relationships between latent factors are of

interest. Multilevel models are more flexible in their handling of unstructured time data, which is

important in the present case because 73 individuals have missing data on the outcome at T5.

Further, multilevel models have been shown to be easier in terms of building models and more

efficient for computation (Chou, Bentler, and Pentz, 1998). In this dissertation, the multi-level

model I use that incorporates maturation is a simple extension of the growth trajectories, and

maturation is included as fixed and random factors (e.g., each domain at each time period are

modeled as well as the effect of changes in levels of maturation over time on changes in crime).

In addition, recent advances in statistical programs allow the advantages of latent growth curve

modeling (CSA) to be realized within a multilevel framework. For example, Skrodal and Rabe-

Hesketh’s (2004) program GLLAMM in a sense is a combination of both approaches—for

example providing factor loadings in a multilevel model (Stoel et al., 2003). Thus, a multilevel

approach is primarily used, with other specifications being examined where appropriate.

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The primary analyses of the relationship between maturation and crime over time will

proceed as follows. After developing the empirical estimates of the latent maturation variables at

each time period, those measures are entered into the multilevel growth model (specified in Part

II) in a series of steps. First each of the five domains are entered into the level 1 equation, along

with the covariates described above. This will be done for each maturation domain separately

(e.g., separate models). This will allow an assessment of whether individuals with higher levels

of maturation at each time period have lower levels of crime than would be predicted (Stoel et

al., 2003). Second, to assess whether maturation impacts the slope or rate of change, interaction

terms are calculated by multiplying the AGE variable by each maturation domain. This will

allow an analysis of whether higher levels of maturation are associated with greater change in

delinquency trajectories. This is shown in equation 5.

[5]

𝑌𝑖𝑗 = 𝛽00 + 𝛽10𝐴𝐺𝐸𝑖𝑗 + 𝛽20𝐴𝐺𝐸2 + 𝛽30𝑀𝑎𝑡𝑢𝑟𝑒𝑖𝑗𝑘 + 𝛽40𝑀𝑎𝑡𝑢𝑟𝑒𝑖𝑗𝑘 ∗ 𝐴𝐺𝐸𝑖𝑗 + 𝑣0𝑖 + 𝑣1𝑖𝐴𝐺𝐸𝑖𝑗

+ 𝑣2𝑖𝐴𝐺𝐸2 + 𝜀𝑖𝑗

In equation 5, delinquency/crime is modeled as a function of time, maturation, and

maturation by time. Notice that in this equation, level 1 and level 2 are collapsed into one

equation. Notice also that maturation does not have a unique error term at level 2, indicating that

it is not allowed to vary across individuals. This is done because the theory to be tested in this

dissertation suggests that the process of maturation results in a decline in crime and not that

maturation has different effects for each individual. However, a different form of [5] can be

tested by including a random effect for maturation and using model fit indices to determine

which model is best. This should be done with caution and guided by theory, as adding random

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effects can quickly increase the number of parameters one is estimating in any equation (Rabe-

Hesketh and Skrondal, 2008). Maturation is included in the level 1 equation because it is time-

varying thus cannot be in the between person equation. At level 1, maturation is included as well

as a new variable that is the product of maturation and age. This allows an assessment of the

impact of maturation on the average level of delinquency/crime and also on the growth curve

(Singer and Willett, 2003). In addition, it is possible to include maturation as time constant (as an

overall measure) to examine between individual effects.33 Maturation includes another subscript

that the other parameters do not have, k. This subscript is meant to denote the domain of

maturation (social relationship, civic, psychosocial, identity or neurological).

Finally, the main analysis of interest is in the longitudinal relationship between

maturation and crime. In other words, do changes in levels of maturation produce changes in

criminal/delinquent behavior? As others have noted, to isolate the within-individual effects of

changes in levels of maturation, it is necessary to modify the variable of interest (Laub and

Sampson, 2003; Horney et al., 1995; Piquero et al., 2002; Rabe-Hesketh and Skrondal, 2008;

Singer and Willett, 2003). Thus, maturation domains are group-mean centered, by subtracting

each individual’s overall average from their score at each time point. This centered version of

maturation is entered into the level-1 equation and the overall average is entered into the level 2

equation as a between-individual effect (however, the latter is not of general interest here, but it

allows an assessment of whether higher overall maturation is associated with lower crime). In

this model, the interaction between time and maturation will not be included.

33 Theory should always be tied to model choice. Thus, the decision to include maturation as a level-2 time

varying covariate or a level-1 time varying covariate should be guided by the theoretical foundation of any analysis. Including maturation as a level 2 covariate in the age/time equation implies that maturation directly affects age/time—that is, maturation accounts for the time age/time effect on crime. The theory advocated here is that maturation has an effect over and above age/time. Thus, the theory proposed here is more consistent with the choice of a model that includes maturation as a level 1 effect.

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Maturation Gaps and Interactions (Research Aim 3)

As specified in Chapters III and IV, the relationships between maturation and crime/desistance

may be somewhat complex. Certain theories suggest that because maturation is multi-faceted, it

is important to take more than one domain into account in analyzing its effect on crime. The

work of Moffitt (1993) and Greenberger and Steinberg (1986) suggests that if particular domains

of maturation are more advanced than others, a gap exists and this may be conducive to crime.

Theoretically not every domain is relevant in the maturation gap analysis. For example, it is not

anticipated that a high score on prosocial identity but a low score on neurological maturation

should lead to crime. The relationships to be tested in the dissertation will involve social

maturation, psychosocial maturation and identity maturation. This is driven by theoretical work

(e.g., Galambos and Tilton-Weaver, 2000; Giordano et al., 2002; Greenberger and Steinberg,

1986; Laub and Sampson, 2003; LeBel et al., 2008; Newcomb, 1996) that posits gaps in

maturation between these domains may lead to crime. For theoretical reasons, it is anticipated

that high social maturation but low psychosocial or identity maturation is related to crime,

independent of the effect of the domains on their own. As an example, Greenberger and

Steinberg (1986: 171) argued that “adultoids” are characterized by the “attainment of social

maturity—the assumption of adult roles—without the development of psychological maturity to

go along with it.” These individuals are also more likely to be involved in problem behaviors

(see Galambos and Tilton-Weaver, 2000).

The method to construct maturation gap measures starts by examining individual’s scores

on social maturation compared to psychosocial and identity/cognitive transformation maturation.

As Barnes and Beaver (2010) point out, some researchers have examined maturation gaps by

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using multiplicative interaction terms. However, doing so does not provide a measure of the

“disjuncture” between two maturation domains. Interaction terms are more appropriate for

determining whether the effect of one factor depends on the level of another factor. This is more

appropriate for conditional relationship hypotheses (e.g., does social maturation have a stronger

effect on crime if identity maturation is higher?). Thus, it remains the case that a consensus

regarding how to assess maturation gaps has not been reached in the literature.

In order to construct a continuous rather than a categorical measure of maturation gaps, a

modification of the index of dissimilarity is used. The index of dissimilarity is typically

employed in sociological research assessing the degree of racial segregation in particular locales.

However, it may be used as an innovative method to create a measure that represents the degree

of dissimilarity between two maturation domains—higher scores indicating more of a “gap.” A

modified formula of the diversity index is used (see also Barnes and Beaver, 2010: 1180; Barnes

et al., 2011):

[6]

d={p(M1)2-p(M2)2}

Where d represents the diversity score, Mi represents the maturation domain, and P

represents the proportion or percentage within each domain, i. Typically, the index of

dissimilarity ranges from 0-1, with higher scores indicating more dissimilarity or diversity.

Adapted to the present purposes, d represents the degree of maturation gap between two

domains. In this specification, the range is -1 to 1, with negative values arising if the second

maturation domain is higher than the first. Because the focus in this dissertation is on

“precocious” adult role behavior (e.g., engaging in adult roles without the requisite psychosocial

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or identity maturation), social role maturation is the first term in the equation. Thus, following

Barnes and Beaver (2010), a negative or 0 score indicates no social role maturation gap and a

positive score indicates the presence of a gap. Positive relationships between social role

maturation gaps and delinquency are expected. This test is more relevant to the “adultoid”

argument than of Moffitt’s (1993) biological maturity gap, which argues that youths who are

biologically mature but not able to obtain adult independence are more likely to be delinquent.

As an example, consider subject A, whose social relationship maturation score is .75 of

the total possible and whose psychosocial maturation score is .50 of the total possible. A’s

diversity index or maturation gap score would then be:

d={(.75)2-(.50)2}

d=.3125

In this example, A’s maturation domains are “relatively” balanced. Consider subject B, whose

scores are respectively .97 and .05. This person’s index score is close to 1, at .9384. It is clear

then, that subject B has much more of a “gap” between his/her maturation domains. In this

analysis, higher gap scores are expected to be positively related to crime, following the literature

reviewed above.

For the sake of simplicity, the diversity maturation gap scores are analyzed such that gaps

at T4 and T5 are used to predict delinquency and crime at T4 and T5. This is not a multilevel

model but it allows for a contemporaneous assessment of the effect of gaps on crime. Lagged

effects will not be explored due to the large time gap between T4 and T5 (6 years). The gaps

analyzed below include adult social role maturation compared to psychosocial and identity

maturation.

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Finally, a similar strategy is used for the conditional hypotheses to be tested in this

dissertation. As noted, several of the maturation domains have been posited to be related to one

another in such a way that the effect of one depends on the level of another. This is the case for

identity/cognitive transformations, psychosocial and social relationship maturation in particular.

Rather than utilizing multiplicative interaction terms (which are sometimes difficult to interpret),

the effect of particular domains is assessed in models split by a moderator variable. For example,

the effect of social role maturation is assessed in a sample of “high” psychosocial maturation and

compared to a sample of “low” psychosocial maturation. Coefficients will not be formally tested

but examined for differences with respect to statistical significance.

The maturation gap and condition models will not be analyzed using multilevel models

because these are in essence exploratory models. In addition, the social maturation domain is a

key part of these analyses and, as indicated above, simply lacks variation at T1, T2 and largely

T3 to allow for a meaningful assessment of maturation gaps or interactions. Finally, there are no

hypotheses to suggest that maturation gaps or conditional relationships early in the life-course

should impact crime. Therefore gaps/interactions at T4 and T5 predicting crime or desistance at

T4 and T5, respectively, is the focus in these supplementary models, focusing on the variety

score. Relationships between maturation domains at T4 and crime at T5 are of greatest interest

due to the increased variance in social relationship factors within the HHDP later in the life-

course (e.g., after the individuals have reached their mid-20s).

It should be noted that the above analysis plan is not without shortcomings. In particular,

there are a host of alternative methods and models that could be used to address the research

aims specified in Chapter IV. Thus, while I argue the plan adequately addresses each aim,

flexibility is incorporated such that if a variation on the analyses is warranted, modifications will

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be explored. For example, analysis is conducted on the distribution of the dependent variable

(specifically the variety score) to determine which model is most appropriate. In addition,

because measurement is a limitation of the dissertation, sensitivity analyses are conducted to

ensure that the results are not spurious.

Sensitivity Analyses

To ensure that the results of the present study are not spurious or lack robustness, several

forms of sensitivity analyses are pursued (see results chapter IX). First, it is likely that the

process of maturation is different for females than for males. To be sure, this is true with respect

to biological or physical maturation (Newcomb, 1996), but may not be as relevant to the domains

identified in this dissertation. Nonetheless, analyses of the distribution of crime/delinquency are

undertaken as well as maturation processes by sex. It may be the case that there is too little

variation in criminal behavior for females to provide meaningful analyses. This subgroup

analysis will also be performed by race/ethnicity for descriptive purposes.

Second, sensitivity analyses are conducted with varying constructions of

crime/delinquency. As noted above in this chapter, of the nine types of behaviors used in the

main criminal behavior construct, the highest prevalence is for minor offending (minor theft,

avoiding payment). It is important to determine whether particular results obtained and described

in the proceeding chapters (for example, the distribution of criminal or deviant behavior) apply

to different constructions of criminal behavior. However, it should be noted that the theoretical

framework advanced here is most relevant to general population offending, rather than high risk

offending, and thus it would not be unexpected if the results differ for more serious criminal acts.

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CHAPTER VI. RESULTS: DELINQUENCY & CRIME OVER TIME

One of the major aims of life course criminology is to adequately describe the development of criminal behavior over the life span. The simplest way of visualizing such development is to plot the behavior of

interest…against age. However when individual variation in development is expected, more complicated methods are needed to do justice to the complexities of the data gathered. That is why criminologists have turned to growth

curve models. (Blokland and Nieuwbeerta, 2010: 65)

Introduction

The major aim of this chapter is to explore, in detail, the measures of crime and

delinquency used in this dissertation. As mentioned in Chapter V, the main outcomes are two

versions of crime and delinquency, a variety score and a dichotomous measure. The chapter will

begin by describing these measures over time and by subgroups (e.g., race and sex).

This chapter then seeks to provide a comprehensive description of both outcomes. In

addition to exploring crime and delinquency by groups, the main focus of this chapter is to report

the unconditional growth models that will form the basis for the multivariate multi-level models

(e.g., random effects) in the next chapter. These models will be used to examine the relationship

between maturation and crime/desistance. Thus, it is important to develop a stable unconditional

model, which involves determining the functional form of the curve and whether multiple levels

are required.

Description of Crime Over the Life-Course

Expanded Crime Analysis

Chapter V displayed a graph of both measures of crime against age, as recommended in

the opening quote to this chapter. That graph (Figure 5.1) illustrated that criminal behavior in the

HHDP peaks around age 18 then declines thereafter. This is true for both the variety scale score

as well as the dichotomous indicator. Interestingly, in this sample, the prevalence of criminal

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behavior is very low, less than 30% at T5. This is the lowest incidence of any time period, even

compared to age 12. The same trend holds for the variety score which is lowest at T5.

Theoretically, it is possible that this pattern has as much to do with the changing nature of

behavior and opportunities over the life-course as it does with individuals “making good.” In

other words, petty theft, vandalism, etc. are more youthful endeavors and older individuals

engage in different forms of deviance (e.g., pilfering). Because my concern was to maintain the

same measure of offending over time, adult-oriented crimes such as pilfering and embezzlement

were not included in the measures of delinquency at later time periods. Thus it is important to

determine whether changing the types of acts included in the outcomes at later time periods

results in a different pattern.

To explore this question, in this chapter I examine an alternative version of the

delinquency measure. This measure is altered such that at T4 and T5, they include the following

acts: embezzlement, forgery, used another person’s credit card, and fencing. As is shown in

Figure 6.1, including these four additional, adult-oriented acts does not change the basic

trajectory of crime over the life-course for the entire sample. For both the dichotomous and the

variety score measure, crime peaks at age 18 (T3) and then declines thereafter. Thus the finding

of desistance from crime after age 18 is robust even including additional crimes at later periods.

[Insert Figure 6.1 about here]

Delinquency/Crime Analysis by Sex

It is also well-known that males commit far more delinquent acts than females,

throughout the life-course (Gottfredson and Hirschi, 1990; Zimmerman and Messner, 2010;

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Uggen and Kruttschnitt, 1998). One concern with the current dissertation might be that female

criminality in the present sample is too low to allow stable analyses. This is especially true for

the types of offenses examined here, which are typically dominated by males, whereas females

are more likely to engage in such offenses as shoplifting and prostitution (DeLisi, 2002). Thus,

delinquency and crime were plotted over time for males and females separately to determine if

the same general pattern holds.

Figure 6.2 displays the results for the variety score. Consistent with the extant research,

males commit more delinquency than females, and this is true at each time point. The largest

differential occurs at T4, when the male mean variety score is nearly 3 times that of the female

mean variety score. Interestingly, the peak in female variety score occurs at T2, rather than T3

for males as well as the total sample. For the dichotomous measure (not shown), the peak for

both males and females occurs at T3. Importantly, the amount of delinquency for females is non-

trivial, as the lowest percentage of females engaged in crime at any time point is still over 13%.

In addition, the pattern of crime (peaking in adolescence and declining thereafter) is the same for

both males and females, and this is true for both measures of delinquency/crime. In the language

of the growth models, this would imply that males and females do not have differential rates of

change in delinquency over time.

[Insert Figure 6.2 about here]

Delinquency/Crime Analysis by Race/Ethnicity

Finally, Figure 6.3 displays delinquency over time by race (variety score). As shown,

whites have higher scores than non-whites at each time point except for T3. At T3, the variety

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score for nonwhites spikes to around 1.4. While nonwhites (particularly blacks) have often

shown higher delinquency levels in previous studies it should be noted that 92% of the current

sample is white and the nonwhite category combines black, Oriental, and mixed. Thus the

nonwhite category includes a relatively high delinquency group (black) and a relatively low

delinquency group (Asian) (Rocque, 2010). Again however, the general shape of the

delinquency/crime curve is similar for both whites and nonwhites. This is also true for the

dichotomous measure, for which whites have a higher mean score at each time point. Analyses

of blacks vs. nonblacks were not conducted because of the small number of blacks in the sample

(n=27, 6.1%).

[Insert Figure 6.3 about here]

In sum, these admittedly descriptive but nonetheless important analyses have

demonstrated that while there are clear differences in the HHDP sample in terms of level of

crime or delinquency, the general shape of the age-crime curve is roughly the same for males and

females and for whites and nonwhites. In addition, the level of delinquency for each subgroup

appears adequate for analyses and does not seem to warrant the elimination of particular groups.

Unconditional Growth Models

The first step in a multi-level longitudinal model is to establish the baseline or

unconditional model in which the only independent variable is time or age (Singer and Willett,

2003). I first calculate a random intercept model akin to equation 1, in which the intercept but

not the slope (here, of time) is allowed to vary across persons. Next, I calculate a random slopes

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model and examine the fit statistics to determine whether random slopes are appropriate.

Because this is a growth curve model, Age and Age2 are included in the model. In their raw

forms, only the random effect of Age was estimated; the random effect of Age2 could not be

estimated. Thus, following Nagin (2005), Age was divided by 10. This is done to ensure the age

terms are all “within the same order of magnitude” (Nagin, 2005: 44). The random effects for

Age/10 and (Age/10)2 terms were estimable in the models. The sign and significance of this term

tell us about the shape of the trajectories—i.e., whether a polynomial is required.34 Recall that in

these models, age is centered at the sample mean (20.1) to ease interpretation of the results.

Variety Score

The first set of results show crime over time using the main dependent variable, the

variety score. This model is calculated with a multi-level logistic regression equation specifying

the number of trials in the data (see equation 3). The results of the unconditional model using the

variety score show that crime is declining, on average, at the mean age. For example, in model 1

of Table 6.1, the Age and Age2 coefficients are both negative and significant (-.36 and -1.36,

respectively), showing that on average over the course of the sample crime is decreasing. If Age

is centered at the beginning age (age 12), and the model is recalculated, the age coefficient is

positive and significant while the Age2 coefficient is negative and significant (model not shown).

This demonstrates that crime increases from age 12 to early adulthood, whereupon it declines—

confirming the descriptive results shown above. In model 1, the only random component is the

intercept, which is significantly different from 0.

34 Age3 was not included for theoretical and statistical reasons, though in the baseline random intercept

models, it was positive and significant. The random coefficients for the model with age3 were not estimable. Graphing the predicted trajectories with age3 showed that the growth curves plateaued after T4. This is to be expected given the large decline in crime from T3 to T4.

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In addition, model 2 of Table 6.1 shows the results for the unconditional growth model

using the variety score with the inclusion of random coefficients for Age and Age2. Much like

model 1, Age and Age2 are significant and negative. The random effects for the intercept, Age

and Age2 terms are also statistically significant. A likelihood-ratio test shows that this model is

superior to the random intercepts model (p<.000), as the log-likelihood score increases with the

inclusion of the random coefficients.

[Insert Table 6.1 about here]

Dichotomous Measure

The second set of results, displayed in model 3 of Table 7.1, show the growth curve

model for the dichotomous delinquency/crime measure. This model is fitted using logistic

regression and by specifying random intercepts. The results show a similar story to that found

with the variety score. Model 3 indicates that at the mean age/time period, the estimated log odds

of engaging in any of the nine acts increases by .64 per time period. Both the Age and Age2

terms are significant and negative (-.64 and -1.51, respectively) suggesting that crime is

decreasing by the mean age of the sample.

Model 4 of Table 6.1 illustrates the unconditional growth curve with random effects

included for Age and Age2. Once again, Age and Age2 are significant and negative. A likelihood-

ratio test of whether the additional parameters required for random coefficients is warranted

reveals that this model is a better fit to the data than the simple random intercept model (p<.000).

In other words, a model, in which the effect of age (and Age2) on delinquency/crime is allowed

to vary across individuals is more realistic for these data.

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Note also that the variance components of both the dichotomous and variety models are

informative. The variance components can provide information about how well the individual

growth terms are predicted. As can be seen, variance components include the intercept, Age,

Age2, and the covariance between these items. This model also shows the variance components

for the random effects (intercept, Age, Age2, and the covariance between these items).

Interestingly, the variance of the intercept decreases in both random coefficient models, as

compared to the random intercept models. The covariance between the intercept and age is

positive and significant at the p<.05 level, which indicates that the higher the level of

delinquency or crime at the mean age, the greater the rate of change. The covariance between the

intercept and Age2, however, is not significant. Finally, the results of the baseline unconditional

growth models provide information about how much clustering there is inherent in the data. In

other words, how much of the total variation in delinquency or crime is between individuals? In

the language of multi-level models, this is known as the Intra Class Correlation (ICC), and is

often used to determine whether multi-level models are necessary (Raudenbush and Bryk, 2002).

In this instance (focusing on the dichotomous model), the equation for the ICC is as follows

(Hedeker and Gibbons, 2006):

[6]

𝜎𝑣02 /(𝜎𝑣0

2 + 𝜋2

3),

which is simply the between-individual (intercept) variance divided by the total variance. The

term 𝜋2

3 is the variance of the latent trait which, as Hedeker and Gibbons explain, is “assumed to

be distributed as a standard logistic distribution” with a variance of 𝜋2

3 or 3.29 (Hedeker and

Gibbons, 2006: 158). Using data shown in model 3, the ICC of the dichotomous delinquency

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measure is calculated to be .30, which means that 30% of the variation in delinquency resides

between individuals (rather than within individuals). The corresponding score for the variety

baseline model is .26. Thus, a larger proportion of the variance in delinquency is within-

individual. While the ICC is not overwhelming, it is certainly high enough to suggest that the

results would be biased were the individual level not accounted for in the multi-level format used

herein.

In sum, these results help to establish the shape of the growth trajectories and the baseline

parameters for the models that will be calculated in the following chapters. As was shown, crime

decreases after age 20, which is a typical desistance pattern. In addition, the age and intercept

variance components showed considerable variation that remains to be explained. In the analyses

to be conducted in Chapter VIII, these growth models will be expanded to include covariates (the

control variables and the maturation domains).

Discussion and Summary

This chapter has presented descriptive analyses of delinquency/crime over time as well as

the baseline, unconditional growth models. The first part of the chapter described

delinquency/crime in detail, breaking down trends by expanding the dependent variables by

sex/race. The results indicated that adding four “adult-oriented” offenses at T4 and T5 did not

substantively change the shape of the age-crime curve. In addition, while males offend more than

females and non-whites have fewer offenses than whites in the HHDP sample, all groups show

non-trivial levels of offending. This suggests that isolating the sample to one group may not be

fruitful. Nonetheless, in the analyses presented below, subgroup differences will continue to

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receive attention. This is especially true for the analysis of maturation and sex, which will be

examined in more detail in the next chapter.

The second part of the chapter presented the results of the baseline unconditional growth

models, using the variety and dichotomous measures as dependent variables. These models

largely told a similar story, with both age and age2 significant and negative and a substantial

amount of level 2 variance that has yet to be explained. These models will form the foundation of

the main analyses, to be presented below.

In what follows, I present multi-level growth models for the five maturation domains.

These models will test whether the growth follows a linear or quadratic pattern. This trajectory

analysis will also determine whether there are differences in maturation over time by group (sex

and race/ethnicity). Models will be presented for each of the five maturation domains, as well as

the average total maturation score. This will be followed by the primary analysis, in which

delinquency/crime is modeled over time as a function of maturation.

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CHAPTER VII. RESULTS: MATURATION OVER TIME Traditionally, the transition to adulthood involves establishing emotional and economic independence from parents

or, as historian John Modell described it, “coming into one’s own.” The life events that make up the transition to adulthood are accompanied by a sense of commitment, purpose and identity.

(Furstenburg, 2004: 34)

Introduction

In this chapter, I explore maturation over time. This will provide a thorough description

of the main variables used in the analyses to follow. A discussion of how the maturation domains

were constructed, along with the items and subscales that comprise them is first presented. Next,

the chapter will describe maturation by subgroup (race and sex) and also analyze maturation over

time using random effects growth models.

Analysis of Maturation Over Time

Domain Scores

To begin, I describe how the domain scores were created as well as provide some

descriptive statistics pertinent to them. To create the overall domain scores, the items and scales

described above and shown in Tables 7.1-7.5 were first standardized using POMP scoring at

each time period. The number of items/scales (and even domains) is not uniform across time

periods but the standardization serves to place each maturation score on the same scale, here

ranging from a minimum of 0 to a maximum of 100. As an example, for the T1 social role

maturation domain, there were five dichotomous items included in the score—graduated high

school, full-time job in the last three years, full-time job currently, had a child and significant

relationship. Thus, the highest possible score for this domain was 5. The sum of the individual

items was then divided by this number and multiplied by 100 to achieve the final POMP score.

In cases in which a highest possible score was unclear (e.g., the neurocognitive tests), the highest

140

score achieved in the sample was used (see Chapter V) as the upper limit in the calculations.35

Once again, for certain domains, where not every item had a response category of 0, the lower

limit of the POMP score is greater than 0. For example, if a POMP scale was created with 4

likert items, with response categories from 1-5, the lowest possible POMP score would be .20,

rather than 0.

[Insert Tables 7.1-7.5 about here]

For the most part, each domain was created by requiring full data on all items, so that

individuals with much missing data on particular domain items/scales do not have their score

determined by one or a few items. However, in cases in which missing data on particular items

was expected (for example, the relationship attachment scales), full data was required on 70% or

more of the items comprising the domain variable. For example, at T4-T5, if an individual had

less than 5 valid responses out of the 7 items in the social role maturation domain, he/she was

coded as missing on that measure. This procedure means that there is little missing data on any

of the maturation domain measures. The measure with the lowest N is T5 neurocognitive

maturation (N=356, of a total possible 373, 95%). Thus no imputation for the maturation

domains was conducted for the analysis.

It is interesting to note that most of the domain scores increase over time. However, civic

maturation decreases until T3 and then increases thereafter, which is the inverse of the pattern for

crime. Psychosocial maturation also seems to peak around age 25. The relationships between the

maturation domain scores are intriguing. Lending credence to the notion that there are multiple

35 Because the coding of certain neurocognitive tests resulted in negative scores, some domain calculations

for particular individuals were less than 0. These were recoded to 0.

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distinct domains of maturation is that the domain scores are not all significantly correlated with

one another at each time period. The relationships vary; at T1, civic and social maturation are

related. At T2, civic and psychosocial maturation are significantly correlated. This points to

change over time within and between domains of maturation. However, at T5, all of the domains

are significantly intercorrelated (with the exception of neurocognitive maturation, which is only

correlated with social role maturation). In addition, the within-individual correlations of the

domains (i.e., when the data are restructured into multiple observations per individual), all of the

domains are significantly correlated with the exception of psychosocial maturation and

neurocognitive maturation.

Tables 7.1-7.5 also show a total or average maturation score at each time period. This

was constructed by simply averaging the domains. As is shown, the average maturation scores

increase over time, as would be expected. These scores are all significantly intercorrelated, with

the exception of T1 and T5. From these scores, a total maturation (which does not vary across

time) score can be calculated. For this sample, that mean score is 48.06.

In the analyses that follow, the focus is on the overall domain scores. However,

supplemental analyses will allow a better understanding of whether particular aspects of the

domains are related to crime or desistance. As noted above, certain items and measures are

identical across all time periods and others are identical at three or more time periods.

Figure 7.1 displays all domains of maturation over time. As can be seen, maturation tends

to increase over time for each domain. However, as noted above, there are some interesting

discrepancies between these domains. For example, while adult social role, identity/cognitive

transformation, and neurocognitive maturation domains increase nearly linearly, civic maturation

and psychosocial maturation do not. Both civic and psychosocial maturation dip after T1. Civic

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maturation decreases until T3 and then increases. Psychosocial maturation increases after T2 and

then appears to stabilize. It should be noted that these results should be interpreted with caution

as the overall domain score is sensitive to which variables are included in the calculation and

how those variables are coded. Nonetheless, the results do appear to indicate that the

measurement of maturation has succeeded in tapping growth over time. This lends support to the

criterion validity of the measures—that they are representing a key form of development.

[Insert Figure 7.1 here]

In addition, comparing the results shown in Figure 7.1 with the pattern of

delinquency/crime over time is informative. For example, because most of the maturation

domains increase linearly, certain domains (e.g., identity/cognitive transformation, adult social

role) may not be able by themselves to explain the increase in delinquency/crime in late

adolescence, but may be more useful in explaining desistance. Maturation gaps during these

years may prove better able to help understand the peak in offending during adolescence (see

Agnew, 2003; Moffitt, 1993).

In terms of subgroup analyses, I begin with sex. Previous literature has posited that

females mature faster than males. This appears to be the case for biological or physical

maturation, which involves such things as pubertal development and physical growth (Rogol,

Roemmich and Clark, 2002), as well as brain maturation (Lenroot et al., 2007). However,

research on cognitive maturation by sex is still nascent. While there are expected differences in

rates and levels of maturation by sex, the measures used in this dissertation are not related to

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puberty but more social and psychosocial in nature. Thus, whether males and females differ on

these measures remains an open question.

Analyses indicate that the domain in which the most differences by sex emerged was

identity/cognitive transformation. Four of the five time periods showed significant differences,

with females scoring higher on this domain. Figure 7.2 displays scores on identity/cognitive

transformation by time and sex.

[Insert Figure 7.2 here]

Other domains showed males scoring higher at certain time periods (for example, T2 on

the social maturation domain). This may represent “precocious” maturity and be a key area to

focus upon during the maturation gap analysis (Carbone-Lopez and Miller, 2012).

In terms of race, again the analyses are limited because the sample used here is nearly all

white. However, a few interesting patterns emerged in the subgroup analysis by race. For the

most part whites and nonwhites had similar scores and patterns. Where differences emerged,

nonwhites often had higher scores (e.g., on psychosocial, identity, and civic maturation).

However, whites had higher social role and neurocognitive scores than nonwhites.

Neurocognitive maturation was the only domain for which there was a consistent significant

difference, with whites scoring higher than nonwhites at each time period (see Figure 7.3).

[Insert Figure 7.3 here]

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Thus, interestingly, the maturation scores appear largely similar for whites and nonwhites

in this sample. This is largely confirmed by the finding that there is only one time period for

which significant differences are found for total maturation scores (T2), with nonwhites scoring

higher than whites (data not shown). After T2, whites have higher total maturation scores

(though these differences do not reach statistical significance).

Growth Trajectory Models

To further examine maturation over time, the next set of analyses focuses on multi-level

growth curves, similar to those estimated in the previous chapter for delinquency/crime. As

before, the results will include first a random intercept and then a random coefficient model, for

each domain. Age will also be centered, but whereas for delinquency/crime it was centered at the

mean age (to examine desistance), age will be centered at age 12 for these models. This will be

done in order to examine how maturation changes over the entire study period. Once again, age

was divided by 10 to facilitate estimation. In addition, unconditional growth models as well as

conditional models will be examined, but the covariance of the random effects will not be

modeled (as was done in the previous chapter). Because one of the goals of this chapter is to

determine whether group differences in maturation exist in the HHDP data, race and sex will be

included in the level 2 equations. Race and sex are constants over time and are thus called time-

invariant variables.

The choice of the model is also slightly different than the growth curves presented in the

previous chapter. The dependent variables here are not counts or dichotomous indicators. Rather,

the maturation domains are continuous and, for the most part, appear relatively normally

distributed. Social role maturation is somewhat skewed, however. Linear mixed effects

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regression are used in the models that follow (rather than logistic, Poisson, or negative binomial

models). Again, the purpose is to assess whether the effect of age is significant and linear. The

degree of clustering and variance components are not of as primary concern here, as the

maturation domains are not the main dependent variable. Finally, the total maturation score

(which is the average of the five domains at each time period) will be examined in the final

models.

Table 7.6 displays the results of the models for social role maturation. As might be

expected, both age and Age2 are significant. Interestingly, Age2 is positive, which implies that

the rate of change in social role maturation is nonlinear as individuals age. Model 2 includes

random coefficients for Age and Age2. In this model the variance of the constant decreases

substantially from model 1. In addition, the variance of Age and Age2 are significant implying

that there are factors not in the model that may explain differences in social role maturation

changes over time.

Model 3 includes the covariate “male”, which is a dichotomous variable scored 1 if male.

Interestingly, the coefficient is not significantly different from 0. This is not necessarily

surprising since the difference between males and females in social role maturation at T1 was not

significant. However, when adding a male by age interaction to the equation (model 4) we see

that males now have a higher initial value than females and a slower overall rate of change.

Finally, model 5 adds nonwhite (another dichotomous variable) to model 2. Neither this variable

nor an interaction of nonwhite with time (not shown) is significant.

[Insert Table 7.6 about here]

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Table 7.7 shows the results for civic maturation. Both model 1 and model 2 (random

intercepts and random coefficients) show that Age and Age2 are significantly related to civic

maturation. Age is negative and Age2 is positive, supporting the descriptive result that showed

civic maturation decreases then increases over time. In these models, as is shown, there is

considerable variation in the random effects and residual variation.

Model 3 adds sex to the level 2 random coefficients equation. As can be seen, males do

not have a significantly higher intercept (age 12) score than females. Interestingly, however,

when the effect of male is allowed to impact the growth rate (by including a male by age

interaction term to the model), the results change. Specifically, similar to social maturation, now

we see (model 4) that both male and male by age are significant. These results indicate males

have a higher initial value than females and change at a slower rate. Model 5 includes nonwhite

in the level 2 equation. Here, again, the coefficient for nonwhite is not statistically significant.

These results tell us that nonwhites do not have a different intercept or average score than whites.

[Insert Table 7.7 about here]

Turning to Table 7.8, the results of the growth curves for psychosocial maturation are

presented. Here, the results are a bit different than the previous two domains. For example, in the

unconditional random intercept model (model 1), we see that age and Age2 are significantly

different from 0, but Age is positive and Age2 is negative. This suggests that growth in

psychosocial maturation is rapid early in the life-course, and then slows down thereafter. Note

also, the descriptive results indicated that psychosocial maturation actually declines from T4 to

T5, which is capture in this model. A second model (not shown) was run adding random

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coefficients to the Age and Age2 factors. However, a likelihood ratio test indicated that the

random coefficients model is not warranted over the random intercept model.

[Insert Table 7.8 about here]

Model 2 includes the variable male in the level two equation. As was the case for the

previous domains, in models with only the binary male variable, the results show that males on

average, do not have different initial values than females. A male by age interaction (not shown)

was also included but was not significant, indicating that males do not have a statistically

different rate of change in psychosocial maturation over time than females.

Model 3 includes nonwhite in the level two equation. Interestingly, here we see that

nonwhites have a higher initial score than whites, on average. Thus it appears that there are

differences by race in terms of psychosocial maturation. It is somewhat difficult to interpret the

positive coefficient, however, because of a) the small proportion of nonwhites in the sample and

b) the mixture of race/ethnicities that make up the nonwhite category. A nonwhite by age

interaction was calculated and included in model 4 but as is shown, this term was not significant.

Table 7.9 displays the results of the identity/cognitive transformation growth curves.

Confirming the descriptive results, in model 1 (random intercept) Age is a significant predictor

of identity/cognitive maturation, indicating that this domain grows over time. However, Age2 is

also significant but negative. This is likely due to the plateauing effect that can be seen in this

domain after about age 18. A random coefficients model was calculated but is not shown because

a likelihood ratio test showed that it was not warranted.

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[Insert Table 7.9 about here]

Model 2 of Table 7.9 displays the results of the conditional growth curves for

identity/cognitive transformation maturation, including male as a covariate. These results show a

sharp divergence from the previous conditional models, with the coefficient for male being

highly significant (p<.001) and negative. In other words, males have a lower initial status on this

domain than females—something that is expected given the descriptive results shown previously.

In addition results shown in model 3 indicated that a male by age interaction was significant and

negative, suggesting that males’ rate of change on identity/cognitive maturation is less than

females (the inclusion of this variable renders the male dummy non-significant, however

p=.056). Thus it appears that males have different growth curves than females for

identity/cognitive transformation.

Model 4 shows the growth curve results with nonwhite included in the place of males.

Here, the coefficient is not statistically significant, indicating that nonwhites and whites do not

have different initial statuses on identity/cognitive transformation. Results not shown also

indicated that a nonwhite by age interaction was not statistically significant.

Table 7.10 displays the results of the growth curve models for neurocognitive maturation.

Because the descriptive results indicated that this domain increases nearly linearly with time, the

model only includes Age rather than Age and Age2. Thus, model 1 simply includes the linear

effect of age. Recall that the neurocognitive maturation domains were not measured until T3,

which may account for the linearity in its growth. It is unclear whether the effect of age would be

curvilinear were earlier time periods have been available. While a random coefficient model was

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calculated, a likelihood ratio test indicated that the random coefficient for age was not warranted,

and thus the neurocognitive maturation models only include random intercepts.

[Insert Table 7.10 about here]

Model 2 includes the covariate male into the level 2 equation. As is shown, the

coefficient for male is not statistically significant, indicating that males do not have a higher

initial (age 18) score on neurocognitive maturation than females. A model (not shown) with a

male*age interaction was also calculated; however, this interaction was not statistically

significant. This suggests that males do not have a different rate of change in neurocognitive

maturation than females.

The next model (model 3) shows the growth trajectory with nonwhite added as a

covariate. As expected given the descriptive results, we see that nonwhites have a lower initial

starting point or intercept than whites. A nonwhite*age interaction was included in model 4, but

was not statistically significant. This suggests that nonwhites and whites have indistinguishable

growth rates for neurocognitive maturation.

The last set of results illustrates growth trajectories for average maturation levels. Recall

this variable was calculated by averaging each of the five maturation domains at each time

period thus producing an average score that is time varying. Model 1 of table 7.11 shows the

unconditional growth model with random intercepts. This model is similar to model 2, which

includes random coefficients for Age and Age2 (which were warranted, according to the

likelihood ratio test). Both Age and Age2 are significant and positive, indicating that average

maturation increases over time, and this increase is nonlinear.

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Model 3 includes male into the level 2 equation. Neither the male nor the male by age

interaction were significant (not shown), suggesting males do not have a different initial status or

rate of change in average maturation than females. Model 4 includes nonwhite into the level 2

equation. As shown, this coefficient is not statistically significant. A nonwhite by age interaction

was included in model 5. As is shown in this model, nonwhites do not have a different initial

value on maturation than whites, but do appear to have a lower rate of change in overall

maturation.

[Insert Table 7.11 about here]

Discussion and Summary

The results presented in this chapter were intended to provide information on the domains

of maturation over time. The first part of the chapter explored maturation in each of the five

domains for the entire sample, and then parsed out by group (sex and race/ethnicity). That the

maturation domains generally increase over time lends support to the validity of the measures.

The results indicated that the general pattern of maturation over time was similar for all groups;

however, there appeared to be differences in levels of certain domains of maturation. This was

especially clear for identity/cognitive transformation maturation by sex and neurocognitive

maturation by race.

The next set of results illustrated unconditional and conditional growth models using

maturation as the dependent variable. These models showed the trajectory of maturation over

time as well as the effect of sex and race on both the intercept and rate of change. For the most

part, both Age and Age2 were significant in the models, indicating that the growth in all domains

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of maturation is nonlinear. The exception was with respect to neurocognitive maturation, which

only includes three time points, thus limiting the ability of a quadratic term to detect nonlinear

growth.

Interestingly as well, when including male and nonwhite as covariates into the models,

overall the results were somewhat mixed. When including only the binary male or nonwhite

variables, the results indicated that these characteristics do not impact initial values of

maturation. However, when including age interactions (thus allowing the characteristics to

impact growth), the results differed. Specifically, for social, civic, and identity maturation, males

had different growth rates than females. For the most part, nonwhite by age interactions were not

statistically significant. The only maturation domain for which there was a difference between

whites and nonwhites was with respect to neurocognitive maturation. Here, the results indicated

that whites have a higher intercept than nonwhites, but not a different rate of change. For overall

(average) maturation, nonwhites appear to have a slower rate of change than whites.

In sum, while overall maturation appears to operate similarly for males and females, there

is some evidence that differences in particular domains exist. This provides justification for

including sex as a covariate in the main analyses (below). In addition, supplementary analyses

will calculate models separately by sex. These results will be considered preliminary and are

mostly presented for illustration. There is less evidence that race makes a difference with respect

to change in maturation over time; however the analyses to follow will continue to be sensitive to

racial/ethnic effects.

The next chapter will present the main analyses—addressing research question 2. That is,

the effect of maturation (overall and by domain) on delinquency and crime will be tested. Both

descriptive and growth model analyses will be used to examine this question. This chapter will

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also address research question 3, which asks whether maturation gaps or contingencies may

impact delinquency/crime over time.

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CHAPTER VIII. RESULTS: THE RELATIONSHIP BETWEEN MATURATION & DELINQUENCY/CRIME

It is widely acknowledged that bosozoku is essentially a youthful phenomenon and that few Japanese youths

are bosozoku after twenty. This public recognition of the “graduation” from gang activity with the attainment of adulthood has led to a folk theory known as bosozoku hashika setsu (measles theory of bosozoku). This theory views bosozoku activity essentially as youthful indiscretion or as a manifestation of the “storm and stress” characteristic

of adolescence. It is assumed that youths’ participation in gang activity is a sort of youthful fever which can be “cured” by self-healing, as in the case of measles, if one matures enough (Sato 1991, 158).

Introduction

To this point, this dissertation has described and developed theoretical domains of

maturation, applicable to the late 20th and early 21st century. It has also examined trajectories of

delinquency/crime for a group of youth born in the 1960s, who came of age in the latter part of

the 20th century. The theory of maturation proposed here has found some support in that the

levels of the measured maturation domains appear to be empirically measurable and operate

largely as expected. However, the major premise of the dissertation is that maturation can

explain offending over time. Thus, this chapter will examine, in detail, the relationship between

maturation and crime.

It is important to provide a baseline for more complex models. Thus, the bivariate

relationship between maturation domains and delinquency/crime will be explored first in this

chapter. This will consist of cross-tabulations and Pearson’s correlations as well as standard

deviation analyses (analyzing the effect on delinquency of a standard deviation increase in

maturation). The analysis will then move on to growth models, in the format that has been

followed in the previous chapters. These models will be somewhat exploratory, including the

effects of maturation in both random intercept and random coefficients models. In addition, for

the most part, the effect of maturation domains will be examined in separate models. Finally,

average maturation (the average of the domains at each time period, as well as an overall

average—time constant—score) will be examined in terms of its relationship to crime.

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The second part of the chapter will explore the third research aim. This question asks

whether the effect of maturation domains may be contingent on other domains, or whether

maturation “gaps” relate to crime and delinquency. For this part of the analyses, single-level,

rather than multi-level models will be used. That is, the data will be in person-level rather than

person-period (Singer and Willett, 2003), in which there is one record per person and time is

represented in terms of variables. The focus of the gap and interaction analyses will be on adult

social role, psychosocial and identity/cognitive transformation maturation at T4 and T5. These

models will be somewhat exploratory, seeking to probe the effect of maturation beyond the main

models presented in this chapter.

Relationships Between Maturation and Delinquency/Crime

Bivariate Analyses

I begin with descriptive, bivariate analyses to explore how each maturation domain is

related to delinquency/crime. These analyses are based upon the longitudinal, person-period data

(Obs=2114, N=447). In terms of correlations, I use Pearson product-moment correlations for the

relationship between maturation and the variety score and point-biserial correlations to assess the

relationship between maturation and the dichotomous score. Table 8.1 shows the results of this

analysis. As can be seen, all but one of the 12 correlations is significant at p<.05. The exception

is the civic maturation and dichotomous delinquency score correlation which is marginally

significant (p=.05). The magnitude of the correlations ranges from a high of -.33 for the

identity/cognitive transformation maturation and variety score relationship to a low of -.05 for

the civic maturation dichotomous score relationship. The total or average maturation score is

statistically significant as well.

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The next set of analyses (Table 8.2) display the average delinquency score for

observations one standard deviation above the mean on each maturation domain, compared to all

others. As can be seen, for the most part, the observations one standard deviation above the mean

have lower delinquency scores (on both the variety and dichotomous variable). However,

interestingly, the neurocognitive domain differs. The observations one standard deviation above

the mean have higher delinquency scores. This is curious because the overall correlation between

delinquency/crime and neurocognitive maturation is significant and negative. To further explore

this issue, scores were calculated representing one standard deviation below the mean of

neurocognitive maturation. Here, there was little difference in the dichotomous score but a

difference in the expected direction for the variety score (data not shown).

[Insert Tables 8.1 and 8.2 about here]

Thus, there is evidence that maturation (on average and by domain) is related to crime in

a manner that is consistent with the theoretical framework advanced in this dissertation. Overall,

this suggests that the hypothesis that maturation is related to crime over time is partially

supported. It should be noted that the focus is on desistance from crime, and thus on this

relationship in emerging and young adulthood. Analyses (not shown) indicated that the bivariate

correlation between crime and social maturation, identity/cognitive transformation maturation, as

well as average maturation is stronger after time 2 as compared to the relationship between these

domains and crime over the entire study period. Interestingly, computing partial correlation

coefficients for all five maturation domains and delinquency (e.g., controlling for the effect of

the other four domains when examining the relationship between each domain and crime) shows

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that only social and identity/cognitive transformation remain significant for the dichotomous

measure. For the variety measure, social, identity/cognitive transformation, and neurocognitive

remain significant, which suggests they are associated with crime independent of each of the

other domains.

Growth models: The relationship between maturation and crime

This section presents the results of the main analyses for this dissertation, examining the

longitudinal relationship between maturation and crime. The models to be used are similar to

those shown in the previous two chapters, building specifically on the models in Chapter VI. The

primary independent variables will be the maturation domains, which will be assessed in terms

of their impact on the intercept, the rate of change, and as deviation scores (which provide

information on between and within individual effects of maturation).

The results presented will begin with random effects models and exclude other controls.

The control variables will be added to determine how these changes impact the effect of

maturation on delinquency/crime, again not modeling the covariance between the random

effects. It should be noted however, that three of the five control variables (friends’ deviance,

attachment to parents, and average grades) were only available at T1 to T3. Thus, there is

complete missing data at T4 and T5. Since missing data on any variable for any observation

results in that entire observation being dropped, including these as time-varying covariates would

limit the analyses to only three time points. To compensate for this problem, the time-varying

covariates were averaged for each individual. This changes the interpretation of the parameter in

the models which now become time-invariant and relate to between-individual differences rather

than both between and within-individual differences over time.

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The analysis will proceed by focusing independently on each domain of maturation (as in

the preceding chapter). Various iterations of maturation coding will be explored, including

allowing the effect of maturation to vary across time and deviation scores. This latter set of

models will include the within-individual mean of maturation as well as deviations from that

mean, which—as stated above—captures the between and within-individual effect of maturation.

There are numerous possible models that could be presented and for the sake of parsimony, only

select models will be shown—others may be discussed for comparison. These results are

available upon request.

We begin with growth models assessing the effect of social maturation on

crime/delinquency over time. The coding of age is set at the mean once again, so that the focus is

on desistance. Table 8.3 includes several models to illustrate the longitudinal effect of social

maturation on the variety measure of crime. Model 1 includes social maturation and age only,

with random effects for age (but without modeling the covariance among these random effects).

Model 2 includes the controls. Interestingly, in model 2 but not model 1, it is shown that social

maturation has a significant and negative impact on the level of crime over time.36 This can be

interpreted as the average difference in crime over time for an increase in maturation levels. The

coefficient for social maturation is quite small, but that is simply a function of the larger metric

for this variable (0-100). In terms of the controls, males, friends’ deviance, and parental

attachment have significant effects on crime, in the theoretically expected direction.

Model 3 includes a social maturation by age interaction. This term shows whether the

effect of social maturation varies over time, or whether social maturation affects the rate of

delinquency over time. As can be seen, this interaction is negative and marginally significant

which provides some evidence that the effect of social role maturation varies by age, or that 36 Model 1 was recalculated without random effects, which resulted in social maturation being significant.

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social role maturation negatively affects the rate of delinquency over time. Social maturation

remains negative and significant, indicating the effect of social maturation when the interaction

term is 0 (Hedeker, 2004). Once again, male, friend’s deviance, and attachment remain

significant, but interestingly, the linear age variable is not. This latter finding is likely due to the

correlation between age and the social role maturation by age interaction (r=.80). Model 4

changes the coding of social maturation. In this model, an overall social maturation mean score

is calculated and included along with a deviation score. As described in Chapter V, this provides

a between and within-individual analysis of the effect of social maturation on crime, which may

be more informative given that including a time-varying covariate in raw form implies that both

the within and between-individual effects are the same (Hedeker, 2004). The results show that

the average social maturation score is significant and negative. This suggests that those with

higher average social maturation scores have lower average crime scores at age 20.1. The

deviation score is negative but not significant at the .05 level. In this model, nonwhite is

significant and negative.

Interestingly, variety score models run separately by sex (not shown), with independent

covariance structures, indicate that social role maturation is negatively related to

crime/delinquency over time for females, but not for males. This may explain why, in the

baseline model without controlling for sex, social maturation was not significant. For the

between and within-individual analyses, the deviation score was significant for females but not

males. The mean social role maturation was significant for both females and males at p<.05. In

sum, it appears that the effect of social role maturation is nearly entirely found for females rather

than males. This finding will be discussed more thoroughly in the proceeding chapter.

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Table 8.4 includes identical models to Table 9.3, but with the dichotomous dependent

variable. As can be seen, overall the results are similar to the variety score results. Interestingly,

now in Model 1, social role maturation is significant at the p<.05 level, whereas it was not for the

variety score. Nonwhite appears to be negatively related to crime using the dichotomous

variable. In Model 3, social role maturation is significant but the interaction with age is not. Thus

it does not appear that using the dichotomous dependent variable, the effect of social role

maturation varies by age. Model 4 differs from the variety score results slightly, with mean

social role maturation reaching statistical significance. In addition, as was the case with the

variety score models, social role maturation expressed as a deviation from the mean for adult

social role maturation is significant and negative. This indicates that increases in social

maturation levels lead to decreases in crime when using the dichotomous dependent variable.

Thus, taking both sets of results (variety and dichotomous score) into account, there is evidence

that social role maturation, as defined here, matter with respect to crime over time and

desistance.

[Insert Tables 8.3 and 8.4 about here]

Table 8.5 shows the results using civic maturation as the main independent variable. As

can be seen across models, it does not appear that civic maturation, whether alone or independent

of the covariates, is significantly related to crime over time. This remains the case in both

random intercept (not shown) and random coefficient models. Civic maturation also does not

appear to affect the rate of change in crime over time (Model 3) and remains insignificant when

expressed as a mean and deviation score (Model 4). It is interesting to note that in certain

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models, civic maturation is positively related to crime over time (though not significant). Thus it

appears that civic maturation, while significantly related to crime on a bivariate level, is not

related to crime once age and the controls are taken into account.37 In terms of the covariates,

male, friends’ deviance, and attachment are consistently related to crime across the models. In

certain models, nonwhite is negatively related to crime. The results for civic maturation and the

dichotomous crime indicator were substantively similar and are not presented here.

[Insert Table 8.5 about here]

The next set of results displays the growth curve models predicting crime (variety) with

psychosocial maturation (Table 8.6). These results differ markedly from the civic maturation

models, in that in most specifications, psychosocial maturation appears to be a robust predictor

of crime or desistance. Model 1 shows the growth curve with random effects for the age

indicators as well as for the intercept. As can be seen, psychosocial maturation has a significant

and negative impact on crime (here the average level) over time. Model 2 confirms this result, by

adding in the controls. Psychosocial maturation remains negative and significant, suggesting the

impact of this maturation domain is independent of risk factors. Male, friends’ delinquency and

attachment are all related to mean crime/delinquency as well.

Model 3 includes an age by psychosocial maturation interaction. The raw maturation

term remains significant and negative, indicating the effect of psychosocial maturation on crime

at age 20 and when psychosocial maturation is 0. However, the age by maturation term is

negative but not significant, which suggests that psychosocial maturation does not affect the rate

37 Interestingly, the mean and deviation analysis, split by sex, indicated that the mean score for civic

maturation was significant for males, but not females (not shown).

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of delinquency or crime over time. Turning to Model 4, the results of the maturation deviation

analysis are shown. Interestingly, both the mean psychosocial maturation and the deviation score

are significant. This suggests that not only does higher psychosocial maturation correspond to

less criminal behavior on average, but within a person’s life, increases in levels of this domain

lead to less offending. In this model, nonwhite is also significant, indicating that nonwhites have

a lower average level of crime/delinquency over time, controlling for the other variables in the

equation.

The results in Table 8.7 show the effect of psychosocial maturation on crime/delinquency

using the dichotomous dependent variable. In large measure, these results are substantively

similar to the variety score results, including the controls. In these models, psychosocial

maturation affects the average level of crime/delinquency (but not the rate of change), and has

both between and within-individual effects. Thus, the results of the psychosocial maturation

analysis suggest that this domain has a substantial effect on crime over the study period of the

domains presented to this point.

[Insert Tables 8.6 and 8.7 about here]

Table 8.8 shows the results of the growth curve analysis with identity/cognitive

transformation as the main independent variable. These results are similar to the psychosocial

analysis, illustrating that identity/cognitive transformation has a strong impact on

crime/delinquency over the course of the study. For example, in Model 1 of Table 8.8, which

shows the effect of identity/cognitive transformation without controls, we see that this domain is

negatively related to crime at the mean age of the sample. In other words, it, like adult social role

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and psychosocial maturation, is related to desistance. Model 2 includes the control variables

(again all time-invariant) and the results are substantively the same. In terms of the controls,

male, friends’ delinquency, and attachment are all related to the mean level of

crime/delinquency.

Model 3 includes an identity/cognitive transformation by age interaction. The results

show that the identity/cognitive transformation maturation main effect remains significant and

also the interaction with age is significant and negative. This implies that identity/cognitive

transformation maturation has a negative effect of the rate of change in crime/delinquency over

time, which is theoretically expected. Interestingly, in Model 3, the raw age term is positive. This

suggests when controlling for the other covariates, crime increases. Centering the covariates

could improve the interpretability of the growth parameters, but that is not the focus here. Model

4 includes the deviation analysis, with the mean of identity/cognitive transformation maturation

(over time, by individual) and a deviation score from that mean. As can be seen, the results

indicate that identity/cognitive transformation maturation has significant between (mean) and

within (deviation) effects. The most interesting parameter is the deviation score, which shows

that for the individuals in the HHDP, as identity/cognitive transformation maturation levels

increase, criminal behavior decreases.

Table 8.9 shows the results of the identity/cognitive transformation maturation analyses

using the dichotomous crime/delinquency indicator. These results indicate that once again,

identity/cognitive transformation is significantly and negatively related to crime and the same

substantive story found with the variety measure holds. Certain of the covariates’ effect is

somewhat different (for example, attachment is not significant in the dichotomous delinquency

models, but nonwhite is significant in certain models). In sum, it appears that this domain of

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maturation is a robust predictor of crime and desistance and should be an important component

of life-course explanations.

[Insert Tables 8.8 and 8.9 about here]

The next set of growth model results, shown in Table 9.10 centers on neurocognitive

maturation. Recall that this domain was not as strongly correlated to crime/delinquency as the

other domains. In addition, the standard deviation analysis indicated that observations 1 standard

deviation above the mean were actually associated with higher levels of crime. Table 8.10

displays the models for neurocognitive maturation. Model 1 includes the domain in time-varying

form without covariates. Unlike the previous two domains, neurocognitive maturation does not

appear to be related to crime. This story remains the same adding the covariates (Model 2) and in

various iterations of neurocognitive maturation (Models 3 and 4). In no specification does

neurocognitive maturation predict crime/delinquency over time. The relationship between

neurocognitive maturation and the dichotomous delinquency variable (not shown) is largely

similar to the variety score.38

[Insert Table 8.10 about here]

In sum, of the five domains of maturation, it appears that adult social role, psychosocial,

and identity/cognitive maturation are related to crime over time, independent of the covariates.

Civic and neurocognitive maturation are related to crime at the bivariate level (and the

38 The models were calculated without the age terms, which led to neurocognitive maturation being highly

significant in most models. Thus the effect of this domain appears difficult to distinguish from the effects of age.

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correlation between neurocognitive maturation and crime/delinquency is significant, independent

of the covariates) but not in the growth models. The final set of growth models considers the

effect of maturation as a whole (that is, the average level of maturation across the domains, at

each time period) on crime/delinquency.

Table 8.11 shows the results of the growth curve analysis using average maturation as the

main independent variable. This variable was created by taking the mean of each domain at each

time period. There is missing data on this measure because at least 4 domains were required to

have valid responses for each individual to receive a score. As can be seen across models, this

variable is strongly related to crime/delinquency over time and is thus—as hypothesized in this

dissertation—a potentially important factor in desistance. Model 1 and 2 show the effect of

maturation in time-varying format (Model 2 includes the covariates). In each model, maturation

has a significant and negative effect on crime. Model 3 includes an age by maturation

interaction, which is not significant (however both terms are significant and negative in a random

intercepts model—not shown).39 This is inconsistent with expectations, which were that the time

or age would influence the effect of maturation on crime over time. However, this can also be

interpreted as showing that the effect of maturation on crime/delinquency is invariant across

time. Model 4 displays the results of the between (mean level) and within-individual (deviation

scores) analysis of maturation and crime. The results indicate that the between individual and

within-individual coding of maturation are both related to crime/delinquency in the expected

direction. In other words, not only are those with higher average maturation values less likely to

commit crimes over time, but for the same person, increases in maturation levels correspond to

decreases in crime, which is informative for understanding the desistance process. The

39 Interestingly, a model (not shown) excluding the age2 variable shows that the maturation by age

interaction is significant and negative. Thus there is some evidence that maturation does affect the rate of change in delinquency/crime.

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dichotomous crime/delinquency models are shown in Table 8.12. As can be seen, the substantive

story is the same: for the most part maturation has a strong and negative effect on crime over

time. Thus the major contention of this dissertation appears to be largely supported.40 This

relationship is illustrated graphically in Figure 8.1, which shows the results of the predicted

values of the variety score in Model 2 of table 8.11. This clearly demonstrates the age-crime

curve, with the number of acts declining substantially after age 20. Figure 8.2 shows predicted

values for the entire sample using xtmixed, which allows one to plot the fitted values. Figure 8.2

represents Model 4 (the within and between individual model).

[Insert Tables 8.11 and 8.12 about here]

[Insert Figure 8.1 about here]

[Insert Figure 8.2 about here]

Maturation Gaps and Interactions

The last set of analyses to be presented in this dissertation explores the effect of gaps

between maturation domains as well as whether the effect of particular domains on

crime/delinquency is contingent on other domains. To address this research aim, T4 and T5 data

are used, focusing specifically on the effect of adult social role maturation in relation to

psychosocial and identity/cognitive transformation maturation. Chapter V presented the

methodology and equations that are used to test the maturation gap and interaction hypotheses.

With respect to the maturation gap analysis, four sets of scores were created, a social role,

40 It should be noted that particular variations on the models reported in this chapter were tested and many

would not converge. For example, for each domain, including a random effect for maturation resulted in nonconvergence. Specifying unstructured covariances in the models for which the maturation domains were statistically significant produced substantively the same results with few exceptions (for example, the p-value for the average social maturation--Model 4 in table 8.3--increased to .056).

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psychosocial maturation gap at T4 and T5, and a social role, identity/cognitive transformation

gap at T4 and T5. These scores range from -1 (meaning psychosocial or identity/cognitive

transformation levels are higher than social role levels) to 1 (meaning social role levels are

higher than psychosocial or identity/cognitive transformation). It is anticipated that these gap

scores will have a positive relationship to crime/delinquency, where positive scores represent a

“gap” between social role and other domains of maturation (thus “psuedomaturity”).41

In terms of the interactions, rather than multiplicative interaction terms, I cut the sample

at the mean of the reference domains (psychosocial and identity/cognitive transformation) and

examine whether the effect of social role maturation differs by level of that domain. The cut

point was chosen so that the groups compared were at or below the mean vs. above the mean.

For the most part, these differences are considered in terms of whether social role maturation

reaches statistical significance in both groups. The groups were created as follows: the mean of

pschosocial and identity/cognitive transformation maturation was calculated at T4 and T5. Then

a variable was created, scored 1 if the individual’s score was above the mean and 0 otherwise.

Finally, the analysis was conducted in both groups to determine whether an interaction is present.

It should be noted that to conduct this analysis, the variety dependent variable is the focus and as

such, the binomial regression models are used again. It is possible, in single level binomial

regression, to control for dispersion in the model, and this is done here (the variety score has a

larger SE than mean at each time period).

[Insert Table 8.13 about here]

41 For these analyses, I divide the domain scores by 100 to achieve a score that ranges from 0-1, accounting

for the larger coefficients.

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Bivariate relationships between the maturation gaps and crime were calculated, but not

shown. For the most part, the maturation gaps do not predict crime. However, the T4 social

role/identity gap is significant and positive for T4 and T5 crime/delinquency. Table 8.13 shows

the results of the overdispersed binomial regressions of T4 and T5 gaps on crime. Models 1 and

2 show the contemporaneous effects of adult social role gaps on crime at T4. Interestingly, the

results show that the social role/psychosocial gap is significant but negative. This is contrary to

expectations and the bivariate relationship. In Model 2 of Table 8.13, we see that the social

role/identity gap is not significantly related to crime. Models 3 and 4 show the impact of T5

social role gaps on crime at T5, and indicate no significant effect. In sum, it appears that

maturation gaps, as currently measured, do not have much influence on crime. The one

significant multivariate finding was actually in the opposite direction as expected, implying that

as social role maturation increases relative to psychosocial maturation, crime decreases.

However, this finding did not hold for the dichotomous crime/delinquency model (not shown),

indicating that it should be interpreted with caution. Perhaps these results are to be expected,

given that pseudomaturity is theoretically more likely to occur during adolescence, whereas the

measurement of crime here took place when the individuals were 25 and 31 years old.

The next set of results (Table 8.14) shows the interaction analysis at T4. Models 1-4

show the effect of social role maturation at T4 with psychosocial and identity/cognitive

transformation split at their mean at the same time period. Model 1 (‘low psychosocial

maturation’) includes individuals with psychosocial maturation scores at T4 at the mean or

lower. Model 2 includes all other individuals (‘high psychosocial maturation’). As can be seen,

social role maturation is significant in both groups, indicating that social role maturation has a

negative effect on crime regardless of the individual’s level of psychosocial maturation. Models

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3 and 4, however, show a different story. Here we see that social role maturation is significant

only in the low identity/cognitive transformation group. Thus, this suggests that, contrary to the

arguments of some researchers (e.g., Giordano et al., 2002; LeBel et al., 2008; Shover, 1985),

social roles such as marriage and employment do not only matter for those who have the

requisite identity/cognitive transformation maturation. To the contrary, it appears that social role

maturation does not matter for those who have a relatively high identity/cognitive transformation

maturation level—whether or not social role maturation is high makes little difference for this

group.

[Insert Table 8.14 about here]

The results shown in Table 8.15 confirm this pattern at T5, with social role maturation

only remaining significant in the low psychosocial and identity/cognitive transformation groups.

Once again this seems to support a “compensatory” hypothesis, whereby people rely on social

connections and support when they need it (e.g., when psychosocial or identity maturation is

low). This is an interesting and somewhat unexpected finding but makes sense from a theoretical

view point. The covariates are not consistently significant in the models. Attachment is

significant and positive in Model 4, which is not theoretically expected. However, when this

model was calculated using the dichotomous crime measure (not shown), this result was not

replicated. The implications of the results discussed in this chapter will be expanded in the final

chapter.

[Insert Table 8.15 about here]

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Discussion and Summary

This chapter presented the main analyses of the dissertation, addressing research aims 2

and 3. Specifically, the relationship between maturation and crime over time was explored. First,

bivariate analyses showed that the five domains of maturation identified and defined earlier are

significantly related to crime. All of the analyses (with the exception of the standard deviation

analysis for neurocognitive maturation) were in the expected direction.

Next, growth models were estimated, focusing on each specific domain separately and

then an average maturation measure. The results showed that adult social role, psychosocial,

identity/cognitive transformation, and average maturation significantly predict crime over time.

This was confirmed in models in which covariates were added, and in models in which

maturation was decomposed into between and within-individual components. For the most part,

maturation did not affect the rate of change of delinquency, contrary to expectations.

The third research aim was also addressed in this chapter. Maturation gaps, focusing on

adult social role maturation relative to psychosocial and identity/cognitive transformation

maturation were assessed. For the most part, these measures did not predict crime/delinquency.

Examining whether maturation domains have a conditional effect on one another showed that, in

large part, adult social role maturation was significant only in low psychosocial or

identity/cognitive transformation groups. This suggests a compensatory effect, where social ties

may not add much beyond the protective effect provided by high psychosocial or identity

maturation, but is needed when these domains are low. This finding occurred at both T4 and T5.

Lagged effects (whereby T4 interactions predict T5 crime) were not explored because of the long

time gap between T4 and T5.

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The next chapter summarizes the dissertation and explores the implications of the

findings, in terms of theory, research, and policy. In addition, directions for future research will

be discussed.

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CHAPTER IX. DISCUSSION & SUMMATION What the hell does it all mean, anyhow?

(Woody Allen)

Introduction: Summary of the Dissertation

The primary interest motivating this dissertation was to expand criminological knowledge

about crime over the life-course, specifically desistance from crime. In recent years, as

chronicled in the first three chapters, much high quality work—theoretical and empirical alike—

has been conducted to advance our understanding of why nearly everyone involved in crime or

delinquency ages out. This work has identified several interpersonal and external factors (social

bonds, orientational changes, cognitive improvements, etc.) that seemingly help explain

desistance (Cusson and Pinnsonealt, 1986; Giordano et al., 2002; Sampson and Laub, 1993;

2005; Shover, 1985; 1986). The theoretical models have often been offered as ostensibly

competing. However, as viewed in this dissertation, many of the models appear to offer a part of

the process of what it means to become a self-sufficient adult in today’s society.

Interestingly, one of the earliest theoretical perspectives developed to explain

desistance—one that has heretofore been discussed only as a relic of the past—seems to provide

a solid framework for understanding how these theories of desistance interrelate. The Gluecks’

theory of maturational reform (Glueck and Glueck, 1937; 1943; Laub and Sampson, 2001) was

not well-specified in their writings but offered a way in which to conceptualize why offenders

(serious and non-serious alike) all apparently desist (Laub and Sampson, 2003). Importantly, the

Gluecks recognized that maturation was multi-faceted and complex, and further work was

needed in order to clarify what it meant. However, in large measure, this call appears not to have

been heeded.

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Thus, this dissertation took as its starting point, the challenge of identifying—from a

criminological perspective—what the “components of maturation” are and how they may relate

to crime and desistance. From the literature on life-course and the transition to adulthood, five

specific domains of maturation were described: adult social role, civic or communal,

psychosocial, identity/cognitive transformation, and neurocognitive. This specification is multi-

disciplinary, relying on sociological, psychological, and neurological research.

Because the definition of maturation in terms of the five domains is complex and multi-

disciplinary, locating a dataset that may have all the necessary elements remains a challenge.

However, the dataset employed for the current dissertation, the Rutgers Health and Human

Development Project (HHDP), proved to be quite inclusive. The first research aim was to

develop empirically sound measures of maturation. Chapter V of the dissertation describes that

process, which involved identifying items from the HHDP and conducting several psychometric

tests of subcomponents comprising each domain. This was difficult because theoretically, each

domain does not necessarily represent one latent construct. In the end, Percent of Maximum

Possible (POMP) scoring was chosen to calculate a score with a maximum of 100 for each

domain at each time period. For the most part, each domain increased over time, from T1 to T5.

In addition, an “average maturation” measure was produced by averaging each domain at each

time period, which increased monotonically from T1. Thus, though not without clear limitations,

it proved possible to construct each domain in a manner that provided face as well as empirical

validity.

The next set of analyses examined growth trajectories of crime/delinquency. The main

dependent variable for the dissertation was a variety delinquency scale, comprised of nine

distinct items. In addition, for the purposes of validation, a dichotomous score was used. For the

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most part, the growth trajectories with both crime scores were similar. The curves showed the

typical age-crime relationship, with crime or delinquency peaking in late adolescence and

declining thereafter. In addition, because maturation may differ by race or sex, these variables

were used as covariates in the growth models. In general, there were few significant effects, and

where there were sex differences, these were with respect to the effect of sex on the rate of

change in maturation. For example, only in the psychosocial maturation models was male

(without the male by age interaction term) significant. However, for social, civic, and

identity/cognitive transformation maturation, sex had an effect on initial status and change when

the male by age interaction was included. There were not consistent sex effects across models for

these domains, and while these results of sex differences were not unexpected, it was not clear

whether this meant that sex conditioned the effect of maturation in crime. Race did not appear to

have an influence on changes in maturation over time.

The descriptive analyses of delinquency/crime by subgroup showed that males engage in

more antisocial acts than females, and that nonwhites engage in less acts than whites. As was

mentioned, the sex difference is theoretically expected. However, the meaning of the race

difference is less clear. The nonwhite category is composed of Asians, blacks, and other racial

groups. There are established expectations in regard to crime rates of blacks vs. whites, but

generally Asians have lower rates than whites (Gabbidon, 2010). Thus, the nonwhite category is

composed of two different groups, which may make comparisons to whites less than ideal.

Nonetheless, of the 31 nonwhites in the sample used, 87% were black. This would suggest that

the nonwhite category should have had a higher rate of crime than whites. Thus it is interesting

that whites had higher rates over time. This finding may have been a function of the much larger

sample size for the white group, which represents 92% of the sample.

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The main analyses were presented in the preceding chapter. Each maturation domain, as

well as average maturation levels, was related to crime on the bivariate level. The growth

models, however, told a slightly different story. Specifically, adult social role, psychosocial, and

identity/cognitive transformation maturation were related to crime over time. To varying

degrees, these domains significantly predicted initial levels, rate of change in offending, and

within-individual effects. The primary results were focused on the variety score, but results using

the dichotomous crime/delinquency score were also presented. For the most part, these were

similar to the variety score. An interesting change was that the within-individual (deviation from

the mean) score for social maturation was not significant (p<.05) for the variety score but was for

the dichotomous score. Thus, this domain may be more sensitive to changes in whether or not

one commits any crimes (e.g., prevalence), rather than changes in the number or extent of crimes

committed. In terms of the covariates, male, friends’ delinquency, and parental attachment were

the variables most consistently related to crime over time.

Because of the differences by sex in certain of the maturation domains discussed in

Chapter XII, models were run separately for males and females. These models were not shown in

the text and were examined for differences in statistical significance of the domains. For the most

part, sex did not appear to condition the effect of maturation on crime. However, sex did

condition the effect of social maturation, which was strongly related to crime for females but not

males. Thus there is some evidence that the effect of social role maturation, as defined here, is

not general. This effect, however, could have occurred because women were more likely to be

married or cohabitating.

It should be noted that because of a reduction in sample size that results when splitting

the sample, these models should be interpreted with caution. The coefficients of maturation by

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sex were also not formally tested for differences because it was not a major focus of the present

dissertation. Future research should examine this issue more closely as well as seek to explain

why there are or are not differences in the effect of maturation by sex. The results of the growth

models are discussed more fully—with particular attention to theory and policy implications—in

the next section.

Finally, the third research aim was addressed in the last chapter. Maturation gaps—in an

admittedly limited manner—were assessed in terms of their relation to crime. These models were

not multi-level and were only presented for the variety score at T4 and T5. The gap scores, using

social role maturation as the reference domain, were generally not related to crime. Testing

whether particular maturation domains had a contingent relationship told a different story. Here,

in three of the four models tested, social role maturation only mattered for those with low

psychosocial or identity maturation. This appears to contradict certain theoretical work

(Giordano et al., 2002; Greenberger and Steinberg, 1986; Newcomb, 1996; Shover, 1985) that

argued social maturation (or ‘hooks for change’) are only likely to facilitate desistance for those

who have the requisite emotional (psychosocial or identity) maturation.

Implications: Theory and Policy

The findings generated in this dissertation have significant implications for

criminological research and for directions that policy may take in order to improve effectiveness.

In this section, the results are first discussed from a theoretical viewpoint, and then the relevance

of the results for policy is reviewed.

Identity/cognitive transformation maturation appeared to have the most consistent effect

of the five domains on crime over time. In all models, with and without controls, expressed in a

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time-varying format, in an interaction with age or time, and parsed by between and within-

individual effects, identity/cognitive transformation maturation was significantly and negatively

related to crime. In addition, in supplementary analyses, identity maturation was the only domain

significantly correlated with crime at each time point using bivariate analyses. This supports

emerging theories that suggest that desistance is associated with a change in how one views

oneself and also how one views crime (Giordano et al., 2002; Maruna, 2001; Paternoster and

Bushway, 2009; Vaughn, 2007). Of these theories, the measurement of identity/cognitive

transformation focused on Giordano and colleagues as well as Paternoster and Bushway’s ideas.

In other words, not only were individuals views of themselves measured for this domain, but also

their attitudes toward crime and honesty. To Giordano et al. (2002), cognitive transformations

include all of these factors.

Critics might question how variable identities are through the life-course. The

criminological work in this area has either been purely theoretical or has not tracked changes in

identity over time in a quantitative manner. Thus, to this point, it is unclear whether changes in

identity actually correspond to changes in behavior. In addition, retrospective accounts of

changes in identity may be somewhat exaggerated by offenders seeking to explain their reform

(see Maruna, 2001). The analyses presented in the last chapter offer a significant advance to the

literature, illustrating that identities quantitatively and prospectively measured do change and do

covary with desistance. This is important information for criminological theory, confirming the

results of the qualitative analyses that have focused on identity as a turning point in the life

course (Baskin and Sommers, 1998; Giordano et al., 2002; Graham and Bowling, 1995; Maruna,

2001).

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The results of the psychosocial maturation analyses are also interesting from a theoretical

viewpoint. The longitudinal association between psychosocial maturation supports work in

developmental psychology (Cauffman and Steinberg, 2000; Monahan et al., 2008). While the

measurement of this domain differs somewhat from previous work, the major components were

similar (e.g., responsibility, temperance, and perspective). This domain of maturation includes

elements of self-control, which appears to increase over time. Gottfredson and Hirschi (1990)

allowed for change in self-control over time, but did not feel it was a major part of the desistance

process. Recent work, however, has shown that self-control does change over time significantly,

and this change is related to crime (Forrest and Hay, 2011; Na and Paternoster, 2012). The

results of the psychosocial maturation analyses seem to confirm this.

In addition, part of psychosocial maturation consists of personality elements (e.g.,

perspective), which also appear to change over time. This largely confirms the arguments of

Blonigen (2010) who argued that desistance may result from changes in personality over time. In

addition, psychosocial maturation includes rational choice components. It thus seems to be the

case that indeed, individuals’ rationality increases over time and this is related to a decrease in

antisocial behavior (Shover, 1985; 1996; Paternoster et al., 2010). In sum, the evidence implies

that the way people view the world, view other people, and make decisions changes over time in

such a way that helps explain crime over the life-course. Psychosocial maturity has, for the most

part, been absent from criminological literature. The results of the current dissertation suggest

that this is an oversight. It should, however, be noted that the measurement of psychosocial

maturation is an issue that deserves further attention; as measured here, psychosocial maturation

levels decreased slightly from T4 to T5. It is difficult to know whether this is an artifact of

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coding or whether in the HHDP sample, psychosocial maturation levels do in fact decline at age

30/31.

The results of the social role maturation analyses are less clear than for identity and

psychosocial maturation. While the time varying variable was significant (indicating that social

maturation influences crime/delinquency over time), it did not appear to affect the rate of change.

In addition the within-individual effect for the variety score was not significant—but it was for

the dichotomous crime score. Nonetheless, this domain did appear to be related to crime over

time in the expected direction. This suggests that adult roles are incompatible with crime, as

argued by several theoretical perspectives, including social control (Sampson and Laub, 1993),

routine activities theory (Haynie and Osgood, 2005; Horney et al., 1995), and role-taking

(Yamaguchi and Kandel, 1985).

Measuring social role maturation as was done in this dissertation has several benefits but

also drawbacks. First, as argued by Giordano and colleagues (2002), adult romantic

relationships, employment, educational and parental status are not likely events that occur in a

vacuum. Rather, they represent components of a “respectability package,” the effects of which

are difficult to disentangle. In addition, romantic relationships were included, whether they were

formally recognized as a marriage or not. This is somewhat controversial in criminology, as

some have found that cohabitation has a differential effect than marriage (Sienneck and Osgood,

2008). Supplementary analyses revealed that the bivariate correlation of relationships with crime

at T5 was only slightly lower than the correlation of marriage with crime (both significant at the

.05 level, though marriage was significant at the .01 level). In addition, the correlation of

marriage and cohabitating relationships with crime at T4 was stronger than marriage alone

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(neither significant at the .05 level, however). Thus the decision to include cohabitation most

likely did not appreciably dampen the effect of social role maturation on crime.

However, the measurement of social roles and relationships as one “package” has the

drawback of not being able to determine which components may matter more or less. As

mentioned though, because these components (work, relationships, attachment) are not likely to

operate in isolation of one another, this measurement scheme is theoretically justified. In

addition, the measurement of social role maturation focused on adult roles. Thus, it is the case

that this domain, unlike the others, may not be expected to be negatively associated with crime

until individuals reach an age at which these roles are socially acceptable. In other words, while

the four other domains described and measured in this dissertation may provide an understanding

of both the increase in antisocial behavior in adolescence as well as desistance in early

adulthood, social role maturation is more relevant for desistance.

Both civic and neurocognitive maturation were unrelated to crime in the growth models.

These non-significant results merit some expanded discussion. In large measure, it appears that

age or time wipes away any civic or neurocognitive effect in the growth models. For example, on

the bivariate level, analyses indicated that these two domains were significantly related to crime.

In addition, growth models calculated without age terms or covariates showed that both domains

were significant. Neurocognitive maturation was significant in a model without age terms but

including covariates.

Additionally, it may be that the measurement of these domains in this dissertation does

not capture the essence of civic or communal activities or cognitive maturation. For example,

variables capturing voting or paying taxes were not available in the dataset. The inclusion of

these factors may be important. With respect to civic maturation, to the extent that voting or

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paying taxes—acting as a ‘responsible citizen, in other words—may be more germane to later

adulthood. Since the dataset used here was limited in that no data is available for individuals past

their early 30s, this domain may have been less well measured than others.

With respect to the neuropsychological exams, those used herein are, at best, a far proxy

for brain development. The tests used here were designed to measure cognitive impairment,

among other things (Bates and Tracy, 1990). Thus they are not exactly the ideal to measure brain

development. Further, the neuropsychological exams were not available prior to T3 in the dataset

used for this dissertation. This limited the amount of “growth” that could be detected in the

models, essentially creating a three wave set of models.

Nonetheless, both domain measures increased over time, as theoretically expected, and

both were inversely related to crime, at least at the bivariate level. Thus there is sufficient reason

to continue attempts to measure civic or communal maturation as well as neurocognitive

maturation in the future.

The main growth curve analyses also examined results for males and females separately

(results not shown). For the most part, there were few substantive differences. It may seem

counterintuitive that the effect of maturation does not appear to vary considerably by sex. After

all, much research has shown that males and females mature at different rates (De Bellis et al.,

2001; Lenroot et al., 2007; Newcomb, 1996). However, this research is generally focused on

biological processes (e.g., sexual maturation). It may be that the domains identified in this

dissertation affect males and females at a similar point in the life course. The growth curve

analyses presented in Chapter XII demonstrated that this assumption may not be warranted, as

sex influenced the rate of change in maturation levels for three of the five domains. Yet even if

the rate of change in maturation levels differs for some domains by sex, this does not mean that

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the impact of maturation on crime differs by sex. For the most part, the effect of maturation

(even if it occurs later for males than females) appears to be similar by sex.

That there are differences in adult social role maturation is interesting. Social role

maturation includes such things as romantic relationships, employment, attachment to partners,

and graduating high school. In the current dissertation, social role maturation levels were higher

for males until age 25, at which point females had higher scores than males. Prior research on the

effect of these factors has not indicated that there may be an interaction by sex. The classic work

by Sampson and Laub (1993; Laub and Sampson, 2003) argues that marriage and employment

lead to desistance, yet their focus was exclusively on males. The results of this dissertation

suggest that social role maturation matters more for females than males—at least within the

sample used here. Some research in the 1970s argued that relationships such as marriage were

beneficial for males, not females (Bernard, 1972). However, more recent work has indicated that

marriages improve outcomes for both males and females (Barkan, 2012). Perhaps changes in

society, where women are afforded more independence in both relationships and in the

workforce may speak to this result, suggesting that these relationships have more meaning for

females in the more recent times. Interestingly, looking specifically at T5 in the present data,

having a full-time job and being married or cohabitating were significantly and negatively

associated with crime only for females.

Interestingly, these results are not in contrast to more recent work that has examined

whether social factors predict desistance for males and females. Graham and Bowling (1995)

described the results of a study that examined offending and desistance in a sample of 14-25

individuals. They found that for females, adult roles (e.g., moved away from their parents, had

romantic partnerships and children) predicted desistance from offending; however, the same was

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not true for males. They speculated that “[m]ales may be less inclined to grasp, or be able to take

advantage of such opportunities, as females. One reason for this might be that the negative

payoff of embracing these opportunities may outweigh the positive outcomes for males, but not

females” (1995: 65). They also suggested that females simply had higher rates of adult role entry

in emerging adulthood than males. This was true in the HHDP sample for close romantic

relationships but not employment. It could be that these relationships or roles simply mean more

to females in the present than males.

Both the Graham and Bowling (1995) study and the present dissertation were similar in

that they did not include information on respondents into later adulthood. Their study stopped at

age 25 and the present one concluded at age 30/31. Therefore, the possibility exists that not only

do certain domains (e.g., civic maturation) have less relevance for the age span analyzed here,

but also that adult social roles matter more for males of a certain age. As Uggen (2000) found,

within a largely male sample (>90%), some adult relationships (e.g., work) are more beneficial

after males reach later adulthood. It should be noted that within Farrington’s Cambridge Study,

marriage at later ages actually dampened the effect of marriage on life-outcomes (see Theobald

and Farrington, 2011). Nonetheless, to the extent that adolescence has been, in effect, extended

in recent years (see Arnett, 2000), the (age-graded?) influence of marriage may be changing.

Thus, future research exploring the impact of adult social roles on crime/desistance over the life-

course with contemporary samples should seek to understand whether age matters.

In terms of the maturation gap and interaction analyses, the result suggested that

maturation gaps did not predict crime. The one gap that was significant was in the opposite

direction and did not hold up under sensitivity analyses. It should be noted that these analyses

were limited in that they were not longitudinal and were meant to probe the effect of maturation

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on crime. In addition, while the work of Moffitt (1993), Newcomb (1996), Galambos and Tilton-

Weaver, (2000), and Greenberger and Steinberg (1986) served as the motivation for these

analyses, Moffitt’s (1993) theory is more relevant for physical maturation as compared to social

independence. To date, studies that have examined maturation gaps in this way have found the

expected relationships (Barnes and Beaver, 2010; Barnes et al., 2011). The focus in the current

dissertation was on maturation gaps in which social maturation scores exceed psychosocial or

identity scores. To be sure, in the HHDP data, social maturation levels exceeded psychosocial or

identity levels very rarely. Thus the range of these gap variables was restricted. Nonetheless, in

certain bivariate analyses, maturation gaps were related to crime, which suggests these

relationships should be further examined.

With respect to the interactive effect of maturation on crime, an interesting pattern

emerged. Once again, the focus was on whether social maturation’s effect is contingent on other

domains. The motivation for this analysis derived from the work of Shover (1985), Giordano et

al. (2002), and LeBel et al., (2008) among others. These authors have argued that the effect of

social roles on crime may not be the same for everyone. Specifically, those who have low levels

of emotional (psychosocial) or identity maturity may not recognize the opportunities that these

roles provide. For example, LeBel et al. (2008: 139) identify one school of thought in desistance

research that suggests that “the impact of the social factor depends on the level of the subjective

characteristic. With the right subjective mindset, the person may be capable of taking advantage

of the good events in life that come along and/or will not be thrown off course by social

disappointments.” In other words, in terms of interactions, these arguments suggest that social

roles (or factors) matter only for those with the requisite psychosocial or identity maturation.

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Interestingly, the findings presented in the last chapter suggest a different mechanism by

which maturation domains may be contingent upon one another. Specifically, we saw that social

role maturation was significant only in observations in which psychosocial and identity/cognitive

transformation maturation were low (for three of the four models). This suggests that rather than

social roles only protecting against crime for those “mature” enough to take advantage of these

opportunities, it may be that these roles are superfluous for those with higher levels of other

forms of maturity. This explanation implies that the protective effect of social role maturation is

simply not needed by those who have high levels of psychosocial or identity maturation.

However, when these latter two domains are low, there is a compensatory effect, whereby social

roles may increase in significance for the individual. This sort of compensatory finding has been

discussed in the natural sciences (Bai et al., 2004) in terms of species flourishing in

environments in which other species are declining. In addition, some research has found that

types of social support may matter more in protecting mental health when other types of support

are low (Syrotuik and D’Arcy, 1984). It could be the case that the same dynamic occurs for

domains of maturation.

In terms of policy implications, the findings of the current dissertation are also intriguing.

The idea that nearly everyone desists is consistent with much life-course research (e.g., Laub and

Sampson, 2001; 2003), but not with U.S. correctional policy. Life sentences without parole

(LWP) are still applicable for juveniles in America convicted of homicide. As Gottfredson and

Hirschi (1986) argued, long-term incarceration stints for offenders who will be locked up beyond

the point at which they are likely to desist are ineffective and inefficient. The sample used in this

dissertation is a more normative group of individuals, containing few high-risk offenders. By the

time they reach the age of 30, nearly all of them report committing no serious or minor crimes.

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Thus, it would appear important for U.S. sentencing policy to take age into account. To some

degree, it seems as if individuals do “age out” of crime.

But as demonstrated here, not everyone ages out at the same time or rate. Several internal

and external factors affect the rate of desistance and crime over time. First, work and

familial/romantic relationships appear to be important in the desistance process. As Sampson and

Laub (1993; 2005a; Laub and Sampson, 2003) have long argued, this suggests that correctional

policy should focus on maintaining social bonds and readying offenders for the world of work

upon release (Sienneck and Osgood, 2008). Certain work has shown that this type of

programming might be more useful for older offenders (see Uggen, 2000). The results of the

present work, however, imply that such efforts may be especially important for those who are

less mature from a psychosocial or identity perspective. In other words, if inmates score high on

instruments measuring whether they see themselves as antisocial, or if they do not view crime as

“wrong,” then they may have more to gain from adult social roles. For these individuals, adult

social roles play more of a protective role against antisocial behavior. Because this contradicts

previous work, further research is necessary to determine whether such an interaction holds in

other samples before making firm recommendations for policy on the basis of these results.

Psychosocial and identity/cognitive transformation appear to be strongly related to

desistance; as such, these domains of maturation should inform policy. To some extent, programs

already in existence speak to elements of both of these domains. For example, as mentioned

above, self-control and impulsivity are key components of psychosocial maturation. In a recent

Campbell Collaboration review of the literature, Piquero, Jennings, and Farrington (2010) found

that programs can increase self-control thus reducing the probability of crime or antisocial

behavior. While their study was limited to children, recent research has shown that self-control

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can be increased even into adulthood (Na and Paternoster, 2012). Thus, this research suggests

that not only should psychosocial maturation be a target for offender rehabilitation, but also in

crime prevention programs. Many child-centered programs appear to target elements of

psychosocial maturation as well, including conscientiousness (see Farrington and Welsh, 2007).

Certain well-validated prison programs also focus on “criminogenic needs,” including

antisocial attitudes and views of the self (Andrews and Bonta, 2010). These programs may

impact the identity/cognitive transformation domain, in ways that reduce the likelihood of

recidivism. For example, effective programs often target antisocial attitudes, which is a

component of identity maturation, as defined in this dissertation (Andrews, Bonta, and Wormith,

2006; Hubbard and Pealer, 2009). Because identity maturation was most consistently related to

crime of all the domains, it seems that prevention and rehabilitation programs would be well

served to focus on instilling a conformist identity in individuals—and the evidence bears this out.

From a theoretical and empirical standpoint, the other maturation domains are linked to

identity (all are correlated with identity maturation at the p<.05 level over time using the person-

period dataset), which implies that a focus on increasing levels of other domains of maturation

may also positively influence identity. For example, allowing ex-felons to have the right to vote

may increase a sense of civic engagement and feelings of being a participatory citizen

(Massoglia and Uggen, 2003; Uggen et al., 2004). This in turn may have a positive influence on

how the individual views themself. The same may be true for other domains, such as

psychosocial maturation or social role maturation. In sum, a prosocial identity appears to be an

important element that prevention and rehabilitation efforts should take into account.

Limitations

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As is the case with all research, the present dissertation is not without limitations or

shortcomings. Perhaps most prominent is the inability to directly measure certain domains of

maturation. In addition, other key elements of particular domains that were not available in the

HHDP data may be important to analyze. For example, with respect to civic maturation,

measures of voting or paying taxes would have been interesting to include. The measure used

herein, which was comprised of communal activities, likely represents a major component of

civic maturation, but other indicators might have strengthened the overall measurement of the

domain. Neurocognitive maturation was also limited in terms of measurement. For example, the

only measures available were neuropsychological exams, and these were limited to three of the

five time periods. While the domain of neurocognitive maturation showed increases over time on

average, and also was related to crime at the bivariate level, it was not related to crime in the

multivariate models. Neither, interestingly enough, was civic maturation. It could be that these

two domains suffered from poor measurement, or that further analyses are required to determine

whether they are indeed a part of the desistance story.

With respect to measurement, while psychometric analyses were conducted on subscales

within each domain (see Tables 6.2-6.6), they were not conducted for the full domain measures.

As mentioned, each domain was anticipated to be multidimensional (e.g., psychosocial

maturation included Temperance, Perspective, and Responsibility). Thus, for example, reliability

analyses would likely indicate low internal consistency. However, to the extent this is true, it

would have served to increase the standard errors in the primary analyses—thus, the findings are

conservative. Should future research seek to utilize different, more consistent measures, the

findings would be expected to be even stronger. The measures used herein, however, showed

evidence of validity with respect to the domains of maturation, including that they each increased

188

over time and were related to crime. Additionally, when examining the reliability (or relative

stability) of each domain over time, each had a Cronbach’s alpha of at least .64 with the

exception of adult social role maturation. A low internal consistency for adult social role

maturation is expected, however, since nearly all the respondents scored close to 0 at the first

two time periods, with scores increasing thereafter. Thus, some ‘shuffling’ would be expected on

this measure (e.g., people moving from a low adult social maturation score at T1 to a high score

at T5). Finally, the measurement of the maturation gaps and interactions could have a) included

more domains and b) used a different coding scheme. Future research should examine these

issues further to determine if changes in coding or measurement result in findings that differ

from those reported in this dissertation. It would also be interesting to determine whether the

results change with a different dataset.

Aside from measurement, it may be the case that maturation includes more than the five

domains identified in this dissertation. The work described above sought a developmentally

sound description of maturation that meshed with research in life-course criminology. Other

possibilities include biological indicators (such as puberty) that were not included here, but have

been examined in relation to crime previously (see Barnes and Beaver, 2010; Barnes et al., 2011;

Moffitt, 1993). However, it is arguable whether physical maturation is as relevant to desistance,

which typically occurs in the mid-20s (emerging adulthood). In addition, the domains identified

here, while drawn from the criminological and life-course literature, appear to be somewhat

comprehensive, representing adult roles, emotional, psychological, and cognitive aspects of

becoming an adult. Thus, the definition of maturation may be applicable to more than just

examinations of crime over the life-course.

189

In terms of the analyses, other models or methodologies could have been used. One

competing method, group based trajectory models (Nagin, 2005), may have applied to the

present dissertation. However, theoretically, it was not anticipated that maturation develops in a

group-based manner. Other research has examined one type of maturation (psychosocial) in a

group-based framework (see Monahan et al., 2009). This research identified groups of antisocial

behavior trajectories, and then found that psychosocial maturation predicted membership in each

of the groups. Identifying groups of antisocial behavior trajectories was not a focus of the current

dissertation. In addition, previous research with the HHDP data has already utilized this

approach, identifying distinct classes of offending trajectories (see Barker et al., 2007; White et

al., 2001). Finally, because of the sheer volume of the number of model specifications that could

have been used, it was not possible to show or test each in the dissertation. To some extent,

certain choices are driven by theoretical concerns (e.g., to use random coefficients for particular

variables, to model the covariance between the random effects). Where this was the case, the

analyses described in the preceding chapters were driven by theoretical considerations. Where

theory was less clear, models were often checked with various specifications as sensitivity

analyses, though not always shown.42 In general, the substantive results were replicated, but

where differences were found, they were noted. The main story—that adult social role,

psychosocial, and identity maturation are related to crime over time, for the most part remained

consistent across specifications.

Finally, while the dissertation took advantage of a unique dataset which includes data the

covers childhood, adolescence, emerging adulthood and young adulthood, it is true that key

segments of the life-course were not analyzed. For example, researchers in criminology have

begun to place more emphasis on early childhood (Tremblay, 2012) as it relates to offending in 42 Certain models, with random effects for variables in addition to age and age2, did not converge.

190

later life. Participation began at age 12 and thus little data were collected on early childhood .

However, the delinquency questions asked about ever engaging in delinquency, thus we have

information on ages even younger than 12. In addition, age 12 is still considered childhood, and

it is often the case that studies that follow individuals into adulthood do not begin prior to age 12,

and those that do, typically begin after age 7 (see Piquero et al., 2003). Furthermore, at age 12

youth were questioned about some earlier events (e.g., delinquent behavior in the last 3 years).

Nonetheless it remains possible that information gathered in the first years of life could be

consequential to a study of maturation and desistance. For example, early experiences with child-

rearing practices could predict onset of maturation and help explain why people vary in terms of

maturation levels throughout life. In addition, recent work has emphasized the importance of

examining crime throughout late adulthood (Laub and Sampson, 2003; Piquero et al., 2007). The

notion of “false desistance” (Laub and Sampson, 2001) suggests that what may appear to be

desistance may simply be a lull in offending that is missed if the study ends before offending has

re-emerged. In some sense, it is impossible to determine whether desistance has actually

occurred without following the individuals until death. Future work should explore how

applicable the theoretical framework advanced in this dissertation is to data that covers different

portions of the life-course.

Further, because of the span between waves in the dataset used, the exact timing of

events could not be adequately analyzed. For example, a person who was married at T5 might

have gotten married at any point from age 25 to 31. Work has suggested that the “benefits” of

marriage may begin to accrue even prior to the event of marriage (see Laub and Sampson, 2003;

Miller-Tutzauer, Leonard, and Windle, 1991). More nuanced event-timing information may have

revealed more precise estimates of the effects of those events on crime/delinquency over the life-

191

course. Nonetheless, because the focus of the current dissertation was not on events alone but

rather development over time, this is not likely a major drawback of the analyses. Future work

should seek to determine if timing of transitions (particularly with respect to adult social role

maturation) has a significant influence on the empirical relationship between maturation and

crime.

Summary and Conclusion

Despite these limitations (and inevitably others not discussed here), the findings reported

above illustrate several things. First, as is the case with criminological theory in general, there

are increasingly numerous theoretical explanations of desistance from crime. It is important, in

order to advance the field, to make sense of these explanations, including their (possible)

relationship to one another. The notion of maturation in terms of multiple domains, allows us to

see how these seemingly divergent explanations of desistance may be related and indeed, may be

part of the same general framework. In addition, it may be argued that criminology has yet to

have offered a comprehensive explanation of desistance, but rather has identified variables or

factors that relate to a decline in crime over time. While the results reported in this current

dissertation do not solve that problem definitively, it is perhaps a step in the right direction

toward theoretical clarification. For example, placing adult social roles in the context of

maturation, I believe, helps to better understand why these roles are engaged in or not and

perhaps how they may affect behavior. Utilizing more than one set of factors to explain

desistance also seemingly represents a more powerful explanation than relying on one or two

isolated variables.

192

Second, the findings reported in this dissertation may help to resurrect a long forgotten

theory of desistance. Indeed, the Gluecks’ maturation reform theory was perhaps the first

explanation of desistance from crime in the criminological literature. For reasons explained in

the second chapter of this dissertation, the Gluecks’ theory has been somewhat overlooked in the

recent resurgence of desistance research. Yet at the same time, it seems that they had possibly

identified a multi-faceted and interdisciplinary approach to understanding desistance—one that

has much potential to this day. As was pointed out above, the Gluecks, as far back as 1940, had

called for researchers to take the torch from them, fleshing out what maturation is comprised of.

Hopefully, this work will serve to show that this was not a call made without merit. Future

research should seek to build on this work, further clarifying what maturation means and how it

is related to crime over the life-course.

Hopefully, the analyses reported above have shown that this was not a call made without

merit. Future research should seek to build on this work, further clarifying what maturation

means and how it is related to crime over the life-course.

Finally, in this dissertation , I was able to show that maturation—at least as defined

here—may be a viable explanation for desistance. Three of the five components identified in the

above chapters were significantly related to crime in the multivariate models. In addition, the

average (or total) maturation measure was strongly related to crime, having both between and

within-individual effects. This is evidence that what “causes” desistance may be a combination

of factors which include both sociogenic and ontogenetic variables.

The purpose of this dissertation was not to offer the final word on what maturation is or

how it is related to crime. Instead, it is hoped that the theoretical and empirical analyses provoke

additional research into the issue, further clarifying an important and thus far generally

193

overlooked explanation of desistance. Future work may identify additional components of

maturation or utilize other measures than those used here. The end goal should be a better

understanding of crime over the life-course—one that advances both theory and policy in

criminology.

194

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TABLES AND FIGURES

Table 5.1. Age and Sample Size for the Youngest Cohort

T1 T2 T3 T4 T5

Age 12 15 18 25 30/31 N 447 437 439 418 374

Years: T1-1979-81; T2-1982-84; T3-1985-87; T4-1992-94; T5-1997-99 Note: Data are available on 410 subjects from T1-T4

224

Table 5.2. Mean Distribution of Delinquency: T1-T5 Variable

T1 T2 T3 T4 T5

Any Delinquency (µ/sd) .38 (.49) .53 (.50) .55 (.50) .32 (.47) .17 (.37)

Variety Score (µ/sd) .57 (.91) 1.07 (1.43) 1.15 (1.54) .57 (1.10) .28 (.78)

225

Table 5.3. Descriptive Statistics for Covariates Item Time Range µ/sd Non-white T1 0-1 .09 (.28) Male T1 0-1 .51 (.50) SES T1 4-77 50.34 (21.02) Grades T1 1-4 1.66 (.70) Grades T2 1-5 1.92 (.82) Grades T3 1-5 1.96 (.84) Parental Attachment T1 9-20 18.76 (1.68) Parental Attachment T2 5-20 18.13 (2.33) Parental Attachment T3 5-20 18.04 (2.51) Friends' Deviance T1 -.35-9.17 .00 (1.00) Friends' Deviance T2 -.60-7.42 .00 (1.00) Friends' Deviance T3 -.87-5.30 .00 (1.00)

226

Table 6.1. Unconditional Growth Models for Crime/Delinquency Variety Dichotomous

Model 1 Model 2 Model 3 Model 4 Parameter Estimate (SE) Estimate (SE) Estimate (SE) Estimate (SE) Intercept -2.38 (.07)*** -2.46 (.09)*** .04 (.10) .14 (.12) Age -.36 (.05)*** -.93 (.13)*** -.64 (.09)*** -1.32 (.22)*** Age2 -1.36 (.10)*** -2.01 (.19)*** -1.51 (.17)*** -2.85 (.38)*** Variance

Components Intercept 1.18 (.13) 1.56 (.20) 1.43 (.23) 2.42 (.70)

Age

1.10 (.23)

2.95 (.99) Age2

1.24 (.39)

5.05 (2.38)

Cov Int-Age

.44 (.16)

1.07 (.53) Cov Int-Age2

-.37 (.23)

-.33 (80)

Cov Age-Age2

.47 (.20)

1.20 (.91) -2 Log L -2349.9406 -2285.4381 -2349.9406 -2285.4381 Individuals 447

Observations 2114 *p<.05, p<.01, ***p<.001

Note: Likelihood ratio test comparing Model 1 to 2 and Model 3 to 4 significant, p<.001

227

Table 7.1. Scale and Item Information for Domain Construction (Time 1) Domain Item/Scale Alpha Range Mean (SD) Adult Social Role

0-40 2.82 (9.19)

Beyond HS − − −

Full or Part Time Work (current)

− 0-1 .06 (.24)

Full or Part Time Work (ever)

− 0-1 .09 (.28)

Marriage/Cohabitate − − −

Children − − −

Work Scale − − −

Partner Attachment − − −

Civic

0-50 17.20 (11.52)

In-School Groups − 0-6 1.43 (1.23)

Out of School Groups − 0-5 1.38 (1.08)

Psychosocial

32.05-87.18 65.76 (9.47)

Independence − 1-5 3.67 (.83)

Confidence − 1-5 3.75 (.84)

Impulsivity-rev (PRF) − 1-12 7.30 (2.01)

Cognitive Structure (PRF) − 2-11 6.95 (1.85)

Agreeableness .74 1.5-5 3.95 (.61)

Identity

45-100 78.44 (9.90)

Good − 2-5 3.83 (.75)

Dishonest-rev − 1-5 4.22 (.83)

Mean-rev − 1-5 3.75 (.79)

Delinquent-rev − 1-5 3.89 (1.02)

Neurocognitive

− − −

WAIS Block Design − − −

WAIS Digit Span − − −

WAIS Digit Symbol − − −

Halstead TMA − − −

Halstead TMB − − −

Category − − −

SILS Abstract − − −

SILS Vocabulary − − −

Average Maturation 28.33-67.75 41.09 (5.43) Note: Social role domain scores are based only on work measures.

228

Table 7.2. Scale and Item Information for Domain Construction (Time 2) Domain Item/Scale Alpha Range Mean (SD)

Adult Social Role

0-40 7.50 (13.64)

Beyond HS − − −

Full or Part Time Work (current) − 0-1 .12 (.33)

Full or Part Time Work (last 3 yrs) − 0-1 .23 (.42)

Marriage/Cohabitate − 0-1 .01 (.11)

Children − − −

Work Scale − − −

Partner Attachment − − −

Civic

0-68.75 15.80 (12.48)

In-School Groups − 0-8 1.61 (1.34)

Out of School Groups − 0-6 .92 (1.01) Psychosocial

33.33-94.87 62.08 (11.33)

Independence − 1-5 3.83 (.79)

Confidence − 1-5 3.62 (.93)

Impulsivity-rev (PRF) − 0-12 6.32 (2.60)

Cognitive Structure (PRF) − 1-11 6.46 (2.10)

Agreeableness .76 2-5 3.97 (.53) Identity

25-100 81.00 (9.94)

Good − 2-5 3.87 (.68)

Dishonest-rev − 1-5 4.35 (.72)

Mean-rev − 1-5 3.82 (.68)

Delinquent-rev − 1-5 4.15 (.85)

Neurocognitive

WAIS Block Design − − −

WAIS Digit Span − − −

WAIS Digit Symbol − − −

Halstead TMA − − −

Halstead TMB − − −

Category − − −

SILS Abstract − − −

SILS Vocabulary − − − Average Maturation 27.50-63.84 41.53 (6.48)

229

Table 7.3. Scale and Item Information for Domain Construction (Time 3)

Domain Item/Scale Alpha Range Mean (SD) Adult Social Role

0-80 17.99 (19.32)

Beyond HS − 0-1 .44 (.50)

Full Time Work (current) − 0-1 .22 (.41)

Full Time Work (last 3 yrs) − 0-1 .22 (.41)

Marriage/Cohabitate − 0-1 .02 (.13)

Children − 0-1 .01 (.09)

Work Scale − − −

Partner Attachment − − −

Civic

0-75 13.25 (14.40)

In-School Groups − 0-8 1.93 (1.74)

Out of School Groups − 0-6 .85 (1.03)

Psychosocial

47.22-92.86 71.53 (8.79)

Independence − 1-5 3.98 (.80)

Confidence − 1-5 3.52 (1.02)

Luck scale .65 1-2 1.67 (.27)

Self-control (16-PF) − 0-2 .93 (.33)

Self-Sufficient (16-PF) − 0-2 .87 (.60)

Agreeableness .82 1.25-5 4.03 (.57)

Identity

50-100 84.33 (9.71)

Good − 1-5 3.98 (.70)

Dishonest-rev − 1-5 4.55(.80)

Mean-rev − 1-5 3.98 (.80)

Delinquent-rev − 1-5 4.34 (.84)

Neurocognitive* 0-83.16 39.12 (17.88)

WAIS Block Design − 7-51 33.98 (9.56)

WAIS Digit Span − 8-26 15.86 (3.76)

WAIS Digit Symbol − 35-93 64.06 (10.25)

Halstead TMA − -57--11 -23.61 (7.55)

Halstead TMB − -150--16 -52.19 (19.06)

Halstead Category − -68-00 -19.93 (17.50)

SILS Total − 20-80 60.21 (9.14)

Spatial Relations Total − -8-68 34.38 (12.03)

Average Maturation 26.19-67.14 45.20 (7.03) *Note: TMA, TMB, and Category tests are reversed by multiplying the raw score by -1.

230

Table 7.4. Scale and Item Information for Domain Construction (Time 4) Domain Item/Scale Alpha Range Mean (SD)

Adult Social Role

0-95.65 39.16 (25.01)

Beyond HS − 0-1 .75 (.44)

Full Time Work (current) − 0-1 .78 (.41)

Full Time Work (last 7 yrs) − 0-1 .41 (.49)

Marriage/Cohabitate − 0-1 .33 (.47)

Children − 0-1 .13 (.34)

Work Scale .74 .20-1.00 .67 (.19)

Partner Attachment .92 1.79-4.93 4.22 (.60)

Civic

0-90 16.51 (15.13)

Civic Satisfaction − 0-2 .88 (.45)

Out of School Groups − 0-7 .91 (1.10) Psychosocial

50.48-95.65 75.46 (8.15)

Independence − 1-5 4.20 (.79)

Confidence − 1-5 3.68 (1.00)

Luck scale .64 1-2 1.63 (.27)

Self-control (16-PF) − 0-2 .88 (.45)

Self-Sufficient (16-PF) − 0-2 1.12 (.51)

Thoughtfully Reflective Decision-Making .67 1-2 1.66 (.31)

Agreeableness .79 2.5-5 4.22 (.54) Identity

58.33-100 87.19 (8.27)

Good − 2-5 4.49 (.61)

Dishonest-rev − 2-5 4.62 (.76)

Mean-rev − 1-5 4.23 (.77)

Delinquent-rev − 1-5 4.28 (.76)

View crime .86 1-5 4.46 (.73)

Honesty .84 2.3-5 4.02 (.57)

Neurocognitive*

0-86.01 45.40 (16.89)

WAIS Block Design − 4-51 36.00 (9.77)

WAIS Digit Span − 8-26 16.57 (3.69)

WAIS Digit Symbol − 37-93 67.66 (10.98)

Halstead TMA − -59--10 3-21.53 (7.12)

Halstead TMB − -230--21 -49.42 (19.00)

Category − -55--1 -20.21 (11.12)

SILS Total − 24-79 64.75 (7.96)

Spatial Relations Total − -14-69 34.96 (11.70) Average Maturation 28.99-75.57 52.76 (7.87)

*Note: TMA, TMB, and Category tests are reversed by multiplying the raw score by -1.

231

Table 7.5. Scale and Item Information for Domain Construction (Time 5) Domain Item/Scale Alpha Range Mean (SD)

Adult Social Role

9.09-98.29 59.20 (26.62)

Beyond HS − 0-1 .80 (.40)

Full Time Work (current) − 0-1 .79 (.41)

Full Time Work (last 7 yrs) − 0-1 .65 (.48)

Marriage/Cohabitate − 0-1 .64 (.48)

Children − 0-1 .36 (.48)

Work Scale .74 .14-1.00 .71 (.17)

Partner Attachment .90 2.36-5 4.25 (.52)

Civic

0-70 18.69 (14.94)

Civic Satisfaction − 0-2 .92 (.79)

Out of School Groups − 0-6 .94 (1.10)

Psychosocial

38.67-94.90 71.84 (11.12)

Independence − 1-5 4.29 (.75)

Confidence − 1-5 3.73 (.92)

Luck scale .66 1-2 1.67 (.27)

Self-control (16-PF) − 0-2 .99 (.45)

Self-Sufficient (16-PF) − 0-2 1.05 (.56)

Impulsivity-rev (PRF) 0.71 0-12 7.59 (2.71)

Thoughtfully Reflective Decision-Making .68 1-2 1.63 (.31)

Agreeableness .80 2.75-5 4.20 (.54)

Identity

59.67-100 88.05 (8.18)

Good − 2-5 4.52 (.62)

Dishonest-rev − 2-5 4.68 (.54)

Mean-rev − 1-5 4.25 (.74)

Delinquent-rev − 1-5 4.28 (.80)

View crime .83 1-5 4.58 (.62)

Honesty .87 1.10-5 4.09 (.60)

Neurocognitive*

0-91.58 52.71 (16.47)

WAIS Block Design − 9-51 38.17 (9.24)

WAIS Digit Span − 8-27 17.35 (4.17)

WAIS Digit Symbol − 39-93 69.26 (11.08)

Halstead TMA − -74--11 -19.71 (6.28)

Halstead TMB − -147--20 -45.59 (15.93)

Category − -48--1 -13.78 (8.85)

SILS Total − 23-79 67.37 (7.62)

Spatial Relations Total − 5-67 36.55 (11.98)

Average Maturation 27.52-78.21 58.17 (9.14) *Note: TMA, TMB, and Category tests are reversed by multiplying the raw score by -1.

232

Table 7.6. Social Maturation Growth Models Unconditional Conditional

Model 1 Model 2 Model 3 Model 4 Model 5

Parameter Estimate (SE) Estimate (SE) Estimate (SE) Estimate (SE) Estimate (SE) Intercept 2.14 (.84)* 2.16 (.71)** 1.37 (.84) .57 (.92) 2.20 (.72)**

Age 22.28 (2.41)*** 22.09 (2.14)*** 22.12

(2.14)*** 23.89

(2.28)*** 22.10

(2.14)*** Age2 4.68 (1.26)*** 4.82 (1.15)*** 4.81 (1.15)*** 4.78 (1.15)*** 4.82 (1.15)*** Male

1.54 (.89) 3.12 (1.15)*

Male*Age

-3.41 (1.56)* Non-white

-.53 (1.58)

Variance Components

Intercept 22.22 (7.30) .00 (.00) .00 (.00) .00 (.00) .00 (.00) Residual 363.39 (12.73) 277.28 (9.93) 276.25 (9.89) 276.03 (9.87) 277.32 (42.68)

Age

38.84 (16.29) 339.59 (16.31) 40.21 (16.20) 38.71 (119.83) Age2

26.92 (6.53) 27.16 (6.51) 26.40 (6.46) 26.91 (42.62)

-2 Log L -9087.9695 -8983.3947 -8981.8972 -8979.5164 -8983.3355 Individuals 447

Observations 2068 *p<.05, **p<.01, ***p<.001

233

Table 7.7. Civic Maturation Growth Models Unconditional Conditional

Model 1 Model 2 Model 3 Model 4 Model 5

Parameter Estimate (SE) Estimate (SE) Estimate (SE) Estimate (SE) Estimate (SE) Intercept 17.03 (.60)*** 17.03 (.55)*** 17.70 (.70)*** 18.19 (.74)*** 17.03 (.57)***

Age -6.91 (1.50)*** -6.88 (1.43)*** -6.90 (1.43)*** -7.84 (1.50)*** -6.88 (1.43)*** Age2 4.29 (.79)*** 4.24 (.74)*** 4.25 (.74)*** 4.26 (.74)*** 4.24 (.74)*** Male

-1.31 (.82) -2.26 (.95)*

Male*Age

1.84 (.90)* Nonwhite

.02 (1.46)

Variance Components

Intercept 48.18 (5.27) 38.42 (5.50) 38.25 (5.51) 38.34 (5.51) 38.43 (5.50) Residual 139.52 (4.82) 123.76 (4.89) 123.70 (4.89) 123.58 (4.88) 123.76 (4.90)

Age

25.47 (8.76) 26.21 (8.67) 25.78 (8.70) 25.48 (8.71) Age2

.35 (2.79) .28 (2.76) .31 (2.78) .39 (2.79)

-2 Log L -8426.7288 -8398.8898 -8396.1405 -8393.2458 -8398.8897 Individuals 447

Observations 2112 *p<.05, **p<.01, ***p<.001

234

Table 7.8. Psychosocial Maturation Growth Models Unconditional Conditional

Model 1 Model 2 Model 3 Model 4

Parameter Estimate (SE) Estimate (SE) Estimate (SE) Estimate (SE) Intercept 63.04 (.46)*** 62.75 (.55)*** 62.78(.46)*** 69.79 (.47)*** Age 14.96 (1.17)*** 14.95 (1.17)*** 14.94 (1.17)*** 14.93 (1.17)*** Age2 -5.21 (.61)*** -5.20 (.61)*** -5.20 (.61)*** -5.20 (.61)*** Male

.57 (.61)

Male*Age Nonwhite

3.02 (1.08)* 2.83 (1.37)* Nonwhite*Age

.24 (1.10)

Variance Components

Intercept 23.51 (2.81) 23.42 (2.80) 22.79(2.76) 22.79 (2.76) Residual 80.52 (2.83) 80.52 (2.83) 80.52 (2.83) 80.52 (2.83) -2 Log L -7602.9326 -7602.4953 -7599.0415 -7599.0173 Individuals 445

Observations 2052 *p<.05, **p<.01, ***p<.001

235

Table 7.9. Identity/Cognitive Transformation Maturation Growth Models Unconditional Conditional

Model 1 Model 2 Model 3 Model 4

Parameter Estimate (SE) Estimate (SE) Estimate (SE) Estimate (SE) Intercept 78.35 (.41)*** 79.98 (.50)*** 79.03(.54)*** 78.22 (.42)*** Age 11.11 (.99)*** 11.05 (.99)*** 12.30 (1.02)*** 11.11 (.99)***

Age2 -3.19 (.52)*** -3.18 (.52)*** -3.21 (.51)*** -3.19 (.52)*** Male

-3.15 (.57)*** -1.33 (.51)

Male*Age

-2.38 (.51)*** Nonwhite

1.53 (1.05)

Variance Components

Intercept 26.04 (2.65) 23.59 (2.49) 23.85 (2.49) 25.85 (2.64) Residual 59.91 (2.09) 59.90 (2.09) 59.09 (2.06) 59.91 (2.09) -2 Log L -7503.3432 -7488.7306 -7477.9958 -7502.2875 Individuals 447

Observations 2094 *p<.05, **p<.01, ***p<.001

236

Table 7.10. Neurocognitive Maturation Growth Models Unconditional Conditional

Model 1 Model 2 Model 3 Model 4

Parameter Estimate (SE) Estimate (SE) Estimate (SE) Estimate (SE) Intercept 32.82 (.95)*** 33.18 (1.23)*** 34.11 (.96)*** 34.07 (.97)*** Age 9.93 (.46)*** 9.93 (.46)*** 9.92 (.46)*** 9.95 (.48)*** Male

-.72 (1.56)

Male*Age Nonwhite

-14.50 (2.68)*** -14.10 (3.29)*** Nonwhite*Age

-.34 (1.67)

Variance Components

Intercept 237.25 (17.92) 237.71 (17.98) 221.54 (16.84) 221.57 (16.84) Residual 61.99 (3.22) 61.99 (3.22) 61.92 (3.22) 62.00 (3.22) -2 Log L -4618.644 -4617.1776 -4602.4773 -4601.0239 Individuals 433

Observations 1177 *p<.05, **p<.01, ***p<.001

237

Table 7.11. Average Maturation Growth Models Unconditional Conditional

Model 1 Model 2 Model 3 Model 4 Model 5

Parameter Estimate (SE) Estimate (SE) Estimate (SE) Estimate (SE) Estimate (SE) Intercept 40.42 (.33)*** 40.44 (.29)*** 40.65 (.36)*** 40.46 (.30)*** 40.38 (.30)***

Age 7.21 (.81)*** 7.17 (.75)*** 7.17 (.75)*** 7.17 (.75)*** 7.36 (.75)*** Age2 1.36 (.42)*** 1.37 (.39)*** 1.37 (.39)*** 1.37 (.39)*** 1.37 (.39)*** Male

-.43 (.43)

Male*Age Nonwhite

-.32 (.76) .72 (.88) Nonwhite*Age

-2.24 (.95)*

Variance Components

Intercept 14.78 (1.60) 8.96 (1.51) 8.93 (1.52) 8.98 (1.52) 9.07 (1.52) Residual 38.58 (1.38) 31.38 (1.28) 31.40 (1.28) 31.39 (1.28) 31.28 (1.27)

Age

8.96 (3.00) 8.85 (3.00) 8.86 (3.01) 8.57 (2.99) Age2

1.21 (.95) 1.23 (.95) 1.23 (.95) 1.29 (.95)

-2 Log L -6727.7074 -6662.6414 -6662.1233 -6662.5528 -6659.7671 Individuals 445

Observations 2005 *p<.05, **p<.01, ***p<.001

238

Table 8.1 Bivariate Relationships Between Maturation Domains and Crime/Delinquency

Obs Variety Score Dichotomous Score^

Adult Social Role Maturation 2113 -.14*** -.19*** Civic Maturation 2111 -.06* -.04* Psychosocial Maturation 2052 -.14** -.17** Identity/Cognitive

Transformation Maturation 2094 -.33** -.31** Neurocognitive Maturation 1176 -.14** -.09** Total Maturation 2005 -.25** -.26**

^Point biserial correlations used *p<.05; **p<.01

239

Table 8.2. Effect on Delinquency of a Standard Deviation Change in Maturation

Domain Variety Score Dichotomous Score Adult Social Role Maturation -.36 -.19 Civic Maturation -.13 -.02 Psychosocial Maturation -.20 -.11 Identity/Cognitive

Transformation Maturation -.51 -.24 Neurocognitive Maturation .03 .06 Total Maturation -.37 -.19 Note: Data represent the difference in means of delinquency between

observations one standard deviation above the mean vs. all other observations.

240

Table 8.3. Growth Models of Social Maturation on Crime (Variety) Variety Score

Model 1 Model 2 Model 3 Model 4

Parameter Estimate (SE) Estimate (SE) Estimate (SE) Estimate (SE) Intercept -2.29 (.09)*** -1.39 (.61)* -1.37 (.61) -1.09 (.62) Age -.44 (.10)*** -.34 (.10)** -.17 (.13) -.39 (.10)*** Age2 -2.03 (.16)*** -1.89 (.16)*** -1.68 (.19)*** -1.88 (.16)*** Social Maturation -.00 (.00) -.01 (.00)* -.00 (.00)*

Social Maturation*Age

-.01 (.00)†

Mean Social Maturation

-.01 (.00)*

Social Maturation Deviation

-.00 (.00)

Male

.85 (.12)*** .84 (.12)*** .83 (.12)*** Nonwhite

-.26 (.22) -.26 (.22) -.54 (.23)*

SES

.00 (.00) .00 (.00) .00 (.00) Grades

.08 (.10) .08 (.10) .08 (.10)

Friends' Dev

.68 (.07)*** .68 (.07)*** .70 (.07) *** Attachment

-.09 (.03)* -.09 (.03)* -.09 (.03)*

Variance Components

Intercept 1.22 (.14) .57 (.08) .57 (.08) .54 (.08) Age .83 (.17) .61 (.15) .59 (.14) .63 (.15) Age2 .92 (.31) .92 (.29) .91 (.29) .92 (.30) -2 Log L -2220.2063 -1953.0492 -1951.1051 -1891.7317 Individuals 447 407 407 384 Observations 2068 1912 1912 1863 ***p<.001, **p<.01, * p<.05, †p=.05

241

Table 8.4. Growth Models of Social Maturation on Crime (Dichotomous) Dichotomous Score

Model 1 Model 2 Model 3 Model 4

Parameter Estimate (SE) Estimate (SE) Estimate (SE) Estimate (SE) Intercept .35 (.14)* 1.45 (.96) 1.49 (.97) 1.63 (.99) Age -.75 (.19)*** -.63 (.18)** -.37 (.22) -.67 (.19)** Age2 -2.52 (.35)** -2.31 (.32)*** -1.98 (.36)*** -2.31 (.33)*** Social Maturation -.01 (.00)* -.01 (.00)** -.01 (.00)*

Social Maturation*Age

-.01 (.01)

Mean Social Maturation

-.02 (.01)*

Social Maturation Deviation

-.01 (.00)*

Male

1.07 (.18)*** 1.06 (.18)*** 1.06 (.18)*** Nonwhite

-.81 (.33)* -.82 (.34)* -1.02 (.36)**

SES

.01 (.00) .01 (.00) .01 (.00) Grades

.00 (.15) -.01 (.15) .03 (.15)

Friends' Dev

1.02 (.14) *** 1.01 (.14)*** .97 (.14)*** Attachment

-.10 (.05)* -.10 (.05)* -.10 (.05)

Variance Components

Intercept 1.82 (.37) .79 (.26) .79 (.26) .81 (.26) Age 2.18 (.72) 1.69 (.60) 1.67 (.60) 1.70 (.61) Age2 4.46 (1.93) 3.92 (1.49) 4.05 (1.51) 3.76 (1.48) -2 Log L -1227.8917 -1062.5484 -1060.9051 -1036.5712 Individuals 447 407 407 384 Observations 2068 1912 1912 1863 ***p<.001, **p<.01, * p<.05

242

Table 8.5. Growth Models of Civic Maturation on Crime (Variety) Variety Score

Model 1 Model 2 Model 3 Model 4

Parameter Estimate (SE) Estimate (SE) Estimate (SE) Estimate (SE) Intercept -2.31 (.09)*** -1.42 (.60)* -1.43 (.60)* -1.49 (.61)* Age -.54 (.08)*** -.50 (.08)*** -.43 (.12)*** -.50 (.08)*** Age2 -2.04 (.16)*** -1.93 (.16)*** -1.94 (.16)*** -1.93 (.17)*** Civic Maturation -.00 (.00) .00 (.00) .00 (.00)

Civic Maturation*Age

-.00 (.01)

Mean Civic Maturation

.01 (.01)

Civic Maturation Deviation

-.00 (.00)

Male

.87 (.11)*** .87 (.11)*** .84 (.12)*** Nonwhite

-.31 (.21) -.31 (.21) -.51 (.23)*

SES

.00 (00) .00 (.00) .00 (.00) Grades

.09 (.10) .09 (.10) .11 (.10)

Friends' Deviance

.68 (.07)*** .68 (.07)*** .70 (.07)*** Attachment

-.09 (.03)** -.09 (.03)** -.10 (.03)**

Variance Components

Intercept 1.21 (.14) .55 (.08) .55 (.08) .55 (.08) Age .84 (.17) .63 (.15) .63 (.15) .65 (.15) Age2 .94 (.31) .93 (.29) .94 (.29) .96 (.30) -2 Log L -2299.4238 -2024.0343 -2023.7681 -1933.9318 Individuals 447 407 407 389 Observations 2111 1950 1950 1899 ***p<.001, **p<.01, * p<.05

243

Table 8.6. Growth Models of Psychosocial Maturation on Crime (Variety) Variety Score

Model 1 Model 2 Model 3 Model 4

Parameter Estimate (SE) Estimate (SE) Estimate (SE) Estimate (SE) Intercept -.72 (.27)* -.04 (.65) .09 (.66) .10 (.82) Age -.35 (.09)*** -.33 (.08)*** .22 (.48) -.35 (.09)*** Age2 -2.13 (.17)*** -1.96 (.16)*** -1.93 (.16)*** -1.97 (.16)*** Psychosocial Maturation .02 (.00)*** -.02 (.00)*** -.02 (.00)***

Psychosocial Maturation*Age

-.01 (.01)

Mean Psychosocial Maturation

-.02 (.01)*

Psychosocial Maturation Deviation

-02 (.00)***

Male

.91 (.11)*** .91 (.11)*** .89 (.12)*** Nonwhite

-.26 (.22) -.27 (.22) -.49 (.24)

SES

.00 (.00) .00 (.00) .00 (.00) Grades

.06 (.10) .06 (.10) .05 (.10)

Friends' Deviance

.63 (.07)*** .63 (.07)*** .65 (.07)*** Attachment

-.08 (.03)* -.08 (.03)* -.08 (.03)*

Variance Components

Intercept 1.18 (.13) .58 (.08) .58 (.08) .58 (.09) Age .78 (.17) .56 (.14) .54 (.14) .58 (.15) Age2 .75 (.30) .69 (.27) .63 (.27) .75 (.29) -2 Log L -2204.9986 -1950.1182 -1949.442 -1855.8902 Individuals 445 407 407 384 Observations 2052 1903 1903 1844 ***p<.001, **p<.01, * p<.05

244

Table 8.7. Growth Models of Psychosocial Maturation on Crime (Dichotomous) Dichotomous Score

Model 1 Model 2 Model 3 Model 4

Parameter Estimate (SE) Estimate (SE) Estimate (SE) Estimate (SE) Intercept 2.92 (.51)*** 3.62 (1.04)*** 3.69 (1.05)*** 3.68 (1.26)** Age -.60 (.15)*** -.62 (.15)*** -.18 (.82) -.61 (.15)*** Age2 -2.48 (.34)*** -2.35 (.31)*** -2.33 (.31)*** -2.40 (.32)*** Psychosocial Maturation -.04 (.01)*** -.04 (.01)*** -.04 (.01)***

Psychosocial Maturation*Age

.01 (.01)

Mean Psychosocial Maturation

-.04 (.01)*

Psychosocial Maturation Deviation

-.04 (.01)***

Male

1.14 (.18)*** 1.14 (18)*** 1.14 (.18)*** Nonwhite

-.66 (.33)* -.66 (.33)* -.97 (.36)*

SES

.01 (.00) .01 (.00) .01 (.00) Grades

-.01 (.15) .01 (15) -.05 (.15)

Friends' Deviance

.88 (.13)*** .88 (.13)*** .89 (.14)*** Attachment

-.10 (.05)* -.10 (.05)* -.09 (.05)

Variance Components

Intercept 1.71 (.33) .80 (.24) .81 (.24) .79 (.24) Age 1.47 (.59) 1.17 (.51) 1.13 (.51) 1.11 (.51) Age2 2.43 (1.54) 2.47 (1.24) 2.36 (1.22) 2.56 (1.27) -2 Log L -1206.5762 -1049.283 -1049.1348 -1011.8642 Individuals 445 407 407 384 Observations 2052 1903 1903 1844 ***p<.001, **p<.01, * p<.05

245

Table 8.8. Growth Models of Identity Maturation on Crime (Variety) Variety Score

Model 1 Model 2 Model 3 Model 4

Parameter Estimate (SE) Estimate (SE) Estimate (SE) Estimate (SE) Intercept 1.90 (.34)*** 1.89 (.64)** 2.58 (.65)*** 3.80 (.91)*** Age -.22 (.08)* -.23 (.08)** 2.50 (.64)*** -.28 (.08)** Age2 -2.15 (.16)*** -2.00 (.16)*** -1.83 (.15)*** -1.99 (.16)*** Identity Maturation -.05 (.00)*** -.04 (.00)*** -.05 (.00)***

Identity Maturation*Age

-.03 (.01)***

Mean Identity Maturation

-.07 (.01)***

Identity Maturation Deviation

-.04 (.00)***

Male

.78 (.11)*** .73 (.11)*** .68 (.11)*** Nonwhite

-.22 (.20) -.23 (.20) -.34 (.22)

SES

.00 (.00) .00 (.00) .00 (.00) Grades

.02 (.09) .03 (.09) -.02 (.09)

Friends' Deviance

.56 (.07)*** .56 (.06)*** .52 (.07)*** Attachment

-.07 (.03)* -.06 (.03)* -.05 (.03)

Variance Components

Intercept .85 (.11) .45 (.07) .43 (.07) .43 (.07) Age .73 (.16) .54 (.14) .48 (.13) .60 (.15) Age2 .75 (.29) .74 (.27) .65 (.26) .75 (.28) -2 Log L -2193.9097 -1945.5915 -1936.5578 -1853.5341 Individuals 447 407 407 389 Observations 2094 1934 1934 1883 ***p<.001, **p<.01, * p<.05

246

Table 8.9. Growth Models of Identity Maturation on Crime (Dichotomous) Dichotomous Score

Model 1 Model 2 Model 3 Model 4

Parameter Estimate (SE) Estimate (SE) Estimate (SE) Estimate (SE) Intercept 6.79 (.70)*** 6.08 (1.11)*** 6.91 (1.17)*** 8.75 (1.50)*** Age -.47 (.15)** -.55 (.15)*** 3.11 (1.22)** -.62 (.16)*** Age2 -2.65 (.33)** 2.49 (.31)*** -2.30 (.30)*** -2.49 (.32)*** Identity Maturation -.08 (.01)*** -.06 (.01)*** -.07 (.01)***

Identity Maturation*Age

-.04 (.01)**

Mean Identity Maturation

-.10 (.02)***

Identity Maturation Deviation

-.05 (.01)***

Male

.93 (.17)*** .88 (.17)*** .83 (.17)*** Nonwhite

-.62 (.31)* -.63 (.31)* -.71 (.33)*

SES

.01 (.00) .01 (.00) .01 (.00) Grades

-.07 (.14) -.06 (.14) -.14 (.15)

Friends' Deviance

.79 (.13)*** .78 (.13)*** .69 (.13)*** Attachment

-.07 (.05) -.06 (.05) -.05 (.05)

Variance Components

Intercept 1.06 (.27) .53 (.22) .55 (.22) .52 (.22) Age 1.85 (.62) 1.57 (.56) 1.51 (.55) 1.68 (.59) Age2 3.18 (1.51) 3.12 (1.28) 2.88 (1.20) 3.11 (1.29) -2 Log L -1200.2719 -1053.5288 -1048.9261 -1019.3862 Individuals 447 407 407 389 Observations 2094 1934 1934 1883 ***p<.001, **p<.01, * p<.05

247

Table 8.10. Growth Models of Neurocognitive Maturation on Crime (Variety) Variety Score

Model 1 Model 2 Model 3 Model 4

Parameter Estimate (SE) Estimate (SE) Estimate (SE) Estimate (SE) Intercept -2.51 (.19)*** -1.48 (.82) -1.51 (.82) -1.49 (.83) Age -.97 (.22)*** -.84 (.23)*** -.61 (.37) -.77 (.21)** Age2 -1.28 (.42)** -1.52 (.44)** -1.39 (.46)** -1.50 (.44)** Neurocog Maturation -.00 (.00) .00 (.00) .00 (.00)

Neurocog Maturation*Age

-.01 (.01)

Mean Neurocog Maturation

.01 (.01)

Neurocog Maturation Deviation

-.00 (.01)

Male

1.05 (.15)*** 1.05 (.15)*** 1.02 (.15)*** Nonwhite

-.20 (.30) -.20 (.29) -.54 (.33)

SES

.00 (.00) .00 (.00) .00 (.00) Grades

-.11 (.14) -.11 (.14) -.11 (.14)

Friends' Deviance

.70 (.09)*** .70 (.09)*** .73 (.09)*** Attachment

-.09 (.04)* -.09 (.04)* -.10 (.04)*

Variance Components

Intercept 1.55 (.20) .87 (.14) .88 (.14) .84 (.14) Age 1.19 (.42) 1.07 (.43) 1.08 (.43) 1.05 (.43) Age2 1.00 (.71) 1.34 (.76) 1.17 (.75) 1.32 (.76) -2 Log L -1219.6022 -1056.9773 -1056.6594 -1028.977 Individuals 433 397 397 378 Observations 1176 1089 1089 1070 ***p<.001, **p<.01, * p<.05

248

Table 8.11. Growth Models of Average Maturation on Crime (Variety) Variety Score

Model 1 Model 2 Model 3 Model 4

Parameter Estimate (SE) Estimate (SE) Estimate (SE) Estimate (SE) Intercept -.52 (.27) .07 (.66) .12 (.66) .59 (.79) Age -.14 (.10) -.17 (.10) .52 (.47) -.20 (.10) Age2 -1.92 (.16)*** -1.80(.16)*** -1.67 (.17)*** -1.81 (.16)*** Average Maturation -.04 (.01)*** -.03 (.01)*** -.03 (.01)***

Average Maturation*Age

-.01 (.01)

Mean Average Maturation

-.04 (.01)***

Average Maturation Deviation

-.03 (.01)***

Male

.87 (.12)*** .86 (.11)*** .85 (.12)*** Nonwhite

-.26 (.22) -.28 (.22) -.51 (.23)*

SES

.00 (.00) .00 (.00) .00 (.00) Grades

-.01 (.10) -.01 (.10) -.05 (.10)

Friends' Dev

.63 (.07)*** .63 (.07)*** .65 (.07)*** Attachment

-.08 (.03)* -.08 (.03)* -.07 (.03)*

Variance Components

Intercept 1.12 (.13) .57 (.08) .57 (.08) .54 (.08) Age .72 (.16) .54 (.14) .52 (.14) .55 (.15) Age2 .68 (.29) .70 (.27) .67 (.27) .72 (.28) -2 Log L -2124.5934 -1878.2133 -1877.105 -1838.5887 Individuals 445 407 407 390 Observations 2005 1858 1858 1833

***p<.001, **p<.01, * p<.05

249

Table 8.12. Growth Models of Average Maturation on Crime (Dichotomous) Dichotomous Score

Model 1 Model 2 Model 3 Model 4

Parameter Estimate (SE) Estimate (SE) Estimate (SE) Estimate (SE) Intercept 2.89 (.50)*** 3.58 (1.07)** 3.59 (1.07)** 3.89 (1.26)** Age -.33 (.18) -.46 (.18)* .74 (.82) -.46 (.19)* Age2 -2.25 (.35)*** -2.18 (.32)*** -1.98 (.33)*** -2.19 (.32)*** Average Maturation -.06 (.01)*** -.05 (.01)*** -.05 (.01)***

Average Maturation*Age

-.03 (.02)

Mean Average Maturation

-.06 (.02)**

Average Maturation Deviation

-.04 (.01)***

Male

1.08 (.18)*** 1.08 (.18)*** 1.05 (.18)*** Nonwhite

-.76 (.34)* -.77 (.34)* -.92 (.35)

SES

.01 (.00) .01 (.00) .01 (.00) Grades

-.13 (.15) -.13 (.15) -.16 (.16)

Friends' Dev

.93 (.14)*** .93 (.14)*** .89 (.14)*** Attachment

-.10 (.05) -.10 (.05)* -.09 (.05)

Variance Components

Intercept 1.68 (.34) .83 (.26) .83 (.26) .82 (.25) Age 1.61 (.63) 1.36 (.57) 1.35 (.57) 1.34 (.56) Age2 2.56 (1.60) 2.90 (1.36) 2.89 (1.35) 2.80 (1.35) -2 Log L -1173.1016 -1022.3611 -1021.2539 -1009.3912 Individuals 445 407 407 390 Observations 2005 1858 1858 1833 ***p<.001, **p<.01, * p<.05

250

Table 8.13. Overdispersed Binomial Regressions of Maturation Gaps on Crime T4 Variety Score T5 Variety Score

Model 1 Model 2 Model 3 Model 4

Parameter Estimate (SE) Estimate (SE) Estimate (SE) Estimate (SE) Intercept -.87 (.89) -.37 (.87) .07 (.96) .03 (.97) Social-Psychosocial Gap (T4) -.89 (.35)*

Social-Id Gap (T4)

.47 (.32) Social-Psychosocial

Gap (T5)

.29 (.37) Social-Id Gap (T5)

-.03 (.38)

Male 1.22 (.20)*** 1.19 (.21)*** .67 (.24)** .68 (.24)* Nonwhite -.44 (.40) -.39 (.40) -.47 (.51) -.44 (.51) SES .00 (.00) .00 (.00) -.00 (.01) -.00 (.01) Grades -.14 (.16) -.15 (.16) -.05 (.20) -.04 (.20) Friends' Deviance .34 (.10)** .35 (.09)*** .30 (.11)* .29 (.11)* Attachment -.15 (.05)** -.15 (.04)** -.20 (.05)*** -.20 (.05)*** N 384 381 349 349 *p<.05, **p<.01, ***p<.001

251

Table 8.14. Overdispered Binomial Regressions of Social Role Maturation on Crime

T4 Variety Score

Model 1 (Low Psychosocial)

Model 2 (High Psychosocial)

Model 3 (Low Identity)

Model 4 (High Identity)

Parameter Estimate (SE) Estimate (SE) Estimate (SE) Estimate (SE) Intercept -.99 (1.19) 1.41 (1.42) 1.89 (1.23) -2.05 (1.39) Social Maturation (T4) -1.07 (.54)* -1.62 (.58)* -1.33 (.53)* -.74 (.54) Male 1.47 (.31)*** 1.01 (.28)** 1.08 (.31)** .67 (.27)* Nonwhite -.35 (.50) -1.03 (.88) -.19 (.53) -.83 (.69) SES .01 (.01) -.00 (.01) .00 (.01) -.00 (.01) Grades .03 (.20) -.44 (.25) -.30 (.21) .11 (.26) Friends' Dev .35 (.13) .37 (.16)* .34 (.12)* .12 (.27) Attachment -.15 (.06)* .17 (.07) -.22 (.06)*** -.09 (.07) N 188 196 173 208

*p<.05, **p<.01, ***p<.001

252

Table 8.15. Overdispersed Binomial Regressions of Social Role Maturation on Crime T5 Variety Score

Model 1 (Low Psychosocial)

Model 2 (High Psychosocial)

Model 3 (Low Identity)

Model 4 (High Identity)

Parameter Estimate (SE) Estimate (SE) Estimate (SE) Estimate (SE) Intercept .91 (1.25) -6.29 (2.64)* 2.07 (1.30) -13.14 (3.28)*** Social Maturation (T5) -2.08 (.58)*** .58 (.70) -1.76 (.57)** -.77 (.61) Male .67 (.30) .33 (.38) .33 (.33) .07 (.34) Nonwhite -.71 (.74) -.67 (.71) -.77 (.78) .33 (.54) SES -.00 (.01) -.01 (.01) -.01 (.01) .00 (.01) Grades -.25 (.26) -.02 (.32) -.22 (.28) -.04 (.29) Friends' Dev .20 (.16) .57 (.18)** .16 (.16) .76 (.23)* Attachment -.16 (.06)* .11 (.14) -.19 (.07)* .47 (.17)* N 175 174 158 191 *p<.05, **p<.01, ***p<.001

253

Figure 3.1. Illustration of Moffitt’s Taxonomic Theory (Moffitt, TE. (1993). Adolescence-Limited and Life-Course-Persistent Antisocial Behavior: A Developmental

Taxonomy. Psychological Review. 100(4). 674-701, p. 677. Reprinted with permission from the American Psychological Association)

254

Figure 4.1. Maturation Domain Schema

255

Figure 5.1. Graphic Illustration of Delinquency Over Time in the HHDP

256

Figure 6.1. Delinquency Over Time with Expanded T4-T5 Items

257

Figure 6.2. Delinquency Over Time by Sex

258

Figure 6.3. Delinquency Over Time by Race

259

Figure 7.1. Maturation Domains Over Time

260

Figure 7.2. Identity/Cognitive Transformation Maturation Over Time by Sex

261

Figure 7.3. Neurocognitive Maturation Over Time by Race

262

Figure 8.1. Fitted Values of the Variety Score over Time in the Average Maturation Growth Curve Model (Model 2)

.2.4

.6.8

1Fi

tted

valu

es

10 15 20 25 30age

263

Figure 8.2. Fitted Values of Average Maturation, Within and Between Individual Model (Model 4)