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2013 The Influence of Antisocial Behavior on the Life Course: An Evolutionary Approach Joseph L. Nedelec

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COLLEGE OF CRIMINOLOGY AND CRIMINAL

THE INFLUENCE OF ANTISOCIAL BEHAVIOR ON THE LIFE COURSE: AN

EVOLUTIONARY CRIMINOLOGY APPROACH

By

JOSEPH L. NEDELEC

A Dissertation submitted to the College of Criminology and in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Degree Awarded: Summer Semester, 2013

Joseph L. Nedelec defended this dissertation on June 14, 2013. The members of the supervisory committee were:

Kevin M. Beaver

Professor Directing Dissertation

Lisa A. Eckel

University Representative

Eric P. Baumer

Committee Member

Eric A. Stewart

Committee Member

The Graduate School has verified and approved the above-named committee members, and certifies that the dissertation has been approved in accordance with university requirements.

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I dedicate this dissertation to my father, George J. Nedelec. It is my most sincere wish that he would have lived to hear his ‘number two and a half son’ be called “Dr. Nedelec”.

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ACKNOWLEDGMENTS

I wish to first acknowledge my supervisor and mentor, Dr. Kevin Beaver. Without Dr. Beaver’s guidance, support, encouragement, and honesty this project would never exist and my time in graduate school would have ended long ago. From the moment I arrived at Florida State, Dr. Beaver has supported my advancement as a scholar. Consistently, Dr. Beaver has exemplified that for which a scientist, an educator, a mentor, and a human being should strive. He has sacrificed his time, energy, involvement with family, and more to see that I succeed. Owing solely to his tutelage, I am certain that my experience in graduate school is qualitatively and quantitatively advantageous relative to those who did not work with Dr. Beaver. It is with overwhelming gratitude that I am thankful for the unimaginable benefit I have been provided in working with such an eminent scholar and person. It has been an absolute privilege to work as Dr. Beaver’s student, and I have no delusions about the improbability of the future I now have had I been without his guidance and support.

I would also like to acknowledge my dissertation committee. Dr. Eric Baumer, Dr. Eric Stewart, and Dr. Lisa Eckel have been exemplary scholars, and their comments on earlier drafts of this project were immensely valuable. It is with abundant delight that I am able to include such recognizable and influential names on the second page of this project. My pure selfishness received considerable satisfaction with their inclusion in my committee and I am exceedingly grateful for their support and guidance. All three of you have my most sincere thanks, respect, and admiration.

In the same vein, I wish to thank and acknowledge my past supervisors and committee members for my honor’s thesis, my Master’s thesis, and those who have been kind enough to write letters of recommendation on my behalf for graduate school and employment. My thanks to Dr. Gail Anderson, Dr. William Bales, Dr. Ehor Boyanowsky, Professor Neil Boyd, Mr. Scott Cobbe, Dr. Matt Eichor, Professor H. Martin Jayne, Dr. Bryan Kinney, and Professor Neil Madu. Each of you has provided me with tremendous help in the years leading up to this accomplishment.

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I would like to acknowledge my fellow FSU criminology doctoral students with whom I have shared many great experiences and formed numerous valuable friendships. Particular recognition should be given to Joseph Schwartz and Eric Connolly, both of whom provided instrumental support and friendship. It was an incredible twist of fate that allowed me to work with these two soon-to-be superstars of criminology; I have gleaned far more from them than they ever received in return.

As a criminologist, I have been exposed to mountains of research illustrating the horrible familial circumstances through which many people have lived. With such a contextual perspective, I am unable to imagine how my own familial situation could have been any better than it was. As such, I wish to acknowledge and thank my mother, Judy Brummund and my father, George J. Nedelec, for their unwavering love, encouragement, and support as I continually exploited their every resource to advance my own life (to use the vernacular of ). I want to thank all five of my siblings for their support in the past and the guidance and praise they continue to provide today. What an extraordinary benefit it was for my life to be surrounded by these incredible individuals possessing such a rich love of life, adventure, and knowledge. I have missed all of you dearly while completing my graduate education so far from home. My younger sister, Suzanne, deserves specific praise and recognition. However, sibling rivalry prevents me from providing it in print. I have been blessed with a large extended family from whom I was given incalculable love and support and who I am sure will be happy to see that I finished my schooling before retirement age. Finally, I wish to thank my wife’s family who welcomed a hockey-loving Canuck into their family of Cardinals and Saints fans. My father-in law and mother-in law have been particularly gracious in their willingness to accept me into their family. Their continued love and support is a constant humbling experience for which I am overwhelmingly grateful.

My final acknowledgement goes to my wife, Amanda. Her love, friendship, and smile have allowed me to carry on through more days than I care to admit. With selfless abandon she left an incredible job, her friends, and her family to accompany me to Tallahassee. With continued selflessness she never, not even once in four years, got upset with me for having to work on research, course work, or my dissertation. Now that it is all over, I am asking her to once again abandon what she has acquired in her life as my career advances. Her sacrifices are v

incredible and her reward miniscule. To be sure, without her support this project would not have been completed, and without her presence in my life my purpose would be deficient and my laughter diminished.

Trying to put into words the gratitude for a lifetime filled with supportive family, friends, colleagues, teachers, and mentors is a futile task of despicable insufficiency. I wish to thank each and every person who has helped me, in one way or another, to achieve the accomplishment that accompanies this project. However, it is impossible to list everyone and those who are listed have not been given the appropriate praise and recognition that they so richly deserve. This list is therefore incomplete and unsatisfactory, as my level of gratitude is beyond adequate expression.

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This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth). No direct support was received from grant P01-HD31921 for this analysis.

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TABLE OF CONTENTS

LIST OF TABLES ...... xi

LIST OF FIGURES ...... xiii

ABSTRACT ...... xiv

CHAPTER 1 STATEMENT OF THE PROBLEM ...... 1

1.1 Antisocial Conduct and Sexual Behaviors: A Quagmire ...... 3 1.2 Research Questions ...... 7 1.3 Contributions of the Current Study ...... 9 1.4 Outline ...... 12

CHAPTER 2 AND LIFE HISTORY THEORY ...... 17

2.1 Evolutionary Psychology ...... 19 2.1.1 Evolutionary Biology: An Introduction...... 20 2.1.2 Theoretical and Empirical Foundations of Evolutionary Psychology ...... 22 2.1.3 Sexual Behaviors, Delinquency, and Evolutionary Psychology ...... 24 2.1.4 Evolutionary Psychology in Criminology ...... 25 2.2 Life History Theory ...... 26 2.2.1 Empirical Research on Life History Theory ...... 27 2.3 Summary and Discussion of Evolutionary Psychology and Life History Theory ...... 33

CHAPTER 3 BIOSOCIAL CRIMINOLOGY: AN INTRODUCTION ...... 34

3.1 Behavior Genetic Research Methods: An Overview ...... 39 3.1.1 Methods for Estimating h2, c2, and e2 ...... 44 3.1.2 Twin Studies ...... 45 3.1.3 The Equal Environment Assumption in Twin Studies ...... 52 3.1.4 Assortative Mating and Twin Studies ...... 56 3.1.5 Zygosity Determination in Twin Studies...... 57

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3.1.6 The External Validity of Twin Studies ...... 60 3.1.7 Summary of Twin Studies ...... 61 3.1.8 Family Studies...... 62 3.1.9 Adoption Studies ...... 64 3.1.10 External Validity of Adoption Studies ...... 65 3.1.11 Selective Placement in Adoption Studies ...... 66 3.1.12 Timing of Adoption in Adoption Studies ...... 66 3.1.13 Monozygotic Twins Reared Apart Studies ...... 67 3.1.14 Combination Design Studies...... 69 3.1.15 Isolating Components of e2 ...... 70 3.1.16 Summary of Behavior Methods ...... 72 3.2 Molecular Genetics and Gene-Environment Interplay ...... 73 3.2.1 Molecular Genetics ...... 74 3.2.2 The Gene ...... 74 3.2.3 Genetic Polymorphisms ...... 76 3.2.4 Genetic Variance and Phenotypic Variance ...... 79 3.2.5 Gene X Environment (GxE) ...... 80 3.2.6 Gene-Environment Correlation (rGE) ...... 83 3.2.7 Passive rGE ...... 84 3.2.8 Active rGE ...... 85 3.2.9 Evocative rGE ...... 85 3.2.10 Summary of Gene-Environment Interplay ...... 86 3.3 Biosocial Research on Antisocial Behavior and Sexual/Reproductive Behaviors ...... 86 3.3.1 Meta-Analyses of Biosocial Research on Antisocial Behavior ...... 87 3.3.2 Biosocial Research on Sexual/Reproductive Behaviors...... 97 3.3.3 Summary of Biosocial Research on Antisocial Behavior and Sexual/Reproductive Behaviors ...... 102

CHAPTER 4 EVOLUTIONARY CRIMINOLOGY: AN INTRODUCTION AND OVERVIEW ...... 105

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CHAPTER 5 METHODS...... 109

5.1 Data ...... 109 5.1.1 Analytical Sample Creation ...... 114 5.1.2 The External Validity of the Add Health Twin Subsample ...... 117 5.2 Measures...... 118 5.2.1 Delinquent and Criminal Behaviors ...... 118 5.2.2 Involvement with the Criminal Justice System During Adolescence ...... 121 5.2.3 Sexual Behavior Measures ...... 121 5.2.4 Relationship and Reproductive Measures ...... 123 5.2.5 Composite Sexual Behavior Index and Composite Reproductive Behavior Index ... 126 5.2.6 Control Variables ...... 128 5.3 Analytical Plan ...... 131 5.3.1 Research Question 1 ...... 132 5.3.2 Research Question 2 ...... 133 5.3.3 Cross-Twin Correlations...... 133 5.3.4 Univariate ACE Decomposition Model ...... 134 5.3.5 Research Question 3 ...... 139 5.3.6 DeFries-Fulker (DF) Models ...... 139 5.3.7 MZ Difference Score Models ...... 142 5.3.8 Summary of Analytical Plan ...... 144

CHAPTER 6 RESULTS ...... 152

6.1 Research Question 1 ...... 152 6.1.1 Bivariate Analyses...... 152 6.1.2 Multivariate Analyses ...... 153 6.2 Research Question 2 ...... 156 6.2.1 Cross-Twin Correlations...... 156 6.2.2 Univariate ACE Models ...... 157 6.3 Research Question 3 ...... 159 6.3.1 DF Models ...... 160 6.3.2 DF Model Results for Adolescent Antisocial Behaviors ...... 161

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6.3.3 DF Model Results for Adolescent Criminal Behavior ...... 163 6.3.4 MZ Difference Score Models ...... 165 6.3.5 MZ Difference Score Models for the Adolescent Delinquency Measures ...... 168 6.3.6 MZ Difference Score Models for the Adolescent Criminal Behavior Measures ...... 168

CHAPTER 7 DISCUSSION ...... 194

7.1 Summary of Results ...... 194 7.2 Limitations of the Current Study ...... 202 7.3 Future Directions for Research and Criminological Theory ...... 205

APPENDICES ...... 208

A. UNIVARIATE ACE MODEL TABLES ...... 208

B. ITEMS FOR DELINQUENCY AND CRIMINAL INDEXES ...... 227

REFERENCES ...... 234

BIOGRAPHICAL SKETCH ...... 251

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LIST OF TABLES

Table 3.1: Examples of degrees of genetic relatedness by kinship pair...... 63

Table 5.1: Sample composition by kinship pair type...... 145

Table 5.2: Analytical sample creation by sample reduction step...... 145

Table 5.3: Sample composition by kinship pair type, genetic relatedness, and sex...... 146

Table 5.4: Age distributions of analytical sample by wave...... 147

Table 5.5: Eigenvalues and proportional variance for items comprising the sexual behavior index and the reproductive behavior index...... 148

Table 5.6: Factor loadings for items comprising the sexual behavior index and the reproductive behavior index...... 149

Table 5.7: Descriptive statistics of all study variables for all kinship pairs...... 150

Table 6.1: Zero-order correlation matrix for all adolescent antisocial behavior measures...... 170

Table 6.2: Zero-order correlation matrix for all sexual and reproductive behavior measures. ... 171

Table 6.3: Zero-order correlations between the antisocial measures, the sexual/reproductive measures, and the control variables...... 173

Table 6.4: Phenotypic correlations (zero-order) between adolescent antisocial measures and sexual/reproductive behavior outcomes for the entire study sample...... 175

Table 6.5: Significant multivariate associations between antisocial conduct in adolescence and sexual and reproductive behaviors in adulthood...... 177

Table 6.6: Cross-twin (intraclass) correlations for adolescent antisocial behavior measures by level of genetic relatedness...... 181

Table 6.7: Cross-twin (intraclass) correlations for sexual/reproductive and relationship measures by level of genetic relatedness...... 182

Table 6.8: Summary of univariate ACE model analysis for all study variables...... 184

Table 6.9: DF analyses of the effect of adolescent antisocial conduct on sexual/reproductive behaviors in adulthood...... 185

Table 6.10: Descriptive statistics for MZ difference score for all study variables...... 189

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Table 6.11: Tests of average differences between MZ twins and non-MZ twins on all study variables...... 191

Table 6.12: The effect of adolescent antisocial behaviors on sexual/reproductive behaviors in adulthood using monozygotic twin difference score models...... 193

Table 7.1: Number of significant associations between adolescent antisocial measures and any sexual/reproductive outcome item by analytical model...... 200

Table A.1: Univariate ACE model results for the adolescent antisocial measures...... 208

Table A.2: Univariate ACE model results for the sexual behavior measures...... 215

Table A.3: Univariate ACE model results for the reproductive/relationship behavior measures...... 220

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LIST OF FIGURES

Figure 3.1: Punnett square for a hypothetical ...... 77

Figure 3.2: Depiction of a hypothetical GxE...... 82

Figure 4.1: Simplified theoretical model of an evolutionary criminology approach...... 106

Figure 4.2: Conceptual framework for an evolutionary criminology (EvCrim) approach...... 108

Figure 5.1: ACE model path diagram...... 136

Figure 5.2: The ACE decomposition model for two hypothetical phenotypes...... 136

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ABSTRACT

The effects of delinquency and criminal behaviors during early adolescence on events over the life course have been well-established in the criminological research. A segment of this research has revealed that the apparent causal relationship between delinquency and later life course events may be due to a third exogenous confounding variable, namely: genetics. While biosocial research has illuminated the need to include recognition of the proportional influence of genetic factors and environmental factors the research lacks an overarching theoretical framework that allows precision in research and guidance for future research. An evolutionary approach may represent such a framework. Employing data from a large national sample of sibling pairs, this project seeks to assess this assertion by analyzing the influence of antisocial behaviors during adolescence on sexual and reproductive strategies over the life course. Three key findings emerged from the analyses. First, the majority of items tapping antisocial behavior and delinquency in adolescence, as well as measures of sexual, reproductive, and relationship behaviors in adulthood were shown to be influenced primarily by genetic and nonshared environmental factors. Second, multivariate analyses revealed that antisocial conduct during adolescence has an effect on sexual/reproductive outcomes in adulthood. However, when genetically sensitive methodologies are employed the association is significantly diminished.

Third, even after controlling for the influence of shared genetic factors and shared environmental factors some forms of antisocial behavior in adolescence had an effect on sexual/reproductive behaviors across the life course. The findings are discussed within the context of life history theory and evolutionary psychology.

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CHAPTER 1

STATEMENT OF THE PROBLEM

The intellectual and scholastic history of criminology has largely been centered on variables, methodologies, and hypotheses that are premised on a sociological worldview. This worldview is driven almost exclusively by a focus on environmental factors as explanatory agents in proposed accounts of the etiology of human behavior. Consequently, the research produced by most criminologists focuses on factors such as socialization or the influence of societal structures (Wright and Cullen, 2012). Congruently, the majority of the training of future criminologists also primarily focuses on theories and methods which emphasize only social factors (Wright et al., 2008). However, due to the significant advances in fields outside of criminology there has been a recognition by a minority of criminological researchers that the emphasis on social factors is unfounded at best and unscientific at worst (Walsh and Ellis, 2004).

Employing the perspectives and methodologies of fields such as behavioral genetics, neuroscience, molecular genetics, and evolutionary biology a small group of criminologists have contributed to an emerging perspective known as biosocial criminology (Beaver, 2009; Walsh and Beaver, 2009; Wright and Cullen, 2013). This perspective is beginning to gain significant traction within the discipline of criminology and is challenging the predominant sociological viewpoints of human behavior (Cooper, Walsh, and Ellis, 2010).

A vital component of the current biosocial criminology perspective is its incongruence with the biologically based explanations of criminology’s infancy. Gone are the hypotheses such as those associated with phrenology and in their place are theories and research based on the cutting-edge statistical and methodological practices of the hard sciences (Beaver, 2009; Wright and Boisvert, 2009; Wright et al., 2008). A resonating example of this process of sophistication 1

can be found in Nicole Rafter’s (2008) coverage of the of biological theories of .

After eight chapters of highlighting the distressing history of criminology’s integration of biology and two chapters covering modern biosocial research Rafter, a sociologically trained feminist criminologist, concludes that current research in biosocial criminology can not only propel the field forward in terms of comprehending human behavior but also work towards

“progressive social change” (p. 251).

Also found in Rafter’s (2008) book, and elsewhere (e.g., Akers and Sellers, 2009; Walsh and Ellis, 2007), is the recognition that criminology as a field is moving towards a greater integration of a multitude of academic disciplines. Indeed, some authors have addressed the issue of a possible paradigm shift within the field of criminology as a whole (Cooper et al.,

2010). Nowhere is the integration of external disciplines more obvious than that of biosocial criminology. Furthermore, a large portion of current biosocial criminology research has focused on testing the claims of traditional criminological theories. The result of much of this work has illustrated that sociological theories are not entirely incorrect but rather are incomplete (Beaver,

2009). Consequently, biosocial criminology has illustrated the need to integrate into mainstream criminology the hypotheses and methods of the biological sciences in order for criminology to remain a scientific endeavor (Cullen, 2011; Walsh, 2011). Therefore, it would appear that biosocial criminology is an agent of change that could bring about a paradigm shift in the discipline.

As Kuhn (1996) famously highlighted, with alterations of perspective and methods new questions and ways of testing hypotheses arise. A key component of a biosocial argument is the recognition that evolutionary processes were highly influential in the production of the behavioral repertoire of the human species (Walsh and Beaver, 2009). Despite this recognition,

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however, relatively little biosocial criminology research has integrated empirical assessment of the manners in which evolutionary processes could inform our understanding of antisocial behaviors (Nedelec and Beaver, 2012). In other words, while biosocial criminology has provided tremendous increases in our understanding of some proximal causes of individual differences in antisocial behaviors there is a gap in terms of our understanding of ultimate explanations of such phenotypic variation (Mealy, 2000). Furthermore, there is a lack of understanding in criminology of the manners in which individual differences in antisocial behavior can influence life course trajectories in terms of life history theory, an evolutionarily informed perspective (Rushton, 2000). Addressing these gaps in the biosocial literature will serve at least two general purposes; first, it will further refine and enhance the hypotheses of biosocial criminology and the manners in which they are tested and second, it will provide additional support for the continued integration of biosocial explanations into mainstream criminology.

1.1 Antisocial Conduct and Sexual Behaviors: A Quagmire

The utility of applying a biosocial (including evolutionary) approach to criminology is effectively illustrated in the area of research addressing the connection between sexual behaviors and antisocial conduct. A consistent finding within criminology and other disciplines is the association between antisocial conduct and sexual behavior (Beaver, Wright, and Walsh, 2008;

Capaldi, Crosby, and Stoolmiller, 1996; Ellis and Walsh, 2000; Gottfredson and Hirschi, 1990).

In general, both males and females who engage in antisocial conduct (i.e., delinquency and/or adult criminal behavior) are more likely to report more varied sexual activity, a greater number of lifetime sexual partners, and an earlier age of sexual debut than those who do not engage in antisocial conduct (Beaver, Wright, and Walsh, 2008: Ellis and Walsh, 2000; Lederman, Dakof, 3

Larrea, and Li, 2004). In their review of the relevant literature, Ellis and Walsh (2000) noted that 50 of 51 studies examining the association found a significant positive link between number of sexual partners and criminal behavior. Additionally, of 31 studies which assessed the connection between age of sexual debut and antisocial behavior 30 found a significant positive association. So consistent are such findings that researchers invariably include early onset of sexual behavior and promiscuity as components of a delinquent or antisocial lifestyle (c.f.,

Gottfredson and Hirschi, 1990; Udry and Bearman, 1998).

While the association between early age of sexual debut, higher than average number of sexual partners, varied sexual experiences, and delinquency is consistently and widely observed, both the proposed direction and explanations for the observed association are varied. The most common criminological approach to the association is the observation that sexual behavior precedes delinquency and adult criminal behaviors. Therefore, early age of sexual debut, for example, is seen as an antecedent to antisocial behavior and treated as a causal factor. This approach is illustrated in the analysis completed by Armour and Haynie (2007) where the authors found that after controlling for a number of relevant variables (e.g., respondent demographics, family structure, scholastic achievement, pubertal status, and views on dating, among others) those respondents who had an earlier onset of sexual debut had a higher degree of both concurrent and subsequent delinquency (one year later). In explaining their results the authors employ a social learning perspective peppered with reference to life course theory; they conclude:

our findings are consistent with the idea that transitioning to sexual debut increases the

salience of a peer social context that is conducive to engaging in delinquent activities

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[and that] the timing and sequence of events such as sexual activity [can have] profound

consequences, particularly when they take place prematurely. (pp. 149-150)

Armour and Haynie (2007) clearly exhibit an analytical and explanatory approach that argues sexual behaviors which vary from the average (in their case, age of sexual debut) have a causal connection to delinquency later in life.

Despite the findings of Armour and Haynie’s (2007) study, the authors provide little explanation for why the causal arrow should be in the direction in which they imply. In other words, why should early age of sexual debut cause delinquency? Could it be that engaging in delinquency leads to early onset of sexual debut? Indeed, other researchers have assessed this precise question. For example, in their examination of adolescent males in the Oregon Youth

Study Capaldi et al. (1996) found that childhood antisocial conduct significantly predicted early onset of sexual behaviors. As an illustration the authors note, “being above the median on antisocial behavior measured prepubertally (at grade 4) was predictive of twice the rate of initiation of intercourse through grade 11” (p. 355). Another example comes from a study conducted by Lohman and Billings (2008) that assessed the effect of adolescent delinquency

(drug and alcohol use and serious delinquency) as a risk factor for early sexual debut. The authors found that drug and alcohol use (a form of adolescent delinquency) predicted early sexual debut and even acted as a mediating variable on the connection between various protective factors (e.g., parental monitoring and academic achievement) and early onset of sexual behaviors.

As is evident from the brief review above, we are left with a quagmire in terms of the association between sexual behaviors and antisocial conduct. One approach illustrates that the causal arrow points from sexual behavior to delinquency, while another approach illustrates that

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the causal arrow is reversed (delinquency causes non-normative sexual behavior). Therefore, despite the consistent observation of an association between these factors criminologists have not been successful in providing an adequate explanation for such an association. It is reasonable to assume that this inadequacy owes to the likely existence of an exogenous factor that is influencing the covariance between sexual behaviors and delinquency. In other words, is it the case that the observed association is spurious and due to some unmeasured influence? As indicated in the introduction, the typical criminological approach (referred to as the standard social science model, see Chapter 3) holds that a researcher ought to seek out etiological factors that are solely derived from the environment (e.g., socioeconomic status, parenting styles, or economic structures) and ignore those that are derived from biologically based perspectives such as evolutionary theory or behavioral genetics. However, researchers outside of criminology (and as mentioned above, a few within criminology) recognize that biological perspectives such as evolutionary theory and behavioral genetics have direct relevance to the examination of sexual behaviors, antisocial conduct, and the covariation between the two. For example, in direct response to Armour and Haynie’s (2007) study (and in indirect response to studies proposing a reversal of the causal arrow) Paige Harden and colleagues (2008) illustrated that genetic factors influence both age of sexual debut and levels of delinquency, as well as the covariation between these measures. Consequently, research on the association between sexual behaviors and antisocial conduct which does not control for the influence of genetic factors is likely seriously misspecified. The results of Harden et al.’s (2008) study and the revelation of potential spuriousness was driven not only by separation from the dominant sociologically based theoretical model in criminology but also, and just as importantly, by the application of novel methods to the question of the association between sexual behaviors and antisocial conduct.

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As indicated, evolutionary theory can also help illustrate the underlying reasons for the connection between sexual behaviors and delinquency. For example, evolutionary psychologists have shown that antisocial behavior during early adolescence may in fact be part of the larger competitive field of mate acquisition and maintenance and therefore inexorably linked to sexual conduct (Buss, 1999, 2003; Kanazawa and Still, 2000; Wilson and Daly, 1997). Additionally, recognition of the intimate interplay between biology/genetics and the environment illustrated by evolutionary approaches such as life history theory can also provide guidance and methodological suggestions for reassessments of the observed connection (Rushton, 1985).

Therefore, in order to adequately navigate the quagmire presented by the previous attempts at examining and explaining the association between sexual behaviors and antisocial conduct a novel, biologically driven approach is required.

1.2 Research Questions

In summarizing the above introduction it appears that criminology in general, and assessments of the association between sexual behaviors and antisocial conduct specifically, are in need of a greater integration of the approach taken by a biosocial perspective. The question follows, why is there not a greater integration of these perspectives in contemporary criminology? Authors have noted some potential reasons for a lack of integration of biosocial ideas. For example, there appears to be an unscientific adherence to ideology which simply blinds researchers to the value of the biosocial approach (for example, see Walby and Carrier,

2010). Following Kuhn’s (1996) expectations of reactions to potential shifts in scholastic paradigms, such resistance is unsurprising. However, other more pragmatic limitations have been pointed out; for example, data limitations. Traditional criminological datasets have consisted of collecting only one respondent per household and have not included relevant 7

biological samples (see below for further discussion of this approach). Therefore, the argument goes, biosocial hypotheses cannot be tested (Beaver, 2009). However, there has been a recent increase in the type of sample necessary to complete a biosocial assessment of criminological hypotheses. These samples, such as the National Longitudinal Surveys of Youth (NLSY) and the National Longitudinal Study of Adolescent Health (Add Health) include subsamples of twins, siblings, and even cousins. Such pairings can allow for behavioral genetic analyses, for example. With the increase of such samples, the argument of a lack of available data as a prevention of an integration of biosocial criminology is no longer tenable (Beaver, 2009).

Therefore, the necessary information required to integrate a biosocial approach into mainstream criminological research has been provided by multiple outlets. The data and methodologies employed in the current study illustrate this to be the case. Additionally, the approach of the current study is to further show that both novel and traditional questions about human behavior, including criminal, can be better understood using a biosocial perspective. This idea is illustrated in application of biosocial methodologies to the question of the association between sexual behaviors and antisocial conduct.

Toward these ends, the current study will examine three main research questions that emanate from evolutionary psychology, life history theory, and behavioral genetics (all of which are components of an evolutionary criminology approach). The research questions are:

(1) Do adolescent antisocial behaviors influence reproductively relevant outcomes (e.g., sexual behaviors, types of pair-bonds, and number of offspring)?1

1 This first step is necessary in order to illustrate congruence with the current literature. While it is recognized that this step does not in itself provide a unique contribution it does set up the following research questions which do represent unique contributions. Furthermore, addressing research question one on its own allows for an illustration of how a genetically informed approach can produce different and more reliable results than an analysis which does not include recognition of the influence of genes and biology (see Chapters 3 and 4 for a more in-depth discussion of these issues). 8

(2) Are antisocial behaviors and sexual/reproductive behaviors influenced by genetic factors and to what extent, if any, is the proportional influence of genetic factors relative to environmental factors?

(3) Does the association between antisocial behaviors and reproductively relevant outcomes remain after controlling for the influence of genetic and environmental confounds?2

It should be noted here that the current study assesses the relationship between antisocial conduct and sexual behavior by treating antisocial behavior as antecedent to sexual behaviors.

This approach is taken for two primary reasons. First, given the findings of studies such as

Capaldi et al. (1996) and the observations of researchers such as Moffitt (1992) antisocial behaviors appear early on in life whereas sexual behaviors (at least sexual behaviors that can relate to reproduction) do not appear until puberty. Therefore, in the current project antisocial behaviors are considered antecedent to sexual/reproductive behaviors. Second, there is considerable interest within criminology as a discipline in how antisocial behaviors can influence individual trajectories over the life course (Benson, 2012). Consequently, narrowing in on one portion of the human condition this project focuses on how antisocial behaviors in adolescence can influence sexual and reproductive behaviors in adulthood in order to assess the etiology of such differential life course trajectories.

1.3 Contributions of the Current Study

Overall, the current project provides at least five unique contributions to the literature.

First, this study explicitly recognizes and addresses the limitations of past analyses of the

2 Another way to word this research question is, “To what extent does antisocial behavior in adolescence, as a component of the nonshared environment, account for variance in sexual and reproductive behaviors in adulthood?”. The question is not worded like this in the main text as it requires knowledge of behavioral genetic methodologies which are not outlined until Chapter 3. However, from a biosocial point of view the meaning of the question as stated in this footnote and as stated in the main text is virtually identical. 9

relationship between antisocial behaviors and reproductive outcomes. The description of the methods associated with biosocial criminology detailed in Chapter 3 outlines not only some of the methods employed in the current study but the typical shortcomings of social science research that has neglected a biosocial approach. In doing so, the current project truly is a reflection of the integrative nature of criminology sought after by its founders and mentioned above in the discussion of Nicole Rafter’s (2008) text (Walsh, 2006). Second, the current study provides a much broader look at the association between antisocial conduct and sexual/reproductive behaviors than is currently evident in the literature. Indeed, past research has tended to focus on only a few components of either factor involved in the association (this approach is taken in both traditional criminological studies as well as non-criminological analyses). For example, antisocial conduct is often operationalized as self-reported delinquency.

While the current study does employ such measures, these measures are supplemented with items tapping official contact with the criminal justice system. Criminologists have long recognized the methodological importance of differentiating between these two collections of measures of antisocial behavior, and as a result the current study includes both types

(Gottfredson and Hirschi, 1990). In addition to a broad operationalization of antisocial conduct that is rare in the literature, the current study also includes a wide range of sexual and reproductive behaviors. Rather than limiting the analyses to age of sexual debut, the current study includes measures tapping a variety of sexual behaviors, age of debut for a variety of sexual behaviors (beyond only vaginal sex), number of sexual partners, and engagement in risky sexual behavior, among others. Additionally, the current study includes measures tapping relationship outcomes such as number of long and short relationships, number of extra-pair relationships (i.e., infidelity), number of marriages, and other measures related to relationships.

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Finally, in terms of broad scope the current study also includes items that relate to the production of offspring and views on parenting. The vast array of measures relating to sexual and reproductive behaviors provides multiple benefits, not least is that it sets the current project apart from studies represented in the current literature. More specifically, including such an array of sexual and relationship/reproductive outcomes benefits not only the analysis of the association between sexual behavior and antisocial conduct but also our understanding of the etiological factors that influence each of the constituent measures. As illustrated at the end of Chapter 3, there is a paucity of biosocial research examining the relative influence of genetic and environmental factors on the etiology of a wide swath of human sexual and reproductive behaviors. The current project therefore, also provides a unique contribution in terms of the understanding of the causal factors influencing these sexual and reproductive outcomes. An additional benefit to such broad inclusion is that it allows for the assessment of multiple components of theoretical expectations derived from evolutionary psychology and life history theory (see Chapter 2).

The third unique contribution of the current project is to highlight how research questions derived from an evolutionary point of view can be empirically assessed. A common critique of an evolutionary approach to human behavior is the inability to operationalize concepts and test hypotheses (Barkow, 2006; Buss, 1999; Ketelaar and Ellis, 2000). While this straw man argument has been thoroughly dismantled in the evolutionary psychology literature (e.g., Hagen,

2005; Ketelaar and Ellis, 2000; Kurzban, 2002, 2010; Wright, 1994) there appears a relative scarcity of evolutionary approaches elucidated in the criminological literature (Nedelec and

Beaver, 2012). Therefore, the third unique contribution of the current study is two-fold: it provides an empirical assessment of hypotheses derived from an evolutionary approach and it

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incorporates (in analysis and in title) evolutionary concepts and biosocial methodologies into criminology.

The fourth unique contribution provided by the current project is an assessment of the relationship between antisocial conduct and sexual/reproductive behaviors with a methodological rigor hitherto unrepresented in the current literature. With the employment of multiple methods derived from a biosocial approach, the current study contributes to the understanding of these components of human behavior that is not represented in either the criminological or non- criminological literatures.

Finally, the fifth unique contribution of the current project is the use of a longitudinal sample and an analytical timeframe that spans almost 15 years. Previous assessments of the association between sexual behaviors and antisocial conduct are either cross-sectional in nature or include analytical timespans of one or two years.3 The use of such a long time period in the current study adds to the rigor of the assessment and allows for a unique exploration of the effect of behaviors in adolescence on outcomes in adulthood.

1.4 Outline

As a result of the integrative nature of this dissertation, reviews of biosocial criminology, behavioral genetics, evolutionary psychology, and life history theory will be provided.

Consequently, the reviews will be detailed and include discussions of various theoretical and methodological information. To provide the most accurate and comprehensible review of the respective areas, it is important to outline the organizational structure of the chapters that follow.

3 This is true of even those studies which do include a biosocial approach to the analyses (see Chapter 3).

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In Chapter 2 the theoretical scaffolding of the study will be provided via coverage of evolutionary psychology and life history theory. The first portion of the chapter will focus on the theoretical foundations of evolutionary psychology and some of the empirical evidence supporting these foundations. The section will then conclude with an overview of empirical assessments related to antisocial behavior, crime, and reproductive success in the evolutionary psychology literature. The second portion of Chapter 2 will review life history theory, a unique component of evolutionary biology and evolutionary psychology. The section will include both an exploration of the theoretical foundations as well as some examples of empirical research on these foundations. Additionally, the discussion will review how life history theory is related to evolutionary psychology and how it can inform the current study.

Chapter 3 will review the biosocial criminology approach in general. Included in this review will be coverage of the various topics assessed in biosocial criminology and the multitude of methods employed to address those topics. Additionally, the review will illustrate the methodological shortcomings of the standard social science model (SSSM) employed in mainstream criminology and highlight the manners by which the methods used by biosocial criminology can overcome these shortcomings.

It is of crucial importance to note here the reason for the very detailed description of methodologies outlined in Chapter 3. Some researchers have argued that a biosocial approach is atheoretical and focused primarily on methods (Walsh, 2011). Given the heavy reliance on the application of innovative methodologies to old criminological problems and theories, this claim seems rather appropriate at first glance. However, as Kuhn (1996) pointed out, existing paradigms are initially challenged by the employment of novel methodologies. These new methods produce novel results which in turn drive new theory construction. This dynamic was

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also recognized by Greenwald (2012) in his article entitled “There is nothing so theoretical as a good method”. Greenwald (2012) tackles the methods-theory connection in two unique ways.

First, he notes that in his field of expertise, psychology, there are a number of theories which seek to explain empirical findings rather than empirical findings driving theoretical approaches.

Greenwald (2012) argues that those empirical findings which do not conform to the tenets of the theoretical approach are explained away as anomalous rather than initiating theoretical alterations. This contention is also echoed by Ferguson and Heene (2012) who argue that theoretically driven, rather than empirically driven, research serves to create a discipline full of

“undead theories” (p.555) that live on past their utility (in terms of utility derived from empirical support). These comments are poignant to criminology where scholars have argued in a similar vein that old, outdated, and empirically unsupported theories are continually assessed and taught

(see Cullen, 2011). The second manner in which Greenwald (2012) approaches the issue is to note that the majority of Nobel Prize awards have been for the development of novel methods rather than for novel theories or the development of theory. The dual-pronged approach taken by

Greenwald and the discussions of Kuhn and others all triangulate on a single factor: the utmost importance of methodologies driving theoretical advancement. The current project is a product of such a paradigm and consequently provides an in-depth discussion of the methods of a biosocial approach to human behavior.

Chapter 3 will also comprise a detailed coverage of the assumptions and methods of behavioral genetics including a discussion on gene-environment interactions, a key component of biosocial research. In order to contextualize the approach employed in the current study, Chapter

3 will conclude with a review of biosocial research on antisocial and criminal behaviors as well as a brief review of biosocial research on sexual/reproductive behaviors. The review of biosocial

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methods and relevant research will set up the next chapter, Chapter 4, which will cover the evolutionary criminology perspective.

Chapter 4 will introduce the evolutionary criminology approach employed in the current study. This approach is an amalgamation of the assumptions, hypotheses, and methodologies of evolutionary psychology and behavioral genetics as they apply to criminologically relevant topics. In general, this new field (based primarily on the evolutionary behavioral genetics approach) is concerned with the following question, “What evolutionary forces account for the observed in human traits?” (Keller, Howrigan, and Simonson, 2011: p.281, emphasis in original). At first glance this query seems fairly straightforward. However, in order to hypothesize about the potential evolutionary forces responsible for genetic variation in human traits (in our case, antisocial and sexual/reproductive behaviors) one must first determine if there is variation in the trait’s expression (i.e., phenotypic variation) and if that variation is due to genetic factors. Therefore, Chapter 4 will both synthesize the preceding three chapters and set up the methods utilized in the current study and explained in Chapter 5.

The methods outlined in Chapter 5 will follow the theoretical guidance provided by an evolutionary criminology approach. Consequently, the research questions presented above will be assessed by employing a biosocial approach, with particular emphasis on behavioral genetics methodology. Accordingly, the chapter will be organized primarily by the research question under assessment. Descriptions of the data and each measure used in the study will be provided and followed by a detailed delineation of the analytical plan.

Chapter 6 will provide a summary of the findings of the analyses. The chapter will be organized by research question and will conclude with an overall summary of the results.

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The final chapter, Chapter 7, will be comprised of three main sections. The first section will provide another summary of the results of the analyses presented in Chapter 6 and place the findings within the overall empirical and theoretical literature provided in Chapters 1 through 4.

The second section of Chapter 7 will be an overview of the limitations of the current study.

Chapter 7 will conclude with a third section, a discussion of the implications of the current study for future research and theory construction regarding human behavior.

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CHAPTER 2

EVOLUTIONARY PSYCHOLOGY AND LIFE HISTORY THEORY

[Evolution] is a general postulate to which all theories, all hypotheses, all systems must henceforward bow and which they must satisfy in order to be thinkable and true. Evolution is a light which illuminates all facts, a trajectory which all lines of thought must follow.

-Pierre Teilhard de Chardin4

As illustrated by the above quote, evolutionary processes are essential to biological understanding. Indeed, the title (and text) of Dobzhansky’s well-known essay makes the argument that any theory or hypothesis which is incongruent with the tenets of evolutionary processes is simply incorrect or incomplete. The argument has been echoed by others (e.g.,

Dawkins, 2006) and serves to guide the entire field of biology. It is no surprise then, with such a powerful and parsimonious theoretical framework that the biological sciences have garnered considerable advances in the understanding of biological processes across a considerable range of species. The biological sciences have also provided unparalleled understanding of the human species as well. The use of the word ‘unparalleled’ is intentional here. No other perspective from either the ‘hard’ or ‘soft’ sciences (including criminology) has advanced the understanding of so much of the human condition as the biological sciences. This understanding is not limited to simply biochemical or neurological processes. To be sure, the biological sciences have made extensive contributions to research on human behavior. This argument is put succinctly by

Robinson (2004) who stated, “the biological sciences have made more progress in advancing our

4 As cited in Theodosius Dobzhansky’s (1973) essay entitled, “Nothing in biology makes sense except in the light of evolution”. 17

understanding about behavior in the last 10 years than had made in the past 50 years”

(pp. ix-x). Part of this discrepancy in contribution, as outlined above, is a result of the continued adherence to ‘anti-biology’ ideology by sociologists and criminologists. However, the argument can also be made that sociologically based criminology lacks a compelling over-arching theoretical framework which drives research in a similar manner that evolution drives biology

(Cullen, 2011; Wright and Cullen, 2012) and as a result is less apt to provide similar quality research to a biological approach.

Three points are to be taken from the introduction to this section. First, any understanding of human behavior will be enhanced by the inclusion of a biological approach. As exhibited in Chapter 3 (see below), a biological approach can take many forms (e.g., behavioral genetics, molecular genetics, and neuroscientific approaches) and therefore, researchers have a multitude of options when creating research designs. Second, a biological approach to human behavior does not necessitate the complete abandonment of what sociological approaches have provided (Walsh, 2011). Instead, a biological (i.e., biosocial) approach calls for what Wilson

(1998) terms ‘consilience’, what Rowe (1987) desired as ‘interdisciplinary communication’, and what Barkow (2006) and others (e.g., Cosmides, Tooby, and Barkow, 1992; Walsh, 1997) refer to as ‘vertical integration’. In other words, a biosocial approach employs both biological and environmental variables in its understanding of human behavior. The third point to emanate from the above discussion is that if a biological approach is to be applied to human behavior, evolutionary processes are necessitated. In other words, to the extent that Dobzhansky’s (1973) and others’ claim is correct applying a biologically based analysis of human behavior must coincide with the tenets of modern evolutionary theory. Consequently, the current study incorporates an evolutionary-based biological approach to the study of individual differences via

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the inclusion of evolutionary psychology. The next section outlines the theoretical foundations of this perspective as well as a component of evolutionary psychology relevant to the current analyses: life history theory.

Before outlining the evolutionary psychology perspective it is important to highlight one final point in this section. While behavioral and molecular genetics have been particular powerful in illuminating the differential impact of genetic factors, environmental factors, and specific polymorphisms on phenotypic variance the approaches are, prima facie, atheoretical.

However, given that they are biologically based they are inexorably tied to the processes of evolution (Ferguson, 2010). Furthermore, given that the approaches are concerned with illuminating the etiological factors of behavior they are congruent with a theoretical approach which incorporates an evolutionary understanding of behavior. Consequently, evolutionary psychology can be seen as the theoretical skeleton (framework) upon which the findings of behavioral and molecular genetics can be placed. The benefits to all parties are reciprocal. In other words, just as a skeleton is aided by the tissue with which it is attached evolutionary psychology can be further enhanced via the findings of behavioral and molecular genetics

(Hawley and Buss, 2011; Ferguson, 2010).

2.1 Evolutionary Psychology

Briefly, evolutionary psychology refers to the application of evolutionary biology to the understanding of human behavior, society, and culture (Barkow, 2006; Mealey, 2000). It is not a component of psychology or a mid- or individual-level theory (Daly and Wilson, 1988). Rather, it can be considered a paradigm or over-arching perspective that guides a wide swath of scientific research. Employing the tenets of evolutionary biology, the approach taken by evolutionary psychology explicitly recognizes that the seat of human behavior is the brain, that 19

the construction and functioning of the brain is due in part to genetic factors, and that the genes and genetic processes that are to be found in modern homo sapiens are a result of the forces of natural and sexual selection (Cosmides et al., 1992; Ellis and Walsh, 1997; Wright, 1994).

Furthermore, the approach recognizes that species-wide psychological exist in humans because the result of possessing such adaptations procured a benefit to reproduction, survival, or both (Daly and Wilson, 1988). In short, the adaptations helped to increase and were therefore differentially selected (Walsh, 2011). Readers unfamiliar with the basic tenets of evolution may find this description of evolutionary psychology confusing.

Consequently, to fully understand the perspective one must have a grasp of the basic ideas, concepts, and assumptions of evolutionary biology. The next section briefly outlines the major components of modern evolutionary biology (for more detailed reviews see Dawkins, 1999,

2006; Mayr, 2001, Wilson, 2000).

2.1.1 Evolutionary Biology: An Introduction

Any discussion of evolutionary biology necessitates a mention of Darwin’s book, On the

Origin of Species by Means of , first published in 1859. The impact of this book cannot be overstated and the quote that opened the current chapter illustrates the status of the ideas contained within Darwin’s work. Many researchers have remarked how extraordinary it is that Darwin was correct about so many aspects of biology without knowledge of quantitative genetics (Dawkins, 2006; Wright, 1994). While some details Darwin proposed have been illustrated to be incomplete or incorrect, the main thrust of his theory of evolution via natural and sexual selection has passed the proverbial test of time. The theory is as parsimonious in its claims as it is applicable in its scope. Wright (1994) captures the theory in his succinct description, 20

All the theory of natural selection says is the following. If within a species there is

variation among individuals in their hereditary traits, and some traits are more conducive

to survival and reproduction than others, then those traits will (obviously) become more

widespread within the population. The result (obviously) is that the species’ aggregate

pool of hereditary traits changes. And there you have it. (p.23)

Wright’s (1994) summary of the theory is both pithy and accurate. Additionally, it highlights the importance of genetic variance among individuals – a point that will prove to be central to upcoming discussions and the thrust of the current study.

Before moving on to evolutionary psychology it is important to provide some coverage of important definitional issues. When evolutionary biologists speak of ‘fitness’ they are referring to the relationship between the possession of a trait (genotype) and its effect on the likelihood of survival or reproduction. Therefore, fitness is not assessed simply by differential rates of survival. Given that evolution occurs over eons of time and the unit of selection is the gene, the production of offspring is paramount (Dawkins, 2006). Consequently, fitness includes both survival and reproduction.5 Related to the concept of fitness is the phrase reproductive success.

This phrase refers not only to the production of offspring in one generation, but rather the production of offspring who in turn are able to survive to a reproductive age (i.e., puberty) and reproduce themselves (Mealey, 2000). Again, when one recognizes that evolution occurs over vast spans of time the importance of passing on genetic material to multiple generations becomes more apparent (Dawkins, 2006). The final key definitions relate to processes that were recognized by Darwin in his earlier writings. Darwin recognizes the value of reproductive

5 Indeed, authors have noted that in many areas of nature reproduction seems to trump mere survival (this is particularly true among many insect species where males will engage in reproductive behavior that is almost certain to lead to death; Judson, 2002). Some researchers have noted that the risky behavior exhibited by some groups of human individuals in certain environmental contexts represents an adaptive strategy to ensure reproductive success even at the risk of poor survival prospects (Daly and Wilson, 2001; Nedelec and Beaver, 2012). 21

success to the process of evolution and therefore distinguished the processes of natural selection and sexual selection (Wright, 1994). Stated generally, natural selection refers to processes of differential selection of genetic variants which work to increase the likelihood of survival for an organism; whereas, sexual selection refers to the differential selection of genetic variants that work to increase the likelihood of reproduction (e.g., being selected as a mate; Buss, 1999;

Mealey, 2000). While these genetic variants create various adaptations6 (both physical and psychological) it is the gene that is the unit of selection in evolution (Beaver, Wright, and Walsh,

2010; Dawkins, 2006). With this very brief coverage of evolutionary biology the discussion now moves to evolutionary psychology as a perspective and how it relates to both sexual behaviors and delinquency.

2.1.2 Theoretical and Empirical Foundations of Evolutionary Psychology

Evolutionary psychology seeks to discover the underlying adaptive functions of specific behaviors. More definitively, evolutionary psychology involves “the analysis of the human mind as a collection of evolved mechanisms, the contexts that activate those mechanisms, and the behavior generated by those mechanisms” (Buss, 1999: p.47). Just as the process of natural selection has crafted in the human species a visual system based on the cumulative advantages for survival and reproduction over millennia, so too have the processes of natural and sexual selection imbued a vast array of behavioral and psychological adaptations to increase the probability of the proliferation of an individual’s genes (Lamb, Collin, and Pugh, 2007; Walsh,

2006). Furthermore, just as we have no conscious recognition of the process by which visual stimuli is converted into images for our perception, the processes tied to psychological

6 Adaptations can be defined as, “a design feature that arose and promoted its increased frequency through an extended period of natural [or sexual] selection because it functioned to increase survival and/or reproductive success” (Walsh, 2011: p. 28). 22

adaptations similarly work on an unconscious level (Cosmides et al., 1992). This can also be stated as recognition by evolutionary psychology that psychological adaptations are not deterministic but employed in a probabilistic fashion in response to specific environmental stimuli (Nedelec and Beaver, 2012; Wilson and Daly, 1997). Relatedly, while evolutionary psychologists analyze behavior they are not seeking to illuminate the evolutionary reason for a specific behavior. Rather, the mandate of an evolutionary psychology approach is to elucidate the evolutionary underpinnings for propensities to behave in certain ways and not others (Walsh,

2011).

In analyzing the evolutionary underpinnings to behaviors evolutionary psychology has focused on a wide range of topics and utilized a variety of key concepts. A key factor that has received a significant amount of attention is the difference between parenting efforts and mating efforts (Mealy, 2000). Effort, in this sense, refers to energy, time, and investment in one activity at the expense of time, energy, and investment in another activity. Mating effort, then, refers to those activities in which an individual engages that increases the likelihood of obtaining and maintaining a mating partner while parenting effort refers to those activities that relate to the care and protection of offspring. Again, the more effort spent on one component necessarily decreases the amount of effort that can be spent on another component (Daly and Wilson, 2005).

In sexually reproducing species there is a tendency for males to be differentially engaged in more mating effort than females and females tend to be more differentially engaged in more parenting effort than males, although this is not always the case (Dawkins, 2006; Judson, 2002).

Importantly, researchers have noted that just as there are differences between sexes in the parenting effort to mating effort ratio there are also within sex differences (Walsh, 2011). In other words, there is variance within a sex in terms of the likelihood that an individual will

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employ a parenting- versus mating-focused strategy (Daly and Wilson, 2005). These distinctions are important to keep in mind as they relate to discussions to follow.

2.1.3 Sexual Behaviors, Delinquency, and Evolutionary Psychology

As an example of how evolutionary psychology can illustrate the underlying cause of the association between sexual behaviors and delinquency it is necessary to recognize the species- typical social environments in which evolved strategies were selected (Buss, 2003). Walsh

(2006) highlights this point by emphasizing that the cooperative nature of the human species provides ample opportunity for cheaters in social interactions. Therefore, while criminal behavior per se is not selected for by natural selection or sexual selection, traits such as highly selfish tendencies or deception may be selected for if they increase the probability of reproduction (Walsh, 2006). These traits, often referred to as ‘conditional strategies,’ are found in all humans but vary in degree across the spectrum of behaviors (Mealy, 1995; Walsh, 2006).

Those individuals who are genetically predisposed to such traits and who are exposed to environments which encourage the expression of such traits are more likely to engage in delinquent behaviors (i.e., ‘cheating’; Walsh, 2006). In other words, the social environment guides the expression of the genetic traits.

Making use of similar logic, one can link such behavioral strategies (e.g., deception and selfishness) to sexual behaviors. Evolutionary psychologists indicate that mating strategies used by men and women are guided by numerous factors including stimuli and information provided by a potential mate (Buss, 2003). Deception is just one example of an antisocial behavioral strategy that can be employed in such interactions in order to maximize mating efforts.

Furthermore, the use of deception (and other ‘antisocial’ methods) is significantly impacted by cues from the social environment. Therefore, variation in both sexual behaviors and delinquency 24

are partially the result of the impact of variation in social environments on individual phenotypes

(Beaver, 2008). Moreover, variation in both sexual behaviors and delinquency are partially the result of the impact of variation in genetic propensity towards the use of antisocial behavioral strategies. In other words, in certain social environments, adaptive traits which work to increase sexual involvement also likely increase involvement in criminal conduct.

2.1.4 Evolutionary Psychology in Criminology

As mentioned above, there is a lack of representation of evolutionary psychology within the traditional criminological literature. Studies completed by criminologists using an evolutionary psychology perspective are found in the literature; however these studies rarely appear in the criminological literature. In fact, with a few notable exceptions (Campbell,

Muncer, and Bibel, 1998; Ellis and Walsh, 1997; Walsh, 2000) the criminological literature is lacking any reference to evolutionary psychology. Additionally, the majority of studies conducted by criminologists tend to lack an empirical component, and instead have focused on the theoretical advances that could be gained from making use of evolutionary psychology approach (Barber, 2007; Ellis, 1987, 2003; Ellis and Walsh, 1997; Kanazawa and Still, 2000;

Rowe, 2002; Walsh, 2003, 2006; but see Beaver et al., 2008 and Nedelec and Beaver, 2012).

While these theoretical postulates represent a necessary step in the vertical integration of evolutionary psychology into criminology, empirical testing is required in order to provide further evidence of the paradigm’s value to criminologists. Therefore, the current project represents an empirical analysis derived from the postulates of evolutionary psychology and behavioral genetics. Towards this end, the evolutionary perspective life history theory is incorporated into the project to further situate the analyses within the relevant literature and theoretical boundaries. 25

2.2 Life History Theory

Life history theory is a perspective from evolutionary biology which predicts differential species- and individual-level strategies relating to maturation, sexual behaviors, and based on both the evolutionary history of the species and the stability or predictability of the contemporaneous environment (Chisholm, 1993; Figueredo et al., 2005, 2006; Figueredo,

Cabeza de Baca, and Woodley, 2012). According to life history theory, between-species variation in strategies relating to survival and reproduction can be placed on a continuum from

‘slow’ to ‘fast’ (Brumbach, Figueredo, and Ellis, 2009). This continuum has also been referred to as the r/K theory, where r-selected species tend to expend more energy on mating effort than parenting effort and high fecundity, matching a ‘fast’ reproductive strategy; whereas, K-selected species tend towards a reproductive strategy characterized by high parental investment and low fecundity (Boutwell et al., 2013; Ellis and Walsh, 1997; Rushton, 2004). Species with a slow strategy (i.e., K-selected) evolved under conditions of relative stability and focus on high levels of parental investment towards a small number of offspring; whereas, species with a fast strategy

(i.e., r-selected) evolved in unstable conditions and focus on high levels of mating efforts and reduced levels of parental investment (Figueredo et al., 2005). While these life history strategies are species-typical, individual organisms (particularly humans) adjust their strategies in accordance with the current conditions of their environments (Brumbach et al., 2009; Dunkel,

Mathes, and Beaver, 2013). This phenotypic plasticity (Belsky, Steinberg, and Draper, 1991) manifests as differential strategies employed by individuals in numerous aspects of their life, including the timing of onset of sexual behaviors, number of sex partners, parenting, and antisocial behavior (Brumbach et al., 2009; Wilson and Daly, 1997).

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A key component of the r/K theory is that individuals within a species will exhibit variation in terms of engaging in a suite of behaviors that represent a more fast (r) or slow (K) life history strategy (Dunkel et al., 2013; Figueredo et al., 2012). Therefore, while most humans will exhibit a K-selected reproductive strategy some individuals will engage in behaviors that represent movement towards an r-selected repertoire (Rushton, 2000, 2004). Recognition of the potential for variance led Rushton (1985) to term his life history theory-based ideas ‘differential

K’. This nomenclature is more appropriate than ‘r/K’ when applied to analyses of human behavior as evolution has pushed humans far towards the K end of the r/K continuum. However, this also highlights the poignancy of differential K selection as individuals who deviate from the species-typical reproductive strategy will likely exhibit a wide range of consequences resulting from such variation (Figueredo et al., 2012). In other words, while life history theory begins, in a manner akin to evolutionary psychology, with an analysis of the species as a whole, it progresses towards an analysis of individual differences and how those individual differences can be situated within the larger analyses (Kaplan and Gangestand, 2005).

2.2.1 Empirical Research on Life History Theory

Research including life history theory within the social sciences has increased considerably over the past two decades (Figueredo et al., 2012). Empirical analyses assessing the validity of the conceptual framework of life history theory has shown considerable support for the perspective (Brumbach et al., 2009; Dunkel and Decker, 2010; Figueredo et al., 2006;

Figueredo et al., 2012). In addition to construct validity analyses, the empirical literature has illustrated the various connections between variance in life history strategy and outcomes such as health-related measures (Ellis, 1987; Rushton, 2000, 2004), relationship measures (Olderbak and

Figueredo, 2010), personality (Figueredo, Vásquez, Brumbach, and Schneider, 2007; Gladden, 27

Figueredo, and Jacobs, 2009), risk-taking propensities (Figueredo et al., 2005; Wilson and Daly,

1997), and antisocial behaviors (Daly and Wilson, 2001; Dunkel et al., 2013; Dunkel, Mathes, and Papini, 2010; Ellis, 1987; Ellis et al., 2012; Rowe, Vazsonyi, and Figueredo, 1997; Rushton,

1989; Rushton and Templer, 2009; Wenner, Bianchi, Figueredo, Rushton, and Jacobs, 2013;

Wilson and Daly, 1997). Elaboration of the last observed association deserves further attention.

In order to illustrate how life history theory could relate to criminal behavior, the poignant example provided by Wilson and Daly (1997) will be discussed. The authors begin by illustrating the illuminative properties of an evolutionary point of view. They indicate that rather than consider behavior which exhibits characteristics of a discounted future (e.g., impulsivity or low self-control) as deleterious, researchers ought to think of such behavior as a reasonable or rational “response to information that indicates an uncertain or low probability of surviving to reap delayed benefits . . . and [recognize that] risk taking can be optimal when the expected profits from safer courses of action are negligible” (Wilson and Daly, 1997: p.1271). When viewed from an evolutionary point of view (one that falls in line with life history theory) impulsive or risky behaviors can therefore be seen as potentially adaptive responses.

Importantly, an evolutionary perspective recognizes that such a constellation of behavioral responses may have been adaptive in an ancestral environment but may or may not still be adaptive in modern societies (e.g., due to such factors as incarceration).

Wilson and Daly (1997) analyzed 77 neighborhoods within Chicago over a range of five years (1988-1993) and included information on life expectancies for males and females, homicide rates, median household income, and an index of income inequality. Importantly, this collection of information was obtained for each neighborhood in the analysis. The authors found two important findings related to life history (and directly relevant to the current study). First,

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the authors found that life expectancy at birth for males was correlated with homicide rates.

Statistically speaking, the magnitude of the correlation was very strong (r = -0.88) and indicated that those areas with a shorter life expectancy for males also had much higher homicide rates.

The observed association was similar for females as well (r = -0.83). The authors also employed multivariate analyses to further assess the relationship after controlling for income and income inequality. The results of the multivariate models indicated a similar relationship as the bivariate models: areas with a shorter life expectancy for males also possessed a higher homicide rate (the relationship did not hold for females in the multivariate models).7

To further illustrate the relationship between life expectancy and homicide rates the authors compared mortality patterns in the top 10 and bottom 10 neighborhoods in terms of life expectancy. The differences were striking: across all age and sex categories (i.e., for both males and females) those neighborhoods with the lowest life expectancy also had the highest mortality rates (i.e., internal mortality, homicide mortality, and non-homicide mortality; see Figure 2 of

Wilson and Daly, 1997). Viewed from an evolutionary point of view, these results indicate that individuals (particularly males) may respond in an unconscious manner to indications of short life expectancy in such a way as to increase their involvement in risky behaviors, including serious violent behaviors. This reasoning falls in line with the above discussion regarding intrasexual competition wherein males will increase their intensity of competitive behaviors to obtain mating opportunities when the likelihood of failure is increased or when the likelihood of a payoff from delayed competition is decreased (Buss, 2003).

7 The findings also revealed that income inequality was significantly associated with homicide rates, beyond that accounted for by life expectancy. However, the magnitude of the effect size was much smaller for income inequality (β = 0.19) than life expectancy (β = -0.74) in the multivariate model. The authors conclude, however, that both variables are of crucial importance in determining the reactionary behavioral strategy of individuals in such socioecological milieus. 29

The second outcome of interest investigated by Wilson and Daly (1997) was age-specific birth rates. The authors reasoned, if life expectancy indicates that one’s probability of reproducing successfully is low if one waits until adulthood then there should be an increased proportion of younger parents in areas with reduced life expectancy. The reasoning emanates from an evolutionary point of view which recognizes the premium placed on reproductive success by natural and sexual selection (Dawkins, 2006; Wright, 1994). As recognized by

Dawkins (2006), those individuals who did not do absolutely everything they could to ensure the successful production and survival of shared genetic material were relegated to the evolutionary dustbin. In other words, they did not become our ancestors. The individuals who are our ancestors possessed the psychological, morphological, and behavioral phenotypes necessary for successful reproduction. Wilson and Daly (1997) argue that a psychological mechanism which unconsciously adjusts timing of reproduction in response to adverse environmental conditions may be one of those psychological adaptations. The results of their second analysis conform to this expectation. Specifically, the authors found that from the ages of 15 to 29 women in the 10 shortest life expectancy neighborhoods had much higher birth rates than women in the 10 longest life expectancy neighborhoods. Notably, the authors illustrate that the most drastic differences between the neighborhoods were observed in the teenage years. To conclude this observation,

Wilson and Daly (1997) provided the following interpretation (guided by past in line with their findings): “the relatively high birth rates in young women in the worst neighborhoods [reflected] a distinct family planning schedule rather than a mere absence of family planning”. In other words, the behavior may be reflective of the resulting effects of an evolved psychological mechanism rather than traits such as impulsivity or low self-control. In

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summary, the study conducted by Wilson and Daly (1997) succinctly illustrates the value and insightfulness of an evolutionary (life history) approach.8

In addition to linking life history strategies with a variety of outcome measures, including criminal behavior, the extant literature includes studies which have applied a behavioral genetic analysis.9 Two such studies have been conducted: Figueredo et al. (2004) and Figueredo and

Rushton (2009). The first study (Figueredo et al., 2004) included a multi-step process. First, the authors identified a single-order higher factor (via factor analyses) that pointed towards a general life history strategy (which they termed the ‘general K factor’ in reference to Rushton’s [2000,

2004] differential-K theory). Second, the authors estimated the effects of genetic factors

(; h2) on the constituent components of the general K factor and the general K factor itself. With few exceptions, the magnitude of the genetic effect on the constituent items ranged between h2 = .30 and h2 = .50 (full range: h2 = .00 to h2 = .65). Additionally, the general K factor was found to be influenced by genetic factors with a high magnitude estimate of heritability (h2 =

.65). In other words, the authors found that 65 percent of the variation in the general K factor (a life history strategy) was due to variation in genetic factors. The third step completed in the study was the assessment of the influence of genetic factors on the covariation between the general K factor and two outcomes related to overall well-being (what the authors termed

‘covitality’) and personality. The results of this bivariate genetic analysis illustrated that the genetic factors accounted for 69 percent of the covariation between the general K factor and the

8 Of note is a portion of the authors’ conclusion wherein they emphasize the damaging feedback effects of such a dynamic as that illustrated in their results. They state, “[t]he number of likely feedback loops among the phenomena of interest if daunting. If many people react to a local socioecological milieu by discounting the future and lowering their thresholds for risk and violence, the behavioral consequences are likely to worsen the very problems that provoke them . . .” (Wilson and Daly, 1997: p. 1274). 9 See Chapter 3 for a detailed description of behavioral genetic methodologies.

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covitality factor and 78 percent of the covariation between the general K factor and the personality factor. In addition to illustrating this significant genetic effect on the variance and co-variance of these factors, the authors found that shared environmental effects accounted for none of the variation in the factors or the covariation between the factors (all estimates of c2 were zero).

The second study employing a behavioral genetic analysis of life history strategies was conducted by Figueredo and Rushton in 2009. The authors made use of the same sample as the previous study (i.e., the twin subsample of National Survey of Midlife Development in the

United States, MIDUS) but sought to assess the influence of nonadditive genetic effects on the general K factor life history strategy as well as a composite strategy called Super-K comprised of the general K factor, the covitality factor, and the personality factor. In brief, a nonadditive genetic effect (denoted as d2) refers to when the effect of one gene is altered by the presence of another gene (i.e., an interactive gene X gene effect; Plomin et al., 2013). The results showed that both the general K factor strategy (d2 = .31) and the Super-K (d2 = .38) factor strategy were influenced by nonadditive genetic effects. The authors concluded that the genetic effect on the variance in life history strategy may be in part due to the interaction between two or more genes.

In summary, these two studies illustrate two important points. First, they highlight the utility of integrating a behavioral genetic approach with an evolutionary framework. Second, they illustrate the considerable effect of genetic factors on variation in life history strategy and the relative minimal effect of shared environmental factors. These results were found both for univariate analyses (e.g., variation in the general K factor) and for bivariate analyses (e.g., the covariation between the general K strategy and personality).

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2.3 Summary and Discussion of Evolutionary Psychology and Life History Theory

Both evolutionary psychology and life history theory are concerned with the analysis of species-wide characteristics and how those characteristics have been selected over evolutionary time. While evolutionary psychology research tends to focus on how behavioral similarity arose across members of the human species there are evolutionary psychology researchers who increasingly test for individual differences (Hawley and Buss, 2011). However, the manner in which such research is conducted necessitates moving a step ‘below’ the approach of evolutionary psychology and employing mid-level theories like life history theory to assess individual differences (Figueredo et al., 2006). Furthermore, given that life history theory explicitly hypothesizes that individual differences in survival and reproductive strategies will arise due to both environmental and genetic influences a researcher is then able to incorporate a behavioral genetics approach. The discussion and literature review of life history theory illustrated this type of dynamic. Therefore, with the theoretical scaffolding now provided via evolutionary psychology and life history theory we now move to a discussion of the methods of a biosocial approach, with a particular emphasis on behavioral genetics. As a reminder, the discussion of the methods of behavioral genetics is intentionally detailed in order to illuminate both its application to the theoretical scaffold provided in the current study and its importance to advancing criminological research and theory overall. Consequently, the discussion of the methods of biosocial criminology is preceded by a brief critique of the dominant methodologies of current criminological research.

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CHAPTER 3

BIOSOCIAL CRIMINOLOGY: AN INTRODUCTION

It is far better to grasp the Universe as it really is than to persist in delusion, however satisfying and reassuring.

-Carl Sagan (1996: p.12)

A student of criminology is presented with a variety of explanations for criminal behavior over the course of their academic career. Invariably, textbooks for the courses covering these explanations contain primarily sociological explanations with a chapter or two covering basic research methodology, statistics, and theory construction. Contained in these textbooks, typically by the third chapter, is also coverage of how biology has been employed in criminological analyses of the past (often housed within the discussion of the positivist school of criminology). This coverage is so consistent in textbooks of criminology it is as if the various authors all follow a similar script: they begin with Lombrosso’s research, recognize the naiveté of phrenology, they touch on the eugenics movement, cover the hype surrounding the XYY phenotype, and often end with a brief mention of the advances of neuroscience and genetics.

The final comments in such chapters are also remarkably similar in tone and content: they point towards a lack of scientific rigor among biology in criminology (e.g., undue generalizations based on small, select samples), they bemoan the manner in which biological research completely ignores the influence of sociological factors, they indicate how biological explanations can only lead to racist and sexist policies, and they provide the inevitable accusations of reductionism and biological determinism. Students of criminology are left with the impression that biology has minimal place within criminology, that any policy based on 34

biology is racist and/or sexist, and that criminology is best served by focusing solely on sociological variables. Despite the increased integration of recent biosocial research efforts, the coverage outlined above can still be found in current texts (e.g., Curran and Renzetti, 2001).

This lack of recognition of the way in which biological research in criminology has advanced has garnered much frustration among current biosocial criminologists and resulted in stagnation in the advancement of students of criminology (Wright and Cullen, 2012). This section of the current study will outline the assumptions and methods of current biosocial criminology and illustrate why the typical coverage of biology in criminology texts and courses is inaccurate and unproductive. Additionally, this chapter will illustrate how a biosocial approach to the study of human behavior can help advance criminology as a discipline and science and move the field beyond the delusion of a non-biological influence on behavior.

In order to best understand and highlight the utility of a biosocial approach, it is necessary to juxtapose the approach to methodologies employed by mainstream criminology.

The collection of techniques found within mainstream criminology has come to be called the standard social science method or SSSM (or SSSMs for the plural ‘standard social science methodologies’; Beaver, 2009; Wright and Boisvert, 2009).10 The SSSM is characterized primarily in terms of its inability to assess the influence of genetic factors. When seen from a philosophy of science point of view, the inability to account for genetic factors is not surprising.

Given that the ideology of sociology, and the majority of criminology, places the explanatory weight of the etiology of human behavior with social causes external to the individual, there is no perceived need to account for genetic factors (Walsh, 2011). Indeed, even though a great deal

10 The SSSMs are also referred to as the standard social science model (see Walsh, 2011). Regardless of the employed nomenclature, the methods used in the approach are similar in that they are not able to account for the influence of genetic factors. 35

of criminological research assesses how individuals vary in their propensity to crime due to latent traits or personality characteristics (e.g., Gottfredson and Hirschi, 1990) they still place the cause(s) of the development of those propensities solely within the bounds of external forces.

An example will help to illustrate this line of thought. When testing a propensity-based theory such as Gottfredson and Hirschi’s (1990) general theory of crime, a researcher employing the

SSSM will collect data from a sample of target households. The individuals sampled to provide the data in each household typically consist of a mother and one of her children. Again, this is no surprise given that Gottfredson and Hirschi (1990) argue that the development of self-control in a child is due to the socialization practices (or malpractices in the case of low self-control) of a parent. Therefore, the ideology of the sociological paradigm directs both theory construction

(i.e., low self-control is caused by poor parental efforts – an external force) and theory testing

(i.e., we need only gather data from a parent and one child to test the theory’s hypotheses). In analyzing the data, the SSSM researcher attributes any observed association between parenting practices and behavioral outcomes in the child as a causal association. This approach is common not only in mainstream criminology but also in disciplines such as social and developmental psychology (Harris, 2009).

The main problem with the approach taken by the SSSM, and with the sociological paradigm in general, is that the method does not allow for the assessment of spuriousness due to shared genetic factors between parent and child (Beaver, 2009; Harris, 2009; Rowe, 1994). This problem is sufficient to raise serious questions about SSSMs based on logic alone, but also in the statistical sense: conclusions derived from an SSSM will be systematically biased to the extent that shared genetic factors can account for the observed association (Rowe, 1994). Given that reviews consistently find genetic effects on a wide swath of behavioral, personality, and

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attitudinal outcomes it is therefore plausible that any study based on the SSSM is misspecified and the conclusions suspect (Beaver, 2009; Plomin, 1990; Plomin, DeFries, Knopik, and

Neiderhiser, 2013). Returning to Gottfredson and Hirschi’s (1990) general theory can illuminate this point. While Gottfredson and Hirschi (1990) argue that poor parenting practices primarily bring about reductions in a child’s ability to control impulsive and selfish behaviors it is likely that shared genetic factors could affect both the parent and the child. In other words, the same genetic factors that could increase the likelihood of a parent failing to monitor his/her child, failing to recognize the child’s misbehavior, and failing to correct the child’s misbehavior may be the same genetic factors that are passed on to his/her child which manifest in reductions of self-control (Beaver and Wright, 2005; Rowe, 1994). A possible retort to this argument could be that one has to first illustrate that genetic factors influence parenting and also influence levels of self-control (which they do, see below). However, the retort is immaterial for our purposes here as the SSSM is ill-equipped to assess any genetic influence on any phenotypic outcomes.

In addition to the potential for spuriousness due to genetic factors acting as exogenous variables, the SSSM also fails to recognize the possibility that the causal arrow in socialization is not a proverbial one-way street. The majority of socialization research, including criminological research testing Gottfredson and Hirschi (1990), contends that variance in parenting practices causes variance in behavioral and personality outcomes in children (Beaver, 2009; Harris, 1995,

2009). The assumption is that the causal connection flows only from parent to child. However, researchers employing genetically sensitive research designs have shown that the arrow can go both ways; i.e., variance in the behaviors and attitudes of the child can elicit variance in parenting styles (Caspi et al., 2004; Harris, 1995; Rowe, 1994). Additionally, researchers note that the variance in childhood behavior, which is eliciting differential parenting practices, may be

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due in part to genetic factors (Rowe, 1994). Therefore, conclusions in studies based on a SSSM will be misguided due to the inability of the approach to account for the multitude of ways in which genetic factors may be influencing observed associations.

Adhering to the assumptions and practices of the SSSM is particularly problematic for criminology. Unlike numerous academic disciplines, criminology possesses a tangible connection to the practical world via its influence on public policy (Wilson, 2011). Therefore, generating and testing theories under the umbrella of a methodology which may be fraught with logical and statistical deficiencies not only renders these theories and tests highly suspect but may result in ineffective public policy. Consequently, there are not only academic/scholarly reasons to adopt an approach that does not rest on spuriousness but ‘real-world’ reasons to do so as well.

The discussion thus far has highlighted the ineptitude of the SSSMs as emanating from an inability to account for genetic factors. This position rests on the foundation that genetic factors are indeed important in the etiology of human behavior (and in reference to the current study, antisocial and sexual behavior in particular). Therefore, it is necessary to illustrate the links between genetic factors and various types of behavioral phenotypes. The primary method by which a biosocial approach has illuminated such links is via the use of a behavioral genetic research approach. Consequently, the next section will provide a detailed account of the concepts, issues, and methods encapsulated within behavioral genetic research.

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3.1 Behavior Genetic Research Methods: An Overview

Stated briefly, the behavioral genetic approach entails the application of statistical methods to the study of human phenotypes (Walsh, 2009).11 The statistical methods are referred to as quantitative genetics and are concerned with assessing the relative influence of genetic factors and environmental factors on the observed variance in behavioral, attitudinal, or personality phenotypes (Evans, Gillespie, and Martin, 2002; Neale, 2009; Plomin et al, 2013;

Udry, 1995; Walsh, 2009). A phenotype is simply an observed trait or underlying (latent) trait that can be measured in some way. A phenotype can range from outright behaviors (e.g., violent aggression) to latent personality traits (e.g., impulsivity). Individuals vary in their possession and expression of certain phenotypes and behavior genetics research is interested in evaluating the relative influence of genetic and environmental factors that impact phenotypic variation

(Plomin et al., 2013; Udry, 1995). There are a number of methods employed by behavior genetic researchers and each method contains its own assumptions and data requirements. Before discussing these methods in detail it is necessary to examine more fully the practice of assessing the relative influence of genetic and environmental factors.

Recognition of variation in the possession and expression of particular traits (i.e., individual differences) is not a new observation and is not unique to behavioral genetics research. Indeed, criminology is comprised of a number of theoretical perspectives that focus on individual differences as important etiological factors (e.g., Agnew, 1992, 2001; Akers, 1996;

Gottfredson and Hirschi, 1990; Moffitt, 1993). However, as outlined above researchers in criminology have typically employed a SSSM approach and therefore have not recognized or

11 Throughout this project the phrases “behavior genetics” and “behavioral genetics” will be used interchangeably. Both labels are used in the literature and each refers to the same perspective. 39

tested the influence of genetic factors in analyzing individual differences. In addition to lacking an integration of genetic factors the SSSM approach confounds different types of environmental influences in its tests of theoretical assumptions and hypotheses (Beaver, 2009). Behavioral genetics research recognizes that there are two types of environmental influences that can have differential effects on phenotypic variation. The following discussion outlines how these types of environmental influences are distinguished and how the relative effect of genetic and environmental factors can be assessed.

In behavioral genetics research individual differences in the possession and/or expression of traits (i.e., phenotypic variation) is statistically decomposed into genetic and environmental components. The genetic and environmental components are represented by the concepts heritability, the shared environment, and the nonshared environment (Plomin et al., 2013). These latent components are characterized by the symbols h2 (heritability), c2 (shared environment), and e2 (nonshared environment; Plomin et al., 2013). Taken together, these three components account for 100 percent of the variance in a phenotype. Equation 2.1, herein referred to as the equation of phenotypic variance, illustrates this argument.

p = h2 + c2 + e2 (3.1)

In Equation 2.1 p is a generic term referring to any measured phenotype, and as the equation illustrates all of the variance in p is due to the combination of both genetic factors (h2) and environmental factors (c2and e2).12 Given that the all of the components taken together account for 100 percent of the phenotypic variance the components are expressed as proportions that

12 Note that throughout this project genetic, shared environment, and nonshared environmental effects will be denoted as h2, c2, and e2, respectively. However, these notations have specific statistic relevance and meaning in the behavioral genetics literature and should not be confused as indications of those statistical meanings across all contexts as used herein. The symbols are simply used herein as a convenient way in which to denote the three components of the equation of phenotypic variance. For a detailed review of the statistical methods underlying behavioral genetics research, see Neale (2009) and Neale and Mays (1992) as well as Plomin et al. (2013). 40

range from .00 to 1.0. For ease of interpretation, the components can be converted to a percentage representing the percentage of the phenotypic variance accounted for by the individual component (Beaver, 2009). How these components are estimated in behavioral genetics research will be outlined in later sections, but first the different components require further elaboration.

The heritability component (h2) is a measure of the proportion of phenotypic variance that is due to, or accounted for, by genetic variance (Beaver, 2009). The key words in the definition of heritability are ‘genetic variance’. As Walsh (2002) notes, equating ‘heritability’ with ‘inherited’ can be problematic and lead to unnecessary confusion. The majority of human

DNA is invariant from person to person, thus most people look and act in similar fashions across the globe; indeed human beings share approximately 99.9% of their DNA (Plomin et al., 2013).

The shared DNA that generates fixed traits across most members of the species are inherited by those members from their ancestors (Walsh, 2002). However, the remaining 0.1% that varies between individuals can cause considerable differences in personality, behavior, and even physical traits. This 0.1%, referred to as ‘distinguishing DNA’, varies from person to person and it is from this variance that heritability estimates are derived (Beaver, 2009). Therefore, if a trait is influenced by variance in genetic factors the heritability component will tap into this genetic variance and provide an estimate of the total amount of phenotypic variance that is due to variance in genetic factors. In other words, h2 provides us with an estimate of the variance in a phenotype that is accounted for by the effects of genetic factors (Plomin et al., 2013).

As a result of the manner in which the equation of phenotypic variance is calculated, heritability is expressed as a proportion (i.e., the proportion of variance explained by h2).

Consequently, estimates of heritability range from .00 to 1.0. An estimate of 1.0 would indicate

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that all of the phenotypic variance is due to heritability (i.e., genetic factors/variance). An estimate of .00 would therefore mean that a negligible proportion of the phenotypic variance can be attributed to genetic factors. As noted above, estimates of h2 can be converted to a percentage to illustrate the percentage of phenotypic variance that can be accounted for by genetic factors.

For example, a heritability estimate of .35 could be read as indicating that 35 percent of the variance in the phenotype is due to differences (i.e., variance) in genetic factors. It is worth noting that heritability is a statistic that is derived from the assessment of phenotypic variance within a population under study (Plomin et al., 2013; Walsh, 2002). Consequently, estimates of heritability cannot be applied to individual phenotypic variance as it only applies to variance at the group level (Beaver, 2009). Additionally, estimates of heritability are not static across populations or over time as the influence of genetic factors can vary with the differential influence of both the environment and other genetic effects (Beaver, 2009; Walsh, 2002).

Behavioral genetic researchers distinguish between genetic and non-genetic influences on phenotypes. Further, as mentioned, they recognize that there are differences in the effects of non-genetic factors. Behavioral genetics research demarcates non-genetic influences into those factors which serve to make siblings more alike and those non-genetic factors that make siblings less alike. The next component in the equation of phenotypic variance is the shared environmental component, represented by the symbol c2. The shared environmental component refers to those non-genetic factors which serve to make siblings more similar (Rowe, 1994). As a reminder, the focus of the similarity is on the phenotype under study and not in terms of behavior or personality overall. There are three characteristics that must be met in order for an environmental factor to be considered as a shared environmental component. First, the factor must be non-genetic; second, the factor must have an influential (i.e., statistically significant)

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effect on the phenotype under study; and third, the factor must be experienced by both persons in a kinship pair (Beaver, 2009). The most often employed example of the shared environment is the parenting or rearing practices employed by parents. To the extent that both siblings

(individuals in a kinship pair) experience similar rearing styles from their parents or caregivers, the rearing style can be said to be an element of the shared environment. As with h2, c2 is an estimate of the effect on phenotypic variance and can vary across time and place.

The final component of the equation of phenotypic variance is the nonshared environment (e2). Factors that fall within the nonshared environment are those factors which serve to make siblings different from one another. In order for a factor to be considered a component of the nonshared environment, three characteristics must be met. First, the factor must be non-genetic; second, the factor must have an influential effect on the phenotype under study; and third, the factor must work to make siblings more dissimilar. Therefore, components considered to be ‘environmental’ can also be non-genetic biological influences. In other words, the nonshared environment can include biological factors that are not shared between siblings and are not genetic in origin (Plomin et al., 2013). For example, if one twin experiences a brain injury which impacts his or her cognitive functioning, the injury is considered a component of the nonshared environmental effect on such functioning. Other examples of the nonshared environment include different peer groups, differential levels of alcohol or drug consumption, differential experiences of parenting practices, and even different prenatal environments (Beaver,

2009; Caspi et al., 2004; Walsh, 2011). Behavioral genetic researchers have also noted that nonshared environmental factors can be a result of the differential subjective interpretations of experiences that both siblings share (Turkheimer and Waldron, 2000). Importantly, encapsulated

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within e2 is random measurement error which, in addition to the three components of the equation allows for the estimation of 100 percent of the variance in the phenotype under study.

With a better understanding of the components of the equation of phenotypic variance, the discussion can now focus on the various methodologies employed by behavioral genetic researchers to estimate the relative effect of h2, c2, and e2. Therefore, the next section of the chapter will outline behavior genetics methodological designs. Crucially, each of these designs is able to address the serious methodological flaw of the SSSM: the inability to account for the effect of genetic factors on the variance of a phenotype.

3.1.1 Methods for Estimating h2, c2, and e2

Decomposing phenotypic variance into an estimate of heritability, shared environment, and nonshared environment requires that a specific type of sampling method be employed.

While SSSMs sample only one child per household, a genetically sensitive design typically employs a method wherein two children per household are sampled (Beaver, 2009). Elaboration of a genetically sensitive design will illuminate why this type of sampling is necessary. Recall that a typical SSSM assumes that any statistical association between a variable like parenting practices and an outcome like childhood levels of impulsivity is viewed as causal (i.e., variance in parenting practices cause variance in childhood levels of impulsivity – this is referred to as the socialization hypothesis or the so-called nurture assumption; Harris, 2009). However, the problem with this assumption is that the parent and the child share 50 percent of their genetic material and it may be the case that portions of that shared genetic material may be influencing the parenting styles of the parent and levels of impulsivity in the child. Therefore, a spurious relationship may exist. Indeed, this is what behavioral genetic research has revealed (Harris,

2009; Rowe, 1994). Therefore, a research design must take into account the effects of the shared 44

genetic material. Consequently, there are two components of a genetically sensitive research design that are necessary in order to assess such an influence. First, two people from a kinship pair per household must be included in a sample.13 Second, there must be a measure of the genetic relatedness between the two individuals. Once these sampling requirements are met there must are two data requirements which must also be included. First, there must be an indication that two respondents in a sample are from the same household (this is usually represented by a family identification number); and second, there must be a measure of the genetic relatedness between the two members of a kinship pair (Beaver, 2009). How these variables are employed in behavioral genetic research will be outlined below, for now it is necessary to understand the methodological requirements of a genetically sensitive design.

These requirements are satisfied in various ways in the behavioral genetics literature, but the most common method employed is the method. The next section outlines this method as well as the assumptions, potential limitations, and responses to these limitations that have been highlighted in the relevant literature.

3.1.2 Twin Studies

The use of twins in empirical research assessing the etiologies of health-related and behavioral outcomes has a relatively long history (Segal, 2010). Indeed, twins have been employed in the study of criminality since at least the early 1900s (Wilson and Herrnstein,

1985). The key factor in the use of twins is that twin sets can be differentiated based on their genetic relatedness. Monozygotic (MZ) twins are genetically identical due to fact that they are

13 A ‘kinship pair’ refers to two individuals who share a proportion of their distinguishing DNA (i.e., members of the same family). In behavioral genetics a kinship pair is typically comprised of a set of twins, however a kinship pair can be comprised of any two related individuals (e.g., full siblings, half-siblings, cousins, etc.). 45

the result of the separation into two embryos of one egg fertilized by one sperm.14 Therefore,

MZ twins share 100 percent of their distinguishing DNA. Dizygotic (DZ) twins, in contrast, are not genetically identical and share approximately (i.e., on average) 50 percent of their distinguishing DNA. DZ twins are the result of two different eggs being fertilized by two different sperm during the same act of conception. Therefore, DZ twins are no more genetically similar than full siblings who are the result of different pregnancies. Importantly, MZ twins are always the same sex within a twin set while DZ twins can be either the same or different sex within a twin set. This differentiation based on sex can have implications for statistical analyses using twins, as outlined in the Methods section below (see also Neale, 2009).

Twins are so often employed in behavioral genetic research because comparisons of phenotypic possession and expression between MZ and DZ twins represents an approximation to a controlled experiment for gaining an estimate of the influence of genetic factors on a phenotype (Wilson and Herrnstein, 1985). Consequently, the difference between MZ and DZ twins can be seen as a ‘natural experiment’ wherein manipulation of genetic relatedness, or zygosity, is not done by the researcher but occurs naturally. Knowing the zygosity of the twin set then allows the researcher to assess the association of zygosity with any observable phenotype. This type of method (also referred to as the comparative method) is employed in a number of other academic and professional arenas and has produced a considerable knowledge base for the medical, biological, and behavioral sciences (Diamond and Robinson, 2010).

The twin method relies on the difference in zygosity between MZ and DZ twins to provide estimates of h2, c2, and e2. The underlying logic is fairly straightforward. Given that

14 The reasons for why this splitting occurs is yet unknown. Gleeson, Clark, and Dugatkin (1994) provide an interesting hypothesis indicating that twinning could be the result of a genetic influence that is differentially expressed given different evolutionary-relevant environmental pressures. 46

both twins within an MZ or DZ twin set are born at the same time, reared by the same caregivers, attend the same schools, and so forth they are said to share the same developmental environments. This initial assumption is referred to as the equal environment assumption (or

EEA) and will be further addressed later. Given that an equal environment can only serve to make the two twins within a twin set more alike any differences between the twins within a twin set must be due to something else. Therefore, if MZ twins are more similar (i.e., concordance) on a phenotype of interest than DZ twins it can be assumed that the congruence is due to the greater similarity in genetic relatedness (recall MZ twins share 100 percent of their DNA while

DZ twins share about 50 percent of their distinguishing DNA). An example will help illustrate this logic. The best example to use is something that is easily observed (i.e., measured), like body weight. Often this phenotype is measured as a ratio of height to weight and is expressed as a person’s body-mass index (BMI). Researchers interested in assessing if genetic factors influence BMI could employ the twin study method. First, a researcher would include in his sample a collection of MZ and DZ twins, appropriately differentiated by zygosity. Second, the researcher would observe (i.e., measure) the BMI for each person in the sample. This would provide an individual measure of BMI for each respondent. Third, the researcher would then produce a measure of the association among the different twin sets. This measure would provide an indication of how similar each twin is to his or her co-twin and an overall account of how similar MZ twins are as a group and how similar DZ twins are as a group. Finally, the researcher would then compare the similarity between the two groups. If the MZ twins are more concordant in terms of their BMI compared to the DZ twins, then the researcher can conclude that BMI is likely influenced by genetic factors.

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The measure of association, or covariance, produced in twin studies is the correlation coefficient (symbolized as r). The correlation coefficient provides an indication of the direction and strength, or degree, of an observed association between two variables (Allison, 1999). The correlation coefficient ranges from -1.00 to 1.00, where an r of -1.00 indicates a perfect negative relationship between the two variables and an r of 1.00 indicates a perfect positive relationship.

Obtaining a perfect correlation, either negative or positive, is rare and therefore researchers assess the strength of an association as weak, moderate, or strong depending on how far from zero (which indicates no association) the value of the correlation coefficient happens to be

(Warner, 2008).

The correlation coefficient is often employed in twin studies. The typical first statistic presented in a twin study is referred to as the cross-twin correlation and it is based on the correlation coefficient. The cross-twin correlation, just like the correlation coefficient, measures the strength and direction of the relationship between two variables. The difference from the usual interclass correlation coefficient presented in many social science studies, is that the cross- twin correlation is an intraclass correlation where the score of one twin on a phenotype of interest is compared to the score of the co-twin (i.e., their sibling) within the twin set (Beaver,

2009). The cross-twin correlation provides an indication of the level of covariance between the twins within a twin set and is therefore an estimation of the proportion of phenotypic variance that is shared within twin pairs (Jensen, 1971; Neale, 2009). Therefore, a cross-twin correlation coefficient of 1.00 would indicate that the two twins are identical on the phenotype of interest, where a coefficient of .00 would indicate that they are not at all similar on the phenotype. Cross- twin correlations are primarily employed as a way of comparing the two types of twin sets.

Therefore, behavior genetic researchers produce a cross-twin correlation coefficient for MZ

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twins (symbolized as rMZ) and a separate coefficient for DZ twins (symbolized as rDZ). This step then allows the researcher to compare the covariance on a phenotype among MZ twins to the covariance on a phenotype to DZ twins. Recall the underlying logic of the twin study holds that any greater within-twin similarity observed among MZ twins, relative to DZ twins, is an indication of genetic effects on the phenotype. Therefore, if rMZ is greater than rDZ it provides initial evidence that genetic factors likely have an influence on the possession and/or expression of the phenotype of interest.

Turning to the BMI example once more will help illustrate the above points about cross- twin correlations. Recall that the researcher obtained a measure of the similarity among the two groups of twin sets and that measure we know now is termed the cross-twin (or intraclass) correlation coefficient. For illustration’s purpose we will assume that the researcher obtained the following results: rMZ = .70 and rDZ = .25 (these are fictitious estimates). There are three conclusions that can be made from these results. First, 70 percent of the variance in BMI is shared amongst MZ twins. Second, 25 percent of the variance in BMI is shared amongst DZ twins. Finally, given that rMZ is greater than rDZ the researcher can conclude that genetic factors likely have a strong influence on the variance in BMI scores.

While the cross-twin correlation coefficient can provide initial evidence of the influence of genetic factors it is limited in its ability to differentiate genetic and environmental factors. In order to obtain an estimate of the relative influence of genetic and environmental factors, further calculations are required. Recall that the equation of phenotypic variance separates 100 percent of the variation in a phenotype into a heritability estimate (h2), a shared environment estimate

(c2), and a nonshared environment estimate (e2). The cross-twin correlation coefficients can be

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employed to generate each of these estimates. First, h2 can be estimated using the following formula:

h2 = 2(rMZ – rDZ) (3.2)

Equation 2.2 accomplishes two things: first, subtracting the covariation among DZ twins from the covariation among MZ twins results in the removal of the phenotypic variance that is due to shared environmental factors and half of the variance due to shared genetic factors. Second, because the first step removes half of the variance due to genes (as a result of the differential level of shared genes between MZ and DZ twins) the resulting difference must be multiplied by

2 (Beaver, 2009; Walsh, 2002). The result of the calculations completed in Equation 2.2 provides an estimate of the proportion of variance in the phenotype of interest that is accounted for by genetic factors. Using the values outlined in the above example, we can easily calculate an estimate of heritability for BMI. Recall that the fictitious cross-twin correlations were rMZ =

.70 and rDZ = .25. Substituting those values into Equation 2.2 produces an estimate of heritability of h2 = .90. Therefore, we would conclude that 90 percent of the variance in BMI (in the sample collected) is due to genetic factors.

The cross-twin correlation coefficients can also be employed to provide an estimate of the variance in a phenotype that is due to the shared environment. In order to gain such an estimate, Equation 2.3 is calculated.

c2 = 2rDZ – rMZ (3.3)

As with Equation 2.2, there are two steps accomplished in Equation 2.3. First, the correlation coefficient for DZ twins is multiplied by 2. This step is once again completed in order to account for the differences in shared genetic material between DZ and MZ twins. This step renders the proportion of variance in the phenotype due to genetic factors equal for both types of

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twins (Beaver, 2009). The second step, subtracting the correlation coefficient for MZ twins from the product of the first step, provides an estimate of the proportion of variance in a phenotype that is due to the shared environment. Our BMI example will once again illustrate how this can be done. Substituting the values from above into Equation 2.3 results in an estimate of shared environmental influence on BMI of c2 = -.20 . In behavior genetic research estimates with negative values, as the one we have found here, are often constrained to a value of zero when interpreting the proportion of variance accounted for by the estimate that resulted in a negative value (Figueredo et al., 2004; Plomin et al., 2013). Therefore, we would conclude that the proportion of variance in BMI accounted for by the shared environment is zero.

The final component of the equation of phenotypic variance, the nonshared environment

(e2), can also be estimated using the cross-twin correlation coefficients. Recall that the nonshared environment refers to all non-genetic factors which serve to make siblings different from one another on a phenotype as well as any variance due to measurement error.

Additionally, recall that the three components of the equation of phenotypic variance accounts for 100 percent of the variance (or 1.0 prior to conversion to percentages). Therefore, any influence on the variance in a phenotype not captured by h2 or c2 is attributed to e2.

Consequently, the equation for the estimate of nonshared environmental influence is relatively simple, as illustrated in Equation 2.4.

e2 = 1 – (h2 + c2) (3.4)

Employing the values from our BMI example we can easily obtain an estimate of the variance in

BMI that is due to nonshared environmental factors; e2 = .10. Therefore, from the three simple equations we would conclude the following about BMI using our fictitious sample: variance in

BMI is due primarily to variance in genetic factors (h2 = .90), is not impacted by shared

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environmental factors (c2 = .00), and is modestly influenced by nonshared environmental factors

(e2 = .10).

Although twin-based research designs are the most popular method employed in behavioral genetic research the equations outlined above have been replaced with more sophisticated computer algorithms which provide more accurate estimates (Beaver, 2009).

However, the logic which underpins these contemporary methods is similar to the logic underlying the equations above. Additionally, while the methods employed in the current study are based on the more sophisticated methodology it is necessary to understand the logic of these simpler techniques to fully comprehend the more advanced methods.

While twin-based designs are widely employed in behavioral genetic research there has been some critiques levied against the design. These critiques have now become recognized as implicit assumptions of the twin-based methodology and, as we shall see, are no longer considered statistical or methodological threats to the conclusions derived from behavioral genetic research (Walsh, 2002). The three main assumptions will be discussed in the following sections – the equal environment assumption (EEA), the issue of assortative mating, and the issue of zygosity determination.

3.1.3 The Equal Environment Assumption in Twin Studies

As mentioned above, twin studies are employed as a way to ascertain the etiologies of the similarities and differences between MZ and DZ twins in the variance of a phenotype of interest.

The differences are attributed to both nonshared environmental factors and nonshared genetic material while the similarities are attributed to shared environmental factors. If it is observed that a phenotype is more concordant (similar) between MZ twins than it is in DZ twins behavioral genetics methods attribute that similarity to the greater amount of shared genetic 52

material between the MZ twins, relative to the DZ twins. However, there is a possibility that MZ twins are more similar not only because of their shared genetic material but because of being treated more similarly than DZ twins. The equal environment assumption (EEA) presumes this to not be the case. In other words, the EEA holds that similarity due to the environmental cause of being treated the same is no more salient for MZ twins than it is for DZ twins. If the assumption is violated, however, the effects of genetic factors can be over-estimated (Derks,

Dolan, and Boomsma, 2006; Plomin et al., 2013). Critics have argued that to the extent violation of the EEA results in an over-estimation of h2 the twin study method may be flawed.

Despite the sound logic of the EEA critique, there are three requirements that must occur in order for violation of the assumption to be of serious concern to the twin study method. First, in order for the EEA to be of concern it must be the case that MZ twins experience more similar environments than DZ twins. Researchers have shown support for this contention. For example,

Loehlin and Nichols (1976) found that MZ twins share such environmental factors as peers, bedroom, and attire. Therefore, it appears that the first requirement for violation of the EEA in twin studies has been met. Second, in addition to its mere presence the environmental factor

(e.g., being treated more alike) must also work to generate greater phenotypic similarity for MZ twins than it does for DZ twins. Stated differently, in order for the environmental factor to be a concern when estimating the genetic influence on phenotypic variance the factor must have a lasting effect such that it causes MZ twins to appear to be more similar in phenotypic variance than the DZ twins appear. If the environmental factor generates equal phenotypic similarity between the two types of twins then the twin method will not result in an over-estimation of h2.

Finally, the third requirement for violation of the EEA is subsumed within the second requirement – i.e., the environmental factor must have a causal relationship with the phenotype

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of interest. In other words, in order for violation of the EEA to be of serious concern to researchers using the twin study method it must be the case that there is a non-spurious, statistically significant, empirically validated causal relationship between the environmental factor and the phenotype under study. This is a problematic situation for most social scientists.

This requirement holds that the way someone looks physically will have a determination on phenotypic outcomes such as personality and behavior. Similar arguments were presented by the man most demonized by contemporary criminologists and sociologists,

(Beaver, 2009).

Given that the violation of the EEA represents a logical, albeit difficult to justify, critique against the twin study method numerous researchers have empirically tested the assumption in a number of ways. The aforementioned study by Loehlin and Nichols (1976) assessed the correlations between scores on a differential treatment measure (i.e., comparing how MZ twins are treated relative to DZ twins) and a wide range of outcomes such as cognitive ability, vocational interests, personality traits, and interpersonal relationships. The authors found that the associations were very low and not outside of the boundaries of what would be expected by chance. Employing a similar method, other authors have also found that the EEA is a valid assumption for such outcomes as depression, anxiety disorder, behavioral problems, and other psychological disorders (Cronk et al., 2002; Derks et al., 2006). Another method of testing the

EEA is to assess if MZ twins who are treated differently express phenotypic variance due to the environmental factor of individualized treatment. Researchers have found that MZ twins who are treated in a more individualized fashion by their parents do not turn out to be more dissimilar than MZ twins who report being treated similarly (Plomin et al., 2013). Another interesting way to assess the EEA is to see if twins who had been misidentified as MZ twins, but were in fact DZ

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twins. In this case, if the EEA is not a valid assumption the DZ twins who, along with their parents, family, and peers, thought the twins were MZ twins would appear more similar than DZ twins who were not misidentified as MZ twins. Results of such studies show that these misidentified DZ twins are no more similar than correctly identified DZ twins (Reiss et al., 2000;

Scarr and Carter-Saltzmann, 1979). In a similar vein, a more recent and innovative study by

Nancy Segal (2012) showed consistent results. Employing a sample of unrelated look-alike individuals Segal (2012) assessed similarities in personality and self-esteem measures. Segal’s results (2012) revealed that there was little concordance between the unrelated look-alikes in her sample and concluded that the EEA is a valid assumption of the twin method. Consequently, the existing research indicates that while it may be the case that the first requirement of violating the

EEA exists (i.e., MZ twins share more similar environments than DZ twins) it does not appear that this observation alone is sufficient to render the twin study methods problematic.

It would appear, then, that the EEA is a valid assumption to make in studies employing the twin methodology and that levying the critique of an EEA violation is insufficient to draw doubt on the findings of such research. Interestingly, in the considerable testing that has gone into assessments of the EEA in the behavioral genetics literature researchers have noted how ubiquitous genetic factors appear to be. As a critique of twin studies, the EEA is assumed to rest on the influence of environmental factors on phenotypes in such a way that the causal arrow points one way. However, researchers have highlighted how genetic factors can elicit differential responses from a person’s environment (Harris, 2009; Rowe, 1994). Ironically, the critique that MZ twins are being treated more similarly actually may highlight, indirectly, how influential genetic factors are in the formation of phenotypic variance. In other words, assessments of the EEA (and other behavioral genetics research) have shown that the causal

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arrow can be reversed such that the child is influencing his/her environment more than the presumed initial relationship (Rowe, 1994). This type of interaction between a person and his/her environment is called ‘gene-environment interplay’ and will be described in more detail below.

3.1.4 Assortative Mating and Twin Studies

While most critiques against the twin method are levied by researchers attached to the

SSSM and are intended to reduce the scholastic attention to genetic factors, the assortative mating issue refers to recognition that researchers are likely underestimating the effect of heritability. Recall that the estimation of h2 is generated by assessing the differences between concordances within twin groups. The comparison rests on the assumption that DZ twins share half of the amount of DNA that MZ twins share. To the extent that this assumption is not correct, estimates of h2 will also be incorrect. A systematic human practice such as assortative mating may in fact be causing estimates of h2 to be incorrect but in a way that the estimates are more conservative than what may be the reality. Nonrandom mating is a result of eons of sexual and natural selection, and as a result people engage in mate choice based on specific traits

(Mealey, 2000). However, in addition to mate choice driven by evolutionary processes there appears to be phenotypic similarities amongst people who mate and some of those similarities are driven by genetic factors. Therefore, assortative mating is a process which refers to the selection of mates who possess similar personality, attitudinal, or behavioral traits (Redden and

Allison, 2006). Researchers have shown that couples co-vary on personality traits such as introversion-extroversion (Ahern et al., 1982), depressive symptoms (Dufouil and Alperovitch,

2000), BMI (Allison et al., 1996), systotic blood pressure (Speers et al., 1986), self-reported delinquency (Krueger et al., 1998), levels of self-control (Boutwell and Beaver, 2010), general 56

intelligence (Plomin et al., 2013), and antisocial behaviors (Boutwell, Beaver, and Barnes, 2012), among others (Plomin et al., 2013). Consequently, to the extent that these traits, and any phenotype shared between the mates, are influenced by variance in genetic factors any offspring that result from such assortative mating will have an increased likelihood of possessing a similar phenotype. Additionally, any full sibling sets produced from parents who have mated in an assortative fashion will be more genetically similar (in terms of the phenotype or phenotypes on which the parents based their mating decisions) than average. As a result, if DZ twins from such a mating were to be included in a twin study the assumption that they share only 50 percent of their distinguishing DNA would be incorrect as they likely share more than that amount due to the effects of assortative mating (Carey, 2003). Therefore, constraining the genetic relatedness to 50 percent (or .50) in the calculations of h2 will result in an underestimation of the effects of genetic factors on phenotypic variance. Given the strong evidence for assortative mating on a wide variety of traits, it is highly likely that many estimates of h2 are deflated as a result (Plomin et al., 2013).

3.1.5 Zygosity Determination in Twin Studies

Zygosity is a term which refers to the level of genetic relatedness between two individuals within a twin set and differentiates MZ twins from DZ twins. Given that the twin method relies on comparing the phenotypic variance between MZ and DZ twins, assessment of zygosity (i.e., twin group classification) is a key component of the research design. Therefore, the accuracy of the determination of zygosity can have an effect on the estimate of h2 for a phenotype of interest (Jackson, Snieder, Davis, and Treiber, 2001). Before discussing the type of problems that could arise with misidentification of twin type the next section describes the methods employed in twin-based research. 57

There are three main ways in which zygosity has been determined in the behavior genetic literature. In the mid-1950s Smith and Penrose (1955) illustrated that blood groups could be employed to determine zygosity. Decades later, Lykken (1978) and Wilson (1980) demonstrated that blood group analysis provided a very accurate manner by which to determine zygosity.

With the increased availability to employ DNA analyses in research, twin studies began to include DNA as a method of zygosity determination. This technique, unsurprisingly, resulted in highly precise determination of zygosity with almost 100 percent accuracy (see Becker, Busjahn,

Faulhaber, Bahring, and Robertson, 1997; Porrini et al., 1990). While these biologically based techniques provide substantial accuracy they are invasive, expensive, and often prohibitive in large samples. Consequently, a third technique has been employed with greater frequency: the use of physical similarity questionnaires (Jackson et al., 2001). While numerous studies have employed this technique, Jackson et al. (2001) provided a comparison of the accuracy of a physical similarity questionnaire with DNA determination of zygosity. The physical similarity questionnaire is provided to the parents (typically the mother) of a twin set and the parent responds to such questions as “Are the children as alike as two peas in a pod?” and “Are there differences in your twins’ eye (and hair) colors?” (Jackson et al., 2001). Given that similarity across a number of different phenotypes requires increased genetic similarity, the more concordant the twins appear according to such a questionnaire the greater the likelihood that the twins are MZ (Plomin et al., 2013).15 Jackson and colleagues (2001) found that the physical similarity questionnaire is a suitable alternative to the biological methods (blood groups or DNA)

15 Importantly, such a questionnaire was employed during data collection for the data included in the current study. Notably, however, both the primary caregiver (typically the mother) and the youth respondent reported on the confusability of physical appearance. There was a segment of twins (less than 100 pairs) whose zygosity was not able to be distinguished using this method; consequently, their zygosity was determined on the basis of molecular genetic markers (i.e., DNA; Jacobson and Rowe, 1998). See the Methods section of the current study (Chapter 5) for a more in-depth discussion of how zygosity was determined in the Add Health study. 58

for accurately determining zygosity within a twin sample. Similar results have been found by other researchers (see Christiansen et al., 2003; Gao et al., 2006) and the current consensus in behavioral genetics research is that a physical similarity questionnaire will typically produce a 95 percent accuracy rate.

Other than the scientific desire to be as accurate as possible in empirical research, the question can asked: What impact can incorrect zygosity determination have in twin study research? As a reminder, heritability estimates are produced by comparing the phenotypic concordance among a group of MZ twins to the phenotypic concordance of a group of DZ twins.

Consequently, to the extent that twins are misidentified estimates of h2 will be inaccurate.

Misidentification will likely not arise when biological determination methods are employed as the accuracy of these methods ranges from 96 to 100 percent (Jackson et al., 2001). Therefore, misidentification is more likely when researchers employ a method such as the physical similarity questionnaire. Furthermore, misidentification is most likely to be unidirectional. It is unlikely that MZ twins will be identified as DZ twins by their parents as the twins will exhibit concordance on sex, hair color, eye color, and other traits more so than DZ twins (Plomin et al.,

2013). Therefore, it is more likely that DZ twins will be misclassified as MZ twins (i.e., the direction of the misclassification is more likely to be from DZ to MZ). What effect does this increased likelihood of misclassification of a DZ twin set as an MZ twin set have on heritability estimates? Given that estimates of heritability will be computed from data which includes an artificially increased number of MZ twins (DZ twins classified as MZ twins) there will be a decreased amount of phenotypic similarity within the MZ group (i.e., the phenotypic discordance that DZ twins express will be attributed to the MZ group in the data). Therefore, the net effect of

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misclassification of zygosity tends to be a decreased estimation of the influence of genetic factors in explaining variance in a phenotype.

In summary, the methods employed in twin study research to determine zygosity are highly accurate overall. When mistakes are made they tend to be associated with non-biological methods of zygosity determination. Furthermore, if misclassification occurs its effect will be to provide a deflated estimate of heritability. Therefore, to the extent that twins are misclassified within a study using twin-based methods estimates of heritability are likely to be conservative estimates overall.

3.1.6 The External Validity of Twin Studies

While twin studies are a powerful tool to provide estimates of the relative influence of genetic and environmental factors in the etiology of phenotypic variance, there can be problems with the external validity of such studies (Plomin et al., 2013). The question is, to what extent can the results of twin-based studies be generalized to the general population and to singletons?

The answer to these inquiries appears to depend on the phenotype under study. Researchers have noted a number of ways in which twins (both MZ and DZ) differ from non-twin sibling pairs.

For example, newborn twins tend to have a lower birth weight than the average newborn singleton (MacGillivray, Campbell, and Thompson, 1988). Additionally, researchers have noted that brain, language, and cognitive ability development appears to be delayed in twins relative to singletons (Knickmeyer et al., 2011; Deary, Pattie, Wilson, and Whalley, 2005; Voracek and

Haubner, 2008). Therefore, to the extent that these differences have an important (i.e., functionally meaningful) influence on the phenotype under study then the results of a twin study will not be generalizable to the general population (i.e., non-twins).

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There are two important points about the differences between twins and singletons that also need to be made. First, the differences highlighted above appear to dissipate and equality is obtained during early school years (Plomin et al., 2013). For example, Christensen et al. (2006) illustrated in a longitudinal study of a Danish sample of twins and singletons that differences in gestational period (i.e., shorter for twins) and birth weight (i.e., less for twins) did not produce differences in scholastic outcomes during adolescence. The second important point is that researchers have found that twins do not differ from singletons on such behaviorally relevant outcomes as psychopathology (Robbers et al., 2010, 2011), motor development (Brouwer, van

Beijsterveldt, Bartels, Hudziak, and Boomsma, 2006), or personality (Johnson, Krueger,

Bouchard, and McGue, 2002). Additionally, researchers have failed to find significant difference between twins and singletons in phenotypes such as cognitive abilities, physical characteristics, or the incidence of a number of diseases (Evans et al., 2002). Therefore, studies which assess the relative influence of genetic and environmental factors on these phenotypes can reasonably be generalized to a general population of singletons.

3.1.7 Summary of Twin Studies

The twin-study method is a powerful tool in understanding the etiology of virtually every measureable phenotype, including behavior. As Plomin et al. (2013) note, over “20,000 papers on twins were published during the five years from 2007 to 2011, with more than half of these focused on behavior” (p. 83). This number has been growing since 2011 and the influence that twin studies have on our understanding of behavior is undeniable. However, as noted above there are some components of the design which could bring results gleaned from a twin study into question.

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Despite the potential for methodological problems with the twin study design, researchers have noted that the bias is not unidirectional. In other words, the genetically sensitive twin design does not always provide an over-estimation of the influence of genetic factors if the methodological problems are of significance. In recognition of this fact, Moffitt, Ross, and

Raine (2011) provide the following observation: “[t]he methodological problems of twin studies are just as likely to decrease heritability estimates as opposed to inflate them. In all probability, these effects tend to cancel each other out” (p.60; emphasis in original). Consequently, the findings that result from twin-study analyses can be generally considered as reliable and robust estimates of the relative influence of genetic and environmental factors on the phenotypes studied. Despite this reassurance, and in response to the critiques of the twin-study design, behavioral genetic researchers have generated and employed a variety of other genetically sensitive designs. The family study is one such design and it is to this alternative to the twin study that we now turn our attention.

3.1.8 Family Studies

As an alternative to twin studies, family-based research designs are primarily employed in order to avoid the potential external validity problems of twin studies. The method is very similar to the twin-study design. Two members of a kinship pair are selected from each household, but instead of the members being only MZ or DZ twins they can be any type of kinship pair (Beaver, 2009). For instance, the members of the kinship pair can be full siblings, half-siblings, stepsiblings, cousins, or any type of kinship pair. As indicated above, in order for the method to be a genetically sensitive design the data-collection requirements of a family identification variable and a genetic-relatedness variable must satisfied. Therefore, in knowing the level of genetic relatedness Equations 2.2, 2.3, and 2.4 can then be employed to estimate the 62

influence of genetic and environmental factors on a phenotype of interest. The equations change based on the different levels of genetic relatedness for different kinship pairs. Table 3.1 provides an illustration of some of the varying degrees of genetic relatedness among different kinship pairs (i.e., the table is not exhaustive). The logic for family-based research is the same as for twin studies: if the kinship pair with the greater level of genetic relatedness exhibits greater phenotypic concordance then the researcher can conclude that the phenotype under study is likely influenced by genetic factors.

Table 3.1: Examples of degrees of genetic relatedness by kinship pair.

Kinship Pair Degree of Genetic Relatedness

Full siblings 0.50 Biological parent (and child) 0.50 Grandparent (and child) 0.25 Half-siblings 0.25 First cousins 0.125 Stepsiblings 0.00

While the logic is similar to twin-based studies, researchers employing the family design must alter their analyses somewhat to account for the difference inherent in different kinship pairs. For example, the kinship pairs outlined in Table 2.1 could all represent individuals within a pair who differ in terms of sex and/or age. Consequently, these demographic characteristics must be accounted for when employing the family-based research design. Fortunately,

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behavioral genetic researchers have developed methods to account for such influences when generating estimates of h2, c2, and e2 using a family-based research design (Plomin et al., 2013).

3.1.9 Adoption Studies

Adoption studies represent a very powerful, quasi-experimental way to assess the relative influence of genetic and environmental factors on a phenotype of interest (Evans et al., 2002).

Indeed, adoption studies are the most direct way of estimating the shared environmental component of phenotypic variation (Rowe, 1994). Given that an adoptee is unlikely to share genetic material with their adoptive parents any similarity between the adoptee and his or her adoptive parents is due to shared environmental effects.16 Conversely, an adoptee does not share an environment (i.e., a rearing environment) with his or her biological parents but does share genetic material. Like any offspring, the adoptee will possesses 50 percent of his or her biological mother’s genes and 50 percent of his or her biological father’s genes. Consequently, any phenotypic similarity that an adoptee shares with his or her biological parent(s) can only be due to genetic factors. Given this underlying logic, the adoption method requires data to be collected from three sources: the adoptee, the adoptive parent(s), and the biological parent(s).

This information then allows for a comparison of the phenotypic variation measured in the adoptee with both his/her adoptive parent(s) and the biological parent(s). Given this information, the researcher can assess the direct estimate of shared environmental effects via the correlation between the adoptee and his/her adoptive parent(s). Furthermore, the researcher can assess the

16 If an adoptee and his/her adoptive parent(s) did share genetic material then any estimation of c2 would likely be an overestimation as the observed similarity could be attributed to that shared genetic material (likewise, any estimate of h2 would be an underestimate in this case). 64

direct estimate of heritability via the correlation between the adopted child and his/her biological parents.17

The adoption method has been widely employed in the social sciences, but is rarely seen in criminology (Beaver, Rowland, Schwartz, and Nedelec, 2011; Raine, 1993). The most likely reason for this relative paucity in the criminological literature is the difficulty in obtaining the three necessary pieces of information outlined above (and the relative lack of primary data in criminology overall; Beaver, 2009). Additionally, the adoption method is not without its critics.

The three main critiques against the adoption method are external validity, selective placement, and timing of adoption.

3.1.10 External Validity of Adoption Studies

Like twin studies, the results of adoption studies are derived from a particular segment of a population. Therefore, to the extent that the subsample (i.e., adoptees, adoptive parents, and biological parents of adoptees) is not representative of the larger population the findings of an adoption study are limited in terms of their generalizability (Evans et al., 2002). Behavioral genetic researchers have explored this issue and have found in a number of adoption-based samples biological parents of adoptees and adoptive parents do not appear to be significantly different and can therefore be considered representative (Petrill, Plomin, DeFries, and Hewitt,

2003).

17 The adoption method can also be modified to include two adopted children from the same household who share a rearing environment but are genetically unrelated (Plomin et al., 2013). This design allows for another way in which the shared environmental influence can be directly tested (any correlation between the two children can be attributed to c2). Ideally, this design can be integrated into the design outlined above where information on the adoptees’ biological parent(s) is also included, thereby allowing for a comparison of environmental and genetic effects. 65

3.1.11 Selective Placement in Adoption Studies

The methods by which adoptees were put up for adoption and adopted away have changed over the past century or so (Rowe, 1994). Today, adoption is handled via private attorneys and adoption agencies. These agents may take efforts to match the adoptee with adoptive parents who are similar in characteristics such as race and SES as the biological parents of the adoptee. Therefore, if selective placement matches the adoptive and biological parents the estimates of both genetic and environmental effects could be inaccurate (Plomin et al., 2013). In other words, the underlying logic of the adoption study design (adoptee and biological child do not share an environment) could be in jeopardy. However, the effects of selective placement can be assessed directly if there is sufficient information on the biological parent(s). Researchers have noted that while some phenotypes are initially influenced by selective placement (e.g., IQ) there is little evidence for an effect of selective placement on phenotypes such as personality, psychological dimensions, and psychological disorders (all of which are related to antisocial behaviors; Plomin et al., 2013).

3.1.12 Timing of Adoption in Adoption Studies

The final critique levied against the adoption study design is the effect of differential time of adoption. The critique goes as follows: to the extent that adoption occurs later in the child’s life there will be an effect of the shared environment from the biological parent(s) and any observed phenotypic correlation between the adoptee and the biological parent(s) could be attributed to shared environmental effects rather than shared genetic effects. Therefore, to the extent that variance in a phenotype is influenced by shared environmental effects timing of adoption can be an important variable to consider in interpreting the results of adoption studies.

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Fortunately, much like the selective placement critique, the timing of adoption can be taken into account in multivariate methods employed in behavioral genetics research (Harris, 2009).

Overall, adoption studies are able to overcome the methodological problems relatively easily and therefore provide a supplemental manner in which to accentuate and extend the findings of twin- and family-based research.

3.1.13 Monozygotic Twins Reared Apart Studies

Studies employing monozygotic twins reared apart (MZA) take advantage of a rare natural experiment. Genetically identical individuals who are separated at birth have been included in a range of studies covering a swath of behavioral and personality outcomes (Segal,

2010). MZAs represent a particularly stringent manner in which to assess the relative influence of genetic and environmental factors (Bouchard, Lykken, McGue, Segal, and Tellegen, 1990).

The logic of the MZA study follows the basic twin study, but with a twist: given that the twins share all of their genetic material and none of their rearing environments the equal environment assumption does not apply. Therefore, any phenotypic similarity between the twins in an MZA study can only be attributed to genetic factors.

The most well-known collection of MZA studies comes from the Minnesota Study of

Identical Twins Reared Apart (MISTRA). This on-going study began in 1979 and includes the recruitment of MZA twins via a variety of ways (see Bouchard et al., 1990). The twins are brought in to the MISTRA offices, at the expense of the study, and subjected to week-long interviews and questionnaires (often the spouses of the twins are interviewed as well). The variety of phenotypes covered by the MISTRA is vast and includes such factors as IQ, psychophysiological variables, cognitive abilities, personality measures, social attitudes (e.g.,

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religiosity), brainwave measures, sexual behaviors, substance abuse, and antisocial behaviors

(Bouchard et al., 1990; Grove et al., 1990; Segal and Stohs, 2009).

There are at least three key findings that emanate from this collection of research and other studies employing the MZA approach (e.g., Stunkard, Harris, Pedersen, and McClearn,

1990). First, almost every phenotype thus measured using the MZA design has illustrated that genetic factors have a strong influence on phenotypic variance. Second, these studies highlight the relative small impact of the shared environment in generating phenotypic variance. These two overall findings converge with other behavioral genetic research employing different samples and different designs that obtain similar results. The third intriguing result of the MZA studies illustrates that MZ twins who are reared apart are no more different than MZ twins reared together. This finding, in conjunction with the overall results of MZA studies demonstrate further the tenability of the equal environment assumption in twin studies (i.e., it is a fair assumption to make and should not be considered a critique of the twin methodology).

Although the MZA approach is an incredibly informative method, it is not free from critiques. The main critique leveled against the MZA design is similar to the selective placement critique against adoption studies in general. Critics claim that although the twins are separated at birth they are likely placed into adoptive homes which provide similar environments (Beaver,

2009; Walsh, 2002). Consequently, to the extent that the adoptive environments are similar between MZAs estimates of phenotypic similarity attributed to h2 will be inflated. Although the critique appears to be logically sound, it requires that the similarity in environment have a statistically significant influence on the phenotype(s) under study. Furthermore, the critique also requires that the effect of the environment inflate the estimates of heritability so much so as to push the effect beyond that seen in other behavioral genetic designs. These two assumptions do

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not converge with the observable data. First, multiple methods employed in behavioral genetic research indicate the shared environment has a relatively small effect on phenotypic variance.

Importantly, this observation comes from studies where siblings are reared together and therefore have a shared environment correlation of 1.0. Even if it is the case that MZA twins are exposed to different, yet similar, environments the similarity will have a correlation of less than 1.0.

Therefore, it can be expected that any non-zero effect of the shared environment in MZA studies will be minimal. Second, the estimates of heritability derived from MZA studies do not differ significantly from the estimates of h2 derived from other studies (i.e., they are within the same confidence intervals). Therefore, while logically sound the main critique against the MZA design is likely unfounded.

3.1.14 Combination Design Studies

The discussion thus far illustrates that behavioral genetics researchers have employed a variety of research designs in examining the influence of genes and the environment on phenotypic variance. As was also highlighted, each of these methods has been exposed to various critiques. Consequently, an increasingly prevalent practice within behavioral genetic research is to employ a combined research design wherein one or more of the above designs are integrated into the same study. These combinations can take the form of comparing twins reared apart and twins reared together (e.g., Bouchard et al., 1990; Lykken, 2006; Kato and Pedersen,

2005) or the integration of biologically related individuals into an adoption-based study (e.g.,

Plomin, Fulker, Corley, and DeFries, 1997). Additional alternatives to the designs outlined above also exist. For example, a combination of the twin and family design recently employed includes the study of families of MZ twins and is known as families-of-twins design (Knopik,

Jacob, Haber, Swenson, and Howell, 2009). Another version of the families-of-twins design 69

involves the inclusion of both twins and their children; this method is known as the children-of- twins design (D’Onofrio et al., 2003; Knopik et al., 2006; Narusyte et al., 2007). Researchers have noted that this method is particularly suited for evaluating the risk factors for certain negative phenotypes (e.g., schizophrenia and other personality disorders; D’Onofrio et al., 2003).

While the primary attraction to employing a combination design is to compensate for the limitations of any single method, there are at least three other advantages. First, widening the parameters of inclusion for respondents necessarily results in an increased sample size.

Therefore, combination designs will tend to allow for greater statistical power. Second, including various types of respondents in a sample allows the researcher to test such assumptions as the EEA, selective placement, or similarity in environments (Plomin et al., 2013). Finally, in employing a combination design, which often leads to a more conservative test of a hypothesis, researchers can provide further confidence in the results of studies which employed more specialized samples of just twins and adoptees (Plomin et al., 2013).

3.1.15 Isolating Components of e2

As the above summary indicates, research employing a genetically sensitive design has highlighted the influential effects of both genetic and environmental factors. Specifically, these studies have consistently shown that two of three components of the equation of phenotypic variance (see Equation 2.1) carry the majority of the weight in terms of explained variance: h2

(heritability) and e2 (nonshared environment); while the final component, c2 (shared environment), tends to have minimal effect. While these findings have provided considerable advancement for our understanding of human behaviors and personality, decomposing the variance of a phenotype into the three components (h2, c2, and e2) does not provide an indication of the specific causal variables that work to produce phenotypic variance. In addition to 70

molecular genetics (described below) to isolate components of the heritability estimate, behavioral genetics researchers employ particular methods to isolate components of the environment to assess their relative impact on a phenotype. Collectively referred to as nonshared environment studies, these research designs strive to isolate the effect of specific constituents of the nonshared environment after statistically controlling for the effect of genes, the shared environment, and the remaining nonshared environment (excluding the isolated component;

Plomin and Daniels, 1987; Plomin, Chipuer, and Neiderhiser, 1994).

Nonshared environment studies have been subject to considerable methodological evolution over the past few decades. In their description of designs to tap the nonshared environment Plomin et al. (1994) provided examples of designs which required specific types of samples to be collected (e.g., adoptees, twins, or families) and assumptions about which portions of the nonshared environment were at play to be made. These types of methods have been replaced with more sophisticated designs and statistical techniques. For example, a commonly employed method is the sibling discrepancy or sibling difference score method (Rovine, 1994).

The most rigorous version of this design is the MZ difference design wherein a difference score for a particular phenotype is calculated amongst a group of MZ twins (i.e., the first twin’s score on a phenotype is subtracted from the second twin’s score, or vice versa). Given that MZ twins share 100 percent of their DNA and 100 percent of their shared environment (by definition) any difference between the twins must be due to the nonshared environment. Therefore, the difference score represents the nonshared environmental influence on the phenotype under study

(Beaver, 2008; Rovine, 1994). This difference score can then be employed in an equation in order to isolate the influence of a specific component of the nonshared environment on phenotypic variance. This method is recognized as the ‘gold standard’ for assessing the effects

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of nonshared environmental influences (Beaver, 2008). Such weight is afforded to this method as the design automatically controls for the effects of genes and shared environmental factors, does not require satisfaction of the EEA (as there is no comparison between MZ and DZ twins), and provides unbiased indices of differences between twins (Rovine, 1994). The MZ difference approach is not only used as a way of isolating the nonshared environment, but it is also a particularly rigorous manner in which to assess the causal relationship between variables.

Indeed, the MZ difference score method is referred to as a quasi-experimental method and some researchers have indicated that it gets as close to assessments of causality that a non- experimental method can be (Vitaro, Brendgen, and Arseneault, 2009). As a result of the method’s ability to approach causality and allow for the most rigorous non-experimental assessment of an association it is employed in the current study (see Chapter 5).

3.1.16 Summary of Behavior Genetics Methods

The quantitative methods outlined above point to a rich array of techniques to assess the relative influence of genetic and environmental factors on phenotypic variance. The importance of these methods can be juxtaposed to the shortcomings of the SSSMs and the findings derived from such methods. Employing such methods has provided considerable advances in the understanding of human behavior and has been increasingly beneficial to understanding antisocial and reproductive behaviors, outcomes germane to the current study (Ferguson, 2010;

Raine, 2002). The critiques of quantitative genetics have received considerable attention in the behavioral genetics literature and these responses have illustrated the methodological and statistical rigor employed in behavioral genetics research (Walsh, 2002). The argument that genes do not belong in explanations human behavior (e.g., Gottfredson and Hirschi, 1990) is no longer a tenable position to hold. The quality of the research designs, the variety of research 72

designs, and the increasing availability of genetically sensitive samples provide to the continued inclusion of behavioral genetics methods into criminological analyses. In addition to such designs, behavioral genetic researchers also employ molecular genetics methodologies. These designs allow for the exploration of the effect of specific elements of the heritability component of the equation of phenotypic variance (see Equation 3.1). It is to this area of biosocial criminology research that we now turn.

3.2 Molecular Genetics and Gene-Environment Interplay

Recall that behavioral genetic research designs decompose the variance of a phenotype into the three components of the equation of phenotypic variance (i.e., h2, c2, and e2). The results of these analyses provide estimates of the amount variance in a phenotype due to each of the three latent components. However, as mentioned above the details of what comprises each of the estimates are not provided in such analyses. The discussion above outlined how specific components of the nonshared environment can be isolated, however isolation can also occur for estimates of genetic effects (although the procedures of isolation differ). In this vein, researchers employ the methodologies of molecular genetics to analyze the effects of specific genes on phenotypic variance (Medland and Hatemi, 2009). While the current study does not employ a molecular genetics analysis, it is necessary to cover this material in order to gain a full understanding of the biosocial approach in general and how an evolutionary paradigm can further inform both behavioral and molecular genetics research. However, the coverage of molecular genetics herein will be cursory and the interested reader should consult more detailed texts (e.g., Carey, 2003; Plomin et al., 2013; for a concise review of molecular genetics and behavior see Ebstein, Israel, Chew, Zhong, and Knafo, 2010).

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3.2.1 Molecular Genetics

The search for the specific genetic information that influences phenotypic variance is predicated on knowledge of the gene. Therefore, any coverage of molecular genetics requires coverage of the gene and genetic functioning.

3.2.2 The Gene

Simply put, a gene is a portion of deoxyribonucleic acid (DNA) that codes for the amino acid sequence of a protein (Walsh, 2002). Sometimes referred to as the “master molecule of life on Earth” (Sagan, 1980: 31) or “immortal coils” (Dawkins, 2006: 21), DNA is found in the nucleus of all cells within an organism (except red blood cells). The function of DNA can be categorized into four separate tasks: first, DNA contains the instructions (blueprint) for the construction of proteins and enzymes; second, DNA influences when proteins and enzymes are made; third, DNA carries its information (instructions) when cells divide; and fourth, DNA transmits its information from parent to offspring during reproduction (Carey, 2003). In humans, every individual’s DNA is unique save for the case of MZ twins who share 100 percent of their

DNA. Consequently, individuals can vary in the manner in which the tasks of DNA are carried out and the resulting effects on phenotypic variance (Beaver, 2009).

The structure of DNA consists of two fibers, referred to as polynucleotides, twisted around each other to form the now famous double helix. Located along each of the two polynucleotides are four different nucleotides that constitute the entirety of the genetic alphabet

(i.e., the sequences by genes are defined). This genetic alphabet is comprised of adenine (A), thymine (T), cytosine (C), and guanine (G). These nucleotides are chemically bonded together to form a base pair which holds the two strands of DNA together (this forms the double helix

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structure). The base pairs are arranged in the same fashion such that A always bonds with T and

C always bonds with G. The base pairs are crucial in defining a gene. A contiguous set of base pairs that work together to code for specialized functions or structures is considered a gene.

Crucial to phenotypic expression is the ordering of base pairs as small divergences can have considerable alterations to phenotypic expression. A poignant example of this is represented by humans and chimpanzees who share approximately 96-98 percent of the same DNA. The considerable morphological and behavioral differences between these species emanates from the quantitative (i.e., base pair ordering) differences in the genetic code rather than qualitative (i.e., different gene products) differences (Walsh, 2011). With the mapping of the human genome, researchers have determined that human DNA contains approximately 3 billion base pairs and approximately 20,000 genes (Beaver, 2009; Dawkins, 2006).

DNA is located within what are known as chromosomes. The double helix structure of the DNA allows for compact inclusion of a vast number of sections of DNA (i.e., genes) to be wrapped around the thread-like chromosomes. It is the chromosome that is the physical unit of genetic inheritance and the manner in which DNA is transferred from parent to offspring (Carey,

2003). In humans, individuals inherit one set of chromosomes from their biological mother and one set of chromosomes from their biological father. The human body consists of 23 pairs of chromosomes derived from these inherited sets. Chromosomes 1 through 22 are referred to as autosomes and genes located on these chromosomes are made up of two different copies

(maternal and paternal; Beaver, 2009). Chromosome number 23 is not an autosome but is referred to as sex chromosomes as it determines the biological sex of the offspring. In humans, females possess two X sex chromosomes while males possess one X sex chromosome and one Y sex chromosome. Given that females do not possess a Y chromosome, sex determination is

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passed on paternally. Additionally, females do not possess any genes located on the Y chromosome but have two copies of any genes located on the X sex chromosomes. Conversely, males possess only one copy of genes located on the X sex chromosome and only one copy of genes located on the Y sex chromosome. Therefore, to the extent that phenotypic variance is influenced by the sex chromosomes the variance will be differentially expressed in males and females.

3.2.3 Genetic Polymorphisms

Every individual within a sexually reproducing species possesses two copies of most genes, with one copy maternally inherited and one copy paternally inherited. Each single copy of a gene is referred to as an allele, where two alleles represent one gene (recall the structure of

DNA). For the majority of genes there is only one version that is inherited by an individual’s biological parents (i.e., there is no allelic difference). The reason for this invariance is what leads to the considerable similarities across members of the same species. Due to the processes of natural and sexual selection, the majority of a species’ genome is identical from individual to individual (Dawkins, 2006). Indeed, humans share approximately 99 percent of their DNA across unrelated individuals (Plomin et al., 2013). However, as mentioned above, individuals can be differentiated based on their genetic differences and these genetic differences are referred to as distinguishing DNA (Beaver, 2009). Distinguishing DNA is represented by allelic differences between individuals and genes with two or more alleles (i.e., alternative copies of genes) are referred to as genetic polymorphisms (Beaver, 2009). It is in polymorphisms that molecular genetic researchers place their interest and where phenotypic variance due to heritability (i.e., h2) can be found (Walsh, 2011).

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Genetic polymorphisms (i.e., allelic differences) are typically illustrated using what is referred to as a Punnett square. The Punnett square displays the statistical probability of differential expression (i.e., phenotypic variance) given the possession, via inheritance, of a particular genotype. The specific combination of alleles for a polymorphism is referred to as the genotype. The genotype is the collection of genetic material that an individual possesses. Figure

3.1 provides an example of a Punnett square for a hypothetical polymorphism.

Maternally Inherited Allele A a A AA Aa Paternally Inherited Allele a aA aa

Figure 3.1: Punnett square for a hypothetical polymorphism.

As illustrated in Figure 3.1, both the mother and the father possess an ‘Aa’ genotype however any offspring they have may possess different genotypes. For example, if the couple were to have four offspring each offspring is represented by a quadrant within the Punnett. The first and fourth quadrants contain a child with what molecular geneticists call a homozygous gene (i.e., identical allelic combinations [‘AA’ and ‘aa’]). The second and third quadrants contain a child with a heterozygous gene (i.e., different allelic combinations [‘Aa’]).18 The

Punnett square also illustrates the difference between dominant and recessive genes. A dominant gene is one that is both represented in the genotype and always expressed in the phenotype;

18 Note that the ordering of alleles is of no importance as the genotype is the same if it is written as ‘Aa’ or ‘aA’. 77

whereas a recessive gene is one that if represented in the genotype is only expressed as a phenotype if paired with another recessive allele (Anderson, 2007).

The connection between polymorphisms and behavior is best highlighted using the

Punnett square and a hypothetical example. Suppose that a hypothetical gene partially influences a person’s level of empathy, where levels of empathy range from 0 to 100, with higher scores representing greater levels of empathy. Additionally, suppose that there are two alleles of the hypothetical empathy gene where ‘A’ increases empathy by 10 points and ‘a’ decreases empathy by 10 points. Using this hypothetical coding scheme and the Punnett square, we can see that the child in the first quadrant would likely have the highest level of empathy (‘AA’ increases empathy by 20 points), whereas the child in the fourth quadrant would likely have the lowest level of empathy (‘aa’ decreases empathy by 20 points). The example, though exceedingly simplified, illustrates that genotypic variance (i.e., different allelic combinations) can partially influence phenotypic variance (i.e., different levels of empathy).19

In molecular genetics, recognizing that genetic variance exists and is inherited by offspring is supplemented by knowledge of how genetic variance (i.e., polymorphisms) occurs.

There are three types of genetic polymorphisms. The first type is referred to as a single nucleotide polymorphism (SNP). As implied by the name, an SNP arises when there is a change in one nucleotide base where two alleles are identical except for a one-nucleotide difference.

SNPs represent the most common way in which polymorphic differences arise and they appear in

19 The example also illustrates two other points. First, the example highlights the point that any single gene will have a relatively small influence on the variance of a single phenotype. For phenotypes as complex as personality characteristics and behavior it is likely that numerous genes interacting with one another and the environment account for the majority of variance in a phenotype. However, the point that one gene still has an influence on a phenotype should not be lost. As Dawkins (2006) and others have argued, the phenotypic variance would be different if not for the presence of the specific genetic variant. The second point illustrated by the example is that recessive genes can sometimes have a considerable effect when expressed in a phenotype. Many deleterious genes (i.e., genes that result in a negative outcome – low empathy could certainly apply here) are recessive and as such, from a statistical norm for the phenotype is greater when a genotype contains two recessive allelic versions of a gene (Anderson, 2007). 78

approximately 100 to 300 base pairs (Beaver, 2009). The second type of genetic polymorphism is referred to as short tandem repeats (STRs) or microsatellites. In this type of polymorphism different genes have a range of various repeated contiguous base pairs (nucleotides) repeated a various number of times. The allelic difference arises due to the varied number of times a small string of contiguous base pairs is repeated. The third type of genetic polymorphism is known as variable number of tandem repeats (VNTRs) or minisatellites. VNTRs are similar to STRs with the main difference being the number of nucleotides (i.e., base pairs) that are repeated. The arbitrary demarcation point distinguishing STRs from VNTRs is around 10 or more repeated base pairs (Beaver, 2009).

3.2.4 Genetic Variance and Phenotypic Variance

Given that social scientists, as well as behavioral and molecular genetics researchers, are interested in what affects behavioral and personality outcomes, information on how genetic variance can influence phenotypic variance is exceedingly valuable. From a molecular genetic standpoint, there are three main ways in which this process can occur.20 The first way genetic variance (i.e., polymorphisms) can produce phenotypic variance is referred to as a monogenic effect. In this instance, one gene is the cause of a particular phenotype. In terms of personality and behavioral outcomes, monogenic effects are unlikely of functional significance. However, the second type of genetic causation, referred to as polygenic effects, is more likely to be influencing variance in behavior and personality. Polygenic effects are those instances in which a number of genes work to produce phenotypic variance. When molecular genetics researchers analyze polygenic effects on complex phenotypes like behavior they recognize that genes work

20 It is important to remember here that molecular genetics recognizes that genes interact with both other genes and the environment in the production of phenotypic variance, a point to which we will soon turn. This portion of the discussion highlights the processes where genotypic variance can influence phenotypic variance in a direct manner. 79

in an interactive fashion to increase or decrease the probability of a particular phenotypic outcome (Beaver, 2009). This point highlights the probabilistic nature of biosocial research and the futility of claims that a genetic approach is deterministic. The third manner in which genetic variance can influence phenotypic variance is referred to as pleiotropy. A pleiotropic effect occurs when one gene has multiple phenotypic outcomes. At first glance, this type of effect seems likely to be relatively rare. However, researchers have noted that many psychological disorders in which considerable comorbidity exists (e.g., ADHD and conduct disorder) may be due to pleiotropic effects (Jain et al., 2007).

As mentioned a number of times, molecular genetics research highlights how genetic variance can result in phenotypic variance. Additionally, it was mentioned that a biosocial approach is necessarily probabilistic. A significant component of taking such an approach is recognizing the interactive fashion between genes and the environment (indeed, the very moniker ‘biosocial’ draws attention to this recognition). Therefore, we turn now to a discussion of how risk factors, both genetic and environmental, can interact to produce phenotypic variance.

These interactions, called gene-environment interplay, come in two primary forms: gene- environment interactions (GxE) and gene-environment correlations (rGE).

3.2.5 Gene X Environment (GxE)

The concept of an interactive effect is very familiar to criminologists. Indeed, assessing the effects of an interaction in a multivariate equation is a common step in many criminological analyses. The logic of a GxE follows the logic of any statistically interactive (i.e., nonadditive) relationship: the effects of a genotype on the likelihood of the development of a particular phenotype (e.g., delinquency) will depend on the exposure to different types of environmental characteristics. The reverse is also true; the effect of a particular environmental factor will 80

depend on the presence of a particular genotype (Moffitt, 2005; Walsh, 2011). Therefore, variations in the possession of particular genotypes and exposure to certain environmental factors work together to produce phenotypic variation that would not otherwise be seen in the absence of one of the constituent factors of the interaction.

Gene-environment interactions provide the explanation for why individuals exposed to similar environments lack a consistent phenotypic covariation. Criminologists often explore criminogenic variables that are referred to as risk factors which increase the likelihood of antisocial behavior. Additionally, these risk factors are often invariant among individuals within a given unit of analysis (e.g., impoverished neighborhoods). However, as recognized by criminological research (e.g., Gottfredson and Hirschi, 1990) the majority of individuals exposed to such environments do not engage in antisocial behavior. Therefore, a causal variable is missing from the equation; this causal variable is genetics.

Using hypothetical data, Figure 3.2 illustrates a simple GxE. Representing the genetic factors are Genotype 1 and Genotype 2. For simplicity’s sake, suppose that the two genotypes represent a difference in one gene resulting from a single nucleotide polymorphism (SNP).

Additionally, the x-axis represents a varied level of exposure to a criminogenic factor found in the environment (e.g., severity of abuse experienced as a child). Finally, the values on the y-axis represent scores reported on a measure of a phenotype of interest (e.g., delinquent acts during adolescence). Inspection of Figure 3.2 highlights three important factors. First, there appears to be a main effect of Genotype 2 in terms of its relationship with delinquency. Across all levels of severity of abuse, Genotype 2 is associated with increased numbers of delinquent acts. Second, there also appears to be a main effect of the environmental criminogenic factor. Across both types of polymorphisms (i.e., genotypes) there is a greater number of reported acts of

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14

12

10

8

Genotype 1 6 Genotype 2

4 Number of Delinquent Acts Delinquentof Number

2

0 1 2 3 4 Severity of Abuse Experienced During Childhood

Figure 3.2: Depiction of a hypothetical GxE.

delinquency with a greater amount of childhood victimization. Finally, although severity of childhood abuse is associated with increased delinquency across both genotypes, Genotype 1 has a much steeper slope than Genotype 2. This is the essence of a GxE. The effect of either genetic or environmental factors is more pronounced when there is an interaction of both genetic risk factors (in our example Genotype 2) and environmental factors (in our example severe childhood victimization). This precise relationship was highlighted in the well-known study by Caspi and colleagues (2002) published in the journal Science. The authors found that, among males, possession of the risk allele for neurotransmitter-metabolizing enzyme monoamine oxidase A

(MAOA) moderated the effect of childhood maltreatment. Stated differently, the effect of the genetic risk (i.e., low-activity MAOA allele) on antisocial behaviors appeared only when 82

combined with the experience of childhood maltreatment. This relationship represents a classic

GxE. Indeed, a great deal of research exploring GxEs arose after the Caspi et al. (2002) study was published analyzing the interactive effects of a wide variety of genetic polymorphisms and environmental factors on various antisocial phenotypes (Beaver, 2009).

3.2.6 Gene-Environment Correlation (rGE)

The second type of gene-environment interplay is referred to as gene-environment correlation, denoted as rGE. While GxEs highlight the manner in which genetic and environmental factors combine to generate phenotypic variance, rGEs illustrate the manner in which genetic factors can lead to variance in exposure to different environments (Beaver, 2009).

When seen through the lens of traditional criminology the word ‘environment’ denotes a social, political, or economical variable external to the individual which is influenced by factors that are also external to the individual. Additionally, the influence of the environment is considered to be a one-way street where individuals are affected by the external events. However, behavioral genetics research has illuminated how variables external to individuals are in fact influenced by factors that are internal to the individual. In other words, this research has shown that genetic factors can not only systematically produce phenotypic variance but also environmental variance

(Scarr and McCartney, 1983). Despite the counter-intuitive nature of this claim, a considerable amount of research has emerged to consistently show that many environmental factors are influenced by genetic variance (Jaffee and Price, 2007). To better understand how this collection of observations could arise, we now turn to the various types of rGEs that have been identified by behavioral and molecular genetics researchers: passive rGEs, active rGEs, and evocative rGEs.

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3.2.7 Passive rGE

The passive rGE typology highlights the fact that a child receives both genetic and environmental influences from his or her parents. Given that these two constellations of factors emanate from the same source they are likely to be correlated. Consequently, a child’s environment will be statistically associated with his or her environment. A passive rGE illuminates the dual parental influence on phenotypic variance in their offspring: first, via genetic propensities for certain personality and behavioral traits in their children and second, via environments characterized by specific factors which facilitate the expression of those propensities (Walsh, 2011).

An important component of passive rGEs is the concept of assortative mating. Recall that this process refers to the nonrandom mating of individuals partially based on genetic propensities relating to one or more phenotypes. Given that people tend to mate based on characteristics which are partially influenced by genetic factors any offspring resulting from such a mating will have an increased probability of also possessing the phenotype(s) that lead to the formation of the relationship. Additionally, given that both parents possess the phenotype(s) of interest they will also create an environment in which the expression of such a phenotype is maximized (see active rGE). Therefore, any offspring of such a mating will receive both an increased probability of possessing a particular genotype and an environment in which expression of that genotype is facilitated (i.e., beyond what would be expected if only one of the parents possessed the genotype of interest; Krueger et al., 1998).

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3.2.8 Active rGE

Active rGE refers to the nonrandom selection into various environments (e.g., peer groups, employment, hobbies, etc.) based on genetic propensities which are best expressed within the selected environments (Plomin et al., 2013). Similarly, just as genetic propensities will work to increase the probability of selection into various environments these propensities will also work to decrease the probability of selection into other environments (i.e., work to push individuals away from certain environments). Given the vast array of environmental circumstances an individual could find him or herself, the extant literature indicates that so- called ‘niche-picking’ occurs partially as a result of the influence of genetic factors (Plomin et al., 2013; Walsh, 2011). Additionally, active rGEs become more salient in an individual’s life as she gains more independence from such social controls as parents and school (Walsh, 2011).

This observation is particularly salient in the finding mentioned above where MZ twins reared apart who reunite in adulthood share a vast number of similarities across a wide swath of their lives (Segal, 1999).

3.2.9 Evocative rGE

The idea that people are ‘blank slates’ awaiting to be ‘written’ upon by parents, peers, and society has a long history in social science and dates back, at least, to the philosophy of John

Locke (Pinker, 2002). The argument has taken different forms since Locke’s pontifications (e.g., the behaviorism of Skinner) but the main thrust of the position is that people come into the world without any innate characteristics which influence their interaction with the world. Evocative rGE presents the opposite argument, indicating that individuals elicit different reactions from others in their environment and those reactions are partially based on genetic influences on such

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phenotypes as personality, emotion, and behavior (Plomin et al., 2013). In other words, genetic differences generate phenotypic variance which in turn elicits variance in the manner in which the individual’s environment reacts to such phenotypic variance. In short, genetic factors evoke different reactions from the environment. Consequently, there is a statistical association between the individual’s genotype and his or her environment. Importantly, the evocative processes are generalized across virtually any environment including the home, school, and the workplace. In other words, individuals will differentially evoke reactions from parents, peers, and strangers alike partially based on their genotype (Caspi et al., 2004; Harris, 1995, 2009).

3.2.10 Summary of Gene-Environment Interplay

Gene-environment correlation has particular relevance to criminology. Indeed, a considerable amount of research has already been produced which highlights the importance of recognizing how genetic variance can impact such criminologically relevant variables as delinquent peers (Beaver, Shutt, et al., 2009; Beaver, Wright, and DeLisi, 2008; Cleveland et al.,

2005) and poor parental attachment (Caspi et al., 2004). So too has gene-environment interactions proved to be informative in this regard (Moffitt, 2005; Raine, 2002). Consequently, as a component of biosocial criminology this type of research is particularly fruitful for the advancement of criminology as a science. Therefore, the next section provides a brief overview of biosocial research on antisocial behavior which has employed both behavioral and molecular genetic approaches.

3.3 Biosocial Research on Antisocial Behavior and Sexual/Reproductive Behaviors

In order to exemplify the application of a biosocial approach to the analysis of antisocial conduct and sexual behaviors this section reviews some of the current literature. Importantly,

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this section reviews the separate literature for both antisocial behavior and sexual/reproductive behaviors. The section provides an overview of the general utility of a biosocial approach to understanding of human behavior rather than an exhaustive review.

3.3.1 Meta-Analyses of Biosocial Research on Antisocial Behavior

In any given field of study there will be a vast amount of research investigating similar topics but that differ in terms of samples, methodology, or statistical analyses. This accumulation of research can lead to a situation where the overall pattern of results contained within the individual studies is difficult to determine. Therefore, researchers in various fields employ the methodological tool of meta-analysis. A meta-analysis is a ‘study of studies’ and allows a researcher to ascertain if a general pattern in the results of the individual studies can be found (Cooper, 2010). This method is crucial to the advancement of science as no one study will provide definitive support for any finding and often within a body of work studies will vary in terms of their results. A meta-analysis can provide an overall summary of a body of literature and indicate what the average finding is across a number of studies. The statistic reported which indicates the average finding is typically referred to as the d-index or r-index and provides an indication of the typical effect size of a relationship, weighted by the various sample sizes of the individual studies (Cooper, 2010). Importantly, not all studies included within a meta-analysis are treated equally. Indeed, an effective meta-analysis will apply differential weight to the findings of studies that are more methodologically rigorous (e.g., better sampling techniques, more accurate measurements of key variables, or greater inclusion of appropriate control variables). This feature not only allows for a more accurate estimation of a general effect size

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but can also serve to direct future research to ensure maximal methodological rigor and accurate estimation of effect sizes.21

For the purposes of the current discussion the average effect size reported in a meta- analysis of behavioral genetic research refers to each of the three components of the equation of phenotypic variance (i.e., h2, c2, and e2). Therefore, a meta-analysis of behavioral genetic research on antisocial behavior will provide the overall estimates of heritability (genetic factors; h2), shared environmental effects (c2), and nonshared environmental effects (e2) on the development of antisocial phenotypes. Currently, there are four meta-analyses that have assessed the overall estimates of these components in terms of the development of antisocial phenotypes (Mason and Frick, 1994; Miles and Carey, 1997; Rhee and Waldman; Ferguson,

2010).22 Given that these meta-analyses were conducted at different time periods there is a relative lack of overlap in the studies that were included in each meta-analysis. Additionally, each subsequent meta-analysis refined the approach taken by the prior meta-analysis such that there is a progressive increase in the rigor of each study. Consequently, to the extent that the four meta-analyses converge in their findings one’s confidence in the findings is increased.

The first meta-analysis of behavioral genetic studies assessing the relative influence of genetic and environmental factors on antisocial phenotypes was completed by Mason and Frick

(1994). Consistent with the meta-analysis technique, Mason and Frick outlined their selection procedures for including studies in their analysis. The purpose of selection criteria and the

21 Not all meta-analyses are equal in methodological value. There are a number of problematic limitations with meta-analyses that can lead to inaccurate estimations of overall effect sizes. For a review of these limitations and the various statistical methods available for researchers conducting meta-analyses see Cooper (2010). 22 Informative systematic literature reviews have also been published. These reviews do not provide a statistical analysis of a body of research like a meta-analysis; rather, they provide an in-depth overview of studies to highlight overall findings and general themes. Three reviews of biosocial research on antisocial behavior that are of particular value are Raine (2002), Moffitt (2005), and Baker, Bezdjian, and Raine (2006). 88

subsequent refinement of studies is to ensure that there is consistency in terms of the type of study included in the analysis (Cooper, 2010). Consistency in study type is key in a meta- analysis as the purpose of the method is to provide an overall summary of a specific component of a body of research and systematic inconsistencies will lead to inaccurate conclusions (Cooper,

2010). Consequently, Mason and Frick (1994) excluded studies that were published before 1975 as those studies did not report the statistics necessary to conduct the meta-analysis. Additionally, the authors included studies up to 1991 as it was the end of their data collection period. The authors also included only those studies which employed a methodology wherein either MZ twins and DZ twins were compared or adoptees were compared to biological and adoptive parents (in terms of antisocial behavioral phenotypes). The authors further excluded any study which did not measure antisocial behavior (e.g., antisocial personality, aggression, or criminal activity) and any study which only included forms of psychological disturbances (e.g., studies which looked only at substance abuse were excluded whereas studies which had other forms of antisocial behaviors and substance abuse were included). The authors included only those studies which compared the respondents (MZ and DZ twins or adoptees and biological and adoptive parents) on the same type of antisocial behavior and excluded studies which had overlapping samples with studies already included in the analysis. Finally, the authors removed studies which reported their results in a way that was not conducive to the calculation of overall an effect size. The 15 studies that met the inclusion criteria accounted for over 4,000 kinship pairs from both twin and adoption studies. The overall findings of the meta-analysis support the claims made in the above discussion. The average heritability estimate of antisocial behavior across the 15 studies was .48. In other words, almost half of the variance in antisocial behavior across the studies was accounted for by variance in genetic factors. In addition, the authors

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found that estimates of heritability for severe antisocial behaviors (h2 = .45) was much greater than for nonsevere forms of antisocial behavior (h2 = .00). The authors noted that the heritability estimates were not affected by such study characteristics as sex, age, or the country from which the sample was derived. The authors did note some fluctuations in the estimate of heritability based on methodological variables but the only difference that was statistically significant was the sample type (i.e., clinical [h2 = .53] versus community volunteers [h2 = .20]).

Behavioral geneticists Donna Miles and Gregory Carey (1997) conducted the second meta-analysis of behavioral genetics research on antisocial phenotypes. The analysis included a dual-study approach wherein the authors examined the critiques of selective placement in adoption studies and the equal environment assumption in twin studies (see above for a description of these issues). The meta-analysis portion of the study was completed in the second half of the paper. In terms of their assessment of the two assumptions, Miles and Carey (1997) concluded that models which do not include measures of heritability do not fit the observed data as well as models with such measures (i.e., genes are a critical component) and that selective placement (even the implausible perfect selective placement) and incorrect assumptions of equal environments do not substantially reduce the effect of genetics. Therefore, the authors concluded that the assumption of minimal impact of selective placement on heritability estimates and the equal environment assumption are safely made in behavioral genetics research using adoption or twin studies. In their meta-analysis portion Miles and Carey (1997) restricted their inclusion to studies that utilized a measure of aggression, hostility, or antisocial behavior.

Additionally, they included studies which employed scales that are used to predict . These criteria resulted in the inclusion of 24 studies covering a variety of behaviors related to aggression. The authors concluded that about 50 percent of the variation in aggression

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was due to variance in genetic factors. The authors assessed the influence of a number of moderating variables (age, sex, measurement tool, and methodological rigor) and found that heritability estimates varied by age and sex with males exhibiting greater heritability estimates than females and heritability estimates increasing in effect size with age. The authors also highlighted the importance of multiple observational methods in measuring behavioral outcomes, as well as the need for longitudinal data in order to track the potential changes over time in aggression. In summary, although the heritability estimates varied by sample type

(males/females/age) and methodology (measurement type) estimates of heritability never dropped below 20 percent (range: h2 = .22 to h2 = .78).

The third meta-analysis of behavioral genetic research on antisocial phenotypes was conducted by Rhee and Waldman (2002). The authors analyzed 42 twin studies and 10 adoption studies for a total of 55,525 pairs of participants. The exclusion criteria employed by the authors was extensive and was based on such study characteristics as construct validity (e.g., studies examining topics related to antisocial behavior like hostility were excluded – only studies which examined antisocial phenotypes determined by clinical diagnoses, official records of criminality, observations or self-reports of aggression, and non-clinical antisocial behavior were included), incomplete reporting of correlational statistics, using assessments of related disorders which interfered with the assessment of antisocial behaviors, and overlapping or nonindependent samples. Similar to the other two meta-analyses above, Rhee and Waldman (2002) assessed the potential influence of a number of moderating variables including sex, age, zygosity determination, assessment method, and operationalization techniques. While Rhee and

Waldman’s (2002) meta-analysis followed the previous two in this regard it also differed in a number of ways from Mason and Frick (1994) and Miles and Carey (1997). For example, Rhee

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and Waldman’s (2002) analysis was more comprehensive in terms of number of studies included, it employed a broader conceptualization of antisocial behavior, included a wider range of potential moderating variables, and was more extensive in its assessment of such behavioral genetic concepts as gene-environment interplay. Making use of univariate twin models (see below) and genetic model fitting techniques, the authors concluded that 41 percent of the variance in antisocial behavior was explained by variance in genetic factors (both additive and nonadditive genetic influence). The authors also found that nonshared environmental effects accounted for 43 percent of the variance and shared environmental effects accounted for a modest16 percent.

In assessing the effects of potential moderators, the authors found that operationalization of the dependent variable (i.e., how antisocial behavior was measured) affected the magnitude of estimates of heritability. However, estimates of heritability never dipped below 30 percent of the variance (range: h2 = .33 to h2 = .50). Notably, the estimate of shared environmental effects also varied but never reached a magnitude beyond 22 percent (range: c2 = .06 to c2 = .22). Recall that traditional criminological analyses (i.e., SSSMs) focus on variables that are often considered components of the shared environment. Therefore, according to this meta-analysis depending on the method of operationalization of antisocial phenotypes criminological research employing a

SSSM will be assessing a variable that will be related to, at best, about 20 percent of the variance in the outcome. Such a finding speaks to the futility of an SSSM approach to understanding criminal and antisocial behavior.

Another moderator that Rhee and Waldman (2002) found to affect estimates of heritability was the assessment method employed in the studies. Overall, the authors found that self-reports of antisocial behaviors (h2 = .39, c2 = .06, e2 = .55) produced lower heritability

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estimates than observational reports (h2 = .53, c2 = .22, e2 = .25). Determination of zygosity was also found to be a significant moderator in the analyses. Recall that zygosity (i.e., MZ versus

DZ) has been determined by blood grouping methods and through questionnaires (although DNA analysis can also be employed, Rhee and Waldman did not include any studies which employed this method of zygosity determination). The authors found that studies using blood grouping had higher estimates of heritability (h2 = .47) than those studies using a questionnaire method (h2 =

.39).23 The final moderator found to affect the heritability estimates of antisocial behavior was age.24 The authors employed a trichotomous coding scheme for age (average [mean or median] age was used for demarcation) that included children, adolescents, and adult. The overall pattern of change showed that heritability estimates decreased with age (children: h2 = .46; adolescents: h2 = .43; adults: h2 = .41).25 While the authors found that these various moderators significantly affected the magnitude of the heritability (and environmental) estimates they cautioned that the moderators they examined may also be confounded with one another. As a poignant example, they note that the studies which employed observational methods of assessment (recall these resulted in higher estimates of both heritability and shared environmental effects than self- reports) were more often found in studies that included samples of children (which also produced higher estimates of heritability and shared environmental effects). The overall conclusion to be

23 The interpretation of this finding is worth comment. In short, those studies which employed a more accurate estimation of zygosity (blood sample versus self-report, for example) lead to higher estimates of heritability of antisocial behaviors. Therefore, studies which employ less accurate determinations of zygosity will produce attenuated estimates of the effect of genetic factors on antisocial behaviors. 24 The authors found that sex was only a moderator when studies which included only one sex and employed inconsistent operationalization and assessment methods (i.e., different methods for either sex) were assessed (males: h2 = .38; females: h2 = .41). However, when these studies (i.e., studies which examined antisocial behavior in only one sex) were removed from the analysis sex was no longer a significant moderator (i.e., there was no significant difference in the heritability estimates between the sexes; males: h2 = .43; females: h2 = .41). 25 Additionally, the authors noted that estimates of shared environmental effects also decreased over time, but to a much greater extent than heritability (children: c2 = .20; adolescents: c2 = .16; adults: c2 = .09). Once again, this particular finding highlights the significance of the problems with an SSSM approach which focuses solely on variables emanating from the shared environment. 93

derived from the meta-analysis, however, is that genetic effects accounted for approximately half of the variance in antisocial behavior with nonshared environmental effects taking up the majority of the remaining variance.

The final meta-analysis assessing the relative influence of genetic and environmental effects on antisocial behavior was conducted by Ferguson in 2010. Importantly, and directly relevant to the current project, Ferguson (2010) situated his review of the literature within the over-arching theoretical scaffold provided by evolutionary psychology. In other words,

Ferguson (2010) explicitly recognized the consilience between an evolutionary approach and a behavioral genetics methodology. The inclusion criteria employed by Ferguson (2010) fell into three broad components: first, the author limited the range of inclusion to more recent studies than past reviews (i.e., 1996-2006); second, he included only those studies which had clear measures of some aspect of antisocial, violent, or aggressive behavior (notably, Ferguson indicated that his criteria in this regard were identical to Rhee and Waldman, 2010); and three, only those studies which employed a twin or adoption study methodology were included. These criteria yielded 38 studies that provided 53 separate effect sizes. In total, these studies provided almost 100,000 cases (N = 96,918). Following the past meta-analyses, Ferguson (2010) also tested for the potential moderating effects of age, sex, and operationalization of antisocial behavior.

Overall, Ferguson’s (2010) meta-analysis of behavioral genetic studies on antisocial behavior produced a heritability estimate of h2 = .56. In other words, 56 percent of the variance in antisocial behaviors across the 38 studies was accounted for by genetic factors. Additionally,

Ferguson noted that shared and nonshared environmental effects accounted for 11 and 31 percent

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of the variance, respectively.26 In his analysis of the influence of potential moderating variables,

Ferguson (2010) illustrated that age served to affect the estimates of heritability. Specifically, he found that effect sizes of genetic effects were greater in children (h2 ≈ .75) than in adults (h2 ≈

.58).27 Not surprisingly, this result dovetails with that found by Rhee and Waldman (2002).

However, as noted by Rhee and Waldman (2002) the effect of age as a moderator should be interpreted with caution as the coding scheme employed by both analyses (i.e., trichotomization of age into child, adolescent, and adult) may impact the manner in which age acts as a moderator on estimates of heritability. Ferguson (2010) also noted that age affected the estimates of nonshared environmental effects, working to increase the magnitude of nonshared environmental effects from childhood (e2 ≈ .50) to adulthood (e2 ≈ .70). The author argued that this considerable change over time is likely reflective of the “gradual accumulation of non-genetic influences such as head injuries, , as well as socialization, and potentially increased agency” (Ferguson, 2010: p.175). While Ferguson (2010) did not find a statistically significant moderating effect of age on shared environmental effects he did note that the effect size reduced across the age categories (childhood: c2 ≈ .25; adulthood: c2 ≈ .20 ). Ferguson (2010) also found that operationalization moderated the results, showing that estimates of heritability were higher when broader measures of antisocial behaviors were employed (relative to such diagnostic tools as the DSM-IV). Operationalization method also significantly moderated the effect of nonshared environmental factors but did not moderate shared environmental effects. Overall, Ferguson’s

26 The total variance explained does not equal 100 percent due to the author’s use of rounding during the effect size calculation for each individual study. 27 The magnitudes of the heritability estimates herein are derived from Figure 1 in Ferguson’s (2010) article and are therefore close approximations as he did not provide the specific effect sizes by moderating variable. Additionally, Figure 1 represents the pooled effect sizes of the three components (i.e., genetics, shared environment, and nonshared environment) and therefore do not represent the percentage of variance explained overall. 95

(2010) review illustrates that genetic and nonshared environmental factors carry the bulk of the weight in terms of the etiology of antisocial behaviors.

In summarizing the results of the meta-analyses there are at least three key findings.

First, the meta-analyses together cover a relatively long time period (1975 to 2006), incorporate over one hundred individual studies, and include hundreds of thousands of respondents.

Consequently, the overall results derived from these studies can be considered substantially robust. Second, the meta-analyses consistently illustrate that roughly half of the variance in antisocial behaviors is accounted for by variance in genetic factors (Mason and Frick: h2 = .48;

Miles and Carey: h2 = .50; Rhee and Waldman: h2 = .41; Ferguson: h2 = .56; average heritability estimate across all four studies: h2 = .49). Therefore, any analysis which does not control for the effect of genetic factors on the variance in antisocial behaviors is seriously misspecified.

Additionally, these findings render the results of past and current traditional criminological analyses of antisocial behaviors uninterpretable as one is left unaware of how genetic factors would have influenced the results of a considerably vast amount of literature employing SSSMs.

Finally, the meta-analyses also illustrate the importance of environmental factors in the etiology of antisocial behaviors. This final point bears some further discussion.

The finding illustrating the significant effects of environmental factors has three crucial points of importance. First, it illustrates that a biosocial approach explicitly includes and highlights the importance of environmental factors. In other words, the critiques once levied against a biological approach as being ignorant of social factors are no longer tenable. Second, the finding highlights the importance of controlling the effects of genetics in order to properly assess the effects of environmental factors. In other words, without controlling for genetic factors the effects of environmental components such as parenting practices, delinquent peers,

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and environmental contagions on antisocial behavior cannot be properly assessed (Beaver,

2009). This point has direct relevance to the methodology employed in the current study (see

Chapter 5). Finally, the meta-analyses bring to light the importance of demarcating environmental effects into shared and nonshared environments. Given the consistent finding that nonshared environmental factors carried the vast amount of weight in terms of non-genetic effects it is crucial that research on antisocial behavior appropriately incorporate recognition of the different types of environments. Therefore, those methods encompassed in an SSSM approach are inadequate to effectively assess the etiological constituents of antisocial behaviors.

3.3.2 Biosocial Research on Sexual/Reproductive Behaviors

The review provided in this section is considerably briefer than the review of the relevant meta-analyses of a biosocial approach to antisocial conduct. There are two primary reasons for this relative paucity; first, in comparison to antisocial behaviors there have been fewer biosocial assessments of adolescent and adult sexual/reproductive behaviors. There is a vast literature on the biological influences (e.g., hormonal, neuropsychological) on sexual behavior but relative fewer studies have applied a biosocial perspective (Lyons et al., 2004). Recall that a biosocial approach recognizes that phenotypes are influenced by both biological/genetic factors and environmental factors, and much of the existing biological research on sexual and reproductive behaviors lacks such an inclusion. Second, when biosocial approaches have been applied to sexual or reproductive behaviors they tend to include sexual dysfunctions or behaviors considered a part of a constellation of risky conduct (e.g., early sexual debut or number of sexual partners).

As indicated, biosocial analyses of sexual behaviors have tended to focus on components such as age of sexual debut and number of sexual partners. For example, the earliest biosocial 97

assessment of the relative influence of genetic and environmental factors on age of sexual debut was conducted in 1976 by Martin and colleagues. This study was based on a modest sample of

246 twin pairs (NMZ = 134, NDZ = 112) and illustrated that nonshared environmental factors were most important in explaining the variance in age of sexual debut among the twins sampled. This study was followed by more recent analyses conducted in a variety of studies. Dunne et al.

(1997) found that genetic factors accounted for 72 percent of the variance in age of sexual debut in males and 49 percent of the variance in females. Shared environment estimates were .00 for males and .25 for females, with nonshared environmental factors accounting for the remaining variance (i.e., 28 percent for males and 26 percent for females). This study was followed by

Rodgers and colleagues (1999) who employed kinship pair linkages within the NLSY dataset.

Overall, the Rodgers et al. (1999) study found that variance in age of sexual debut was accounted for primarily by nonshared environmental effects (55 percent), followed by genetic factors (37 percent), and shared environmental factors (8 percent). Assessing only males from the Vietnam

Era Twin Registry, Lyons et al. (2004) also found that nonshared environmental effects accounted for the majority of the variance in age of sexual debut. Specifically, their analyses produced the following estimates: e2 = .40, c2 = .27, and h2 = .20. Similar results were obtained in a more recent study conducted by Bricker et al. (2006) who assessed the relative influence of genetic and environmental factors on age of sexual debut among adopted and non-adopted children in the Colorado Adoption Project data. Bricker et al.’s (2006) analyses found that genetic factors accounted for 28 percent of the variance in age of sexual debut, while shared and nonshared environmental factors accounted for 24 percent and 48 percent, respectively. Overall, these studies indicate that genetic factors have an influence on the variance in age of sexual debut but it tends to be less than that of nonshared environmental factors.

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The other main area of sexual behavior focused upon by researchers employing a biosocial methodology has been the number of sexual partners, although this topic has not received as much attention in the literature as age of sexual debut. In terms of behavioral genetic modeling, three studies have been conducted and their results converge on the influence of nonshared environmental effects but not on genetic effects. First, Hershberger’s (1997) secondary analysis of number of sexual partners (his primary analysis was the estimation of genetic and environmental effects on sexual orientation) illustrated that 100 percent of the variance was accounted for by nonshared environmental effects. The aforementioned study by

Lyons et al. (2004) included a decomposition of the variance in number of sexual partners. In stark contrast to Hershberger (1997), Lyons et al. (2004) found that genetic factors accounted for

30 percent of the variance in the number of sexual partners with the remaining variance being accounted for by nonshared environmental factors (i.e., c2 = .00). The third study was conducted by Cherkas and colleagues (2004) who examined over 1,600 female twin pairs from the United

Kingdom (making their sample unique in that it focused on females and was non-American).

The authors found that genetic factors accounted for 41 percent of the variance in number of sexual partners, while shared and nonshared environmental factors accounted for 13 percent and

46 percent, respectively. As mentioned, these studies illustrate different findings in terms of the effect of genetic factors on the variance in number of sexual partners but they all point to some genetic influence as well as the importance of the nonshared environment.

Sexual behavior as measured by the number of sexual partners has also been examined from a molecular genetics point of view. Given that the current project is more concerned with behavioral genetics, the review of these studies will be brief. However, they are noteworthy as they point to the influence of genetic factors on the variance in the number of sexual partners.

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The findings from this research have been mixed. For example, the aforementioned study by

Cherkas et al. (2004) also included a genome-wide linkage scan in order to link a specific gene with number of sexual partners in their UK female twin sample. While the authors were able to find a linkage with number of sexual partners and chromosome 7 they were unable to identify a particular locus that associated with the outcome (i.e., number of sexual partners). The authors also noted that a gene found to associate with sexual behavior in non-human animals, the vasopressin receptor gene (AVPR1A), was not linked to variation in the number of sexual partners in their sample as was expected. Similarly, Halpern, Kaestle, Guo, and Hallfors (2007) failed to support their expectation of positive association between two dopamine polymorphisms

(DRD4 and DRD2) and one serotonin polymorphism (5HTT) and number of sexual partners.

The authors found that possession of the risk allele for the DRD2 gene and the 5HTT were both associated with fewer rather than the expected greater number of sexual partners. In contrast to the Halpern et al. (2007) study, Beaver et al., (2010) did find an association between a dopamine genetic polymorphism and number of sexual partners, albeit for a different dopaminergic gene

(DAT1). Specifically, the authors found that those respondents who possessed the risk allele of the DAT1 gene also reported a higher number of lifetime sexual partners even after controlling for the influence of age and race.28

The above review indicates that while biosocial researchers have convincingly illustrated that genetic factors influence sexual behaviors such as age of sexual debut and number of sexual partners there is still research that is required in order to identify which specific genetic factors

28 Interestingly, the discrepancy in result between these last two studies may have been due to the different operationalization of number of sexual partners in either study. While Beaver et al. (2010) employed lifetime sexual partners Halpern et al. (2007) assessed sexual partners in only the past 12 months. Further research is required to see if in fact this difference led to the discrepancy (it could also be a function of the different genetic polymorphisms employed) but on the face of it, one would expect that a longer time period would provide for a greater collection of opportunities within which the effect of the genetic influence could manifest. Importantly, the current study employs lifetime measures of sexual partners as an outcome (as well as sexual partners as a juvenile). 100

are most influential. While these two components of sexual/reproductive behavior have received the most empirical attention from researchers employing a biosocial approach, they do not represent the entirety of such research. For example, in an early biosocial study of relationship behavior McGue and Lykken (1992) assessed the relative influence of genetic and environmental factors on the risk of a marriage ending in divorce. Examining a large same-sex twin sample (N

= 1,516; NMZ = 722, NDZ = 794) the authors found that the proportion of variance in risk of divorce due to genetic factors was .52 for males and .53 for females (.53 for the combined sample). The authors also found that for both males and females the remaining variance was due to nonshared environmental factors while shared environmental factors did not account for any of the variance in the risk of divorce. This finding illustrates that reproductive/relationship outcomes such as marital success can also be influenced by genetic factors (importantly, the current study includes two measures tapping marital outcomes).

The final area of sexual behavior to be reviewed relates to fertility, or the production of offspring. A number of biosocial researchers have assessed the relative influence of genetic and environmental factors on fertility and have found considerable support for genetic influence.

One such study was completed by Rodgers, Bard, and Miller (2007) who employed a longitudinal approach to examine the number of children produced by respondents in the NLSY dataset. Assessing the number of births over time, Rodgers et al. (2007) illustrate that the majority of the variance in fertility over the life course is due to nonshared environmental factors. However, the authors note that their results support a strong genetic influence for early

(i.e., up to age 20) fertility (h2 = .65) which then drops significantly over time. Indeed, beyond the early fertility category heritability estimates were effectively zero. Supplementing these analyses in another study, Miller, Bard, Pasta, and Rodgers (2010) illustrated that variance in the

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final number of children produced is also due primarily to nonshared environmental factors (e2 =

.84) with the remaining variance due to genetic factors (h2 = .16).

In summarizing the above review two primary overall findings are evident in the current biosocial literature regarding sexual and reproductive behaviors. First, it is unmistakable that genetic factors play an important role in the etiology of these outcomes. Although the effect sizes vary, the influence is almost ubiquitously present. Consequently, studies which do not incorporate controls for genetic factors are incomplete. Second, it appears that the nonshared environment is disproportionately responsible for variance in the sexual and reproductive measures. Consequently, much of the material from which to form firm etiological footing in terms of sexual/reproductive behaviors will be derived from analyses that isolate components of the nonshared environment. This recognition provides a significant impetus for the analytical strategy of the current project wherein the final two steps of the analyses isolate antisocial behavior in adolescence as a component of the nonshared environmental effect on sexual and reproductive outcomes (see Chapter 5).

3.3.3 Summary of Biosocial Research on Antisocial Behavior and Sexual/Reproductive

Behaviors

The above discussion of the methods and assumptions of behavioral and molecular genetics as well as the relevant meta-analyses and individual studies highlights a number of key points relevant to both criminology and to the current study. First, it is undeniable that genetic factors are important in the etiology of human behavior, including antisocial conduct and sexual/reproductive behaviors. Second, the effect of genetic factors alone will likely always be less than the interactive effect of genes and the environment (the opposite is true as well – the effect of any single environmental factor will likely be less than the interactive effect). Third, 102

although environmental effects are crucial, it is likely the nonshared environment that comprises the greatest environmental effect on any single phenotype. Fourth, criminological assessments that ignore the influence of genes or the nonshared environment (i.e., continue to focus solely on the shared environment and employ SSSMs) will produce research that is misspecified and uninterpretable. This point has become more poignant with the current increase in the number of studies which have employed a biosocial approach to criminology. Biosocial researchers have shown that a number of strongly held assumptions of criminological theories may be incorrect due to the continued use of SSSMs and the exclusion of a genetically sensitive research design.

Biosocial critiques have been levied against such criminological stalwarts as the proposed causes of self-control (Beaver, Wright, and DeLisi, 2007; Wright and Beaver, 2005), the formation and influence of delinquent peers (Beaver et al., 2009; Beaver, Wright, and DeLisi, 2008; Wright et al., 2008), the age-crime curve (Kanazawa and Still, 2000), the selection into and influence of different life events (so called ‘turning points’; Barnes and Beaver, 2012; Moffitt, 2005), the concentration of crime within areas (Beaver and Wright, 2011) and families (Beaver, 2013;

Boutwell et al., 2012), and the causes of adolescent-limited and life course offending typologies

(Barnes and Beaver, 2010; Barnes, Beaver, and Piquero, 2011; Barnes et al., 2011). Biosocial critiques have not been limited to only the tenets of theory but have also illuminated the faulty assumptions of an SSSM approach to criminal justice processing (Beaver et al., 2013) and treatment strategies (Wright and Cullen, 2012). Fifth, as highlighted in Ferguson’s (2010) review although biosocial research has been instrumental in advancing the science of criminological analysis, it is lacking a theoretical scaffold upon which to guide future research.

Ferguson (2010) argued that an evolutionary approach to behavioral and molecular genetic research could help organize current biosocial research and further influence future research.

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The current project is a reflection of this assertion and consequently, the next chapter outlines an explicit attempt to integrate evolutionary theory into biosocial criminological analyses.

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CHAPTER 4

EVOLUTIONARY CRIMINOLOGY: AN INTRODUCTION AND OVERVIEW

He who does not understand the uniqueness of individuals is unable to understand the working of natural selection.

-Ernst Mayr (1982, p.46)

The above quote by eminent evolutionary biologist Ernst Mayr illustrates the approach emphasized in this section of the current project. The evolutionary criminology approach holds that individual differences which can influence antisocial conduct must be situated within the overarching processes of natural and sexual selection. Additionally, in order to assess and empirically test the claims made by evolutionary approaches individual differences (i.e., phenotypic variance) must be highlighted. The overall approach is illustrated in the simplified theoretical model displayed in Figure 4.1. As Figure 4.1 shows, comprehensive understanding of phenotypic variance begins with an understanding of the evolutionary processes which underlie the human psyche (or human nature; Pinker, 2002). Evolutionary psychology provides the initial theoretical stance and generates testable hypotheses regarding why certain behavioral repertoires exist (Tooby and Cosmides, 1992). The next segment of the model is represented by life history theory. As noted and evidenced in Figure 4.1 life history theory is a mid-level evolutionary theory. Life history theory is employed to further refine the hypotheses of evolutionary psychology. Additionally, life history theory (especially that derived from Rushton’s work) provides operationalization and conceptualization techniques to test the evolutionary arguments of its claims. Importantly, life history theory is also the level at which individual differences are recognized and incorporated into an evolutionary approach. These hypothesized individual differences are then assessed at the next level of analysis using behavioral and molecular 105

Evolutionary Psychology

Life History Theory

Behavioral and Molecular Sociocultural and Genetics Structural Approaches

Comprehensive Understanding of the Etiology of Human Behavioral Phenotypes (Including Antisocial Behavioral Phenotypes)

Figure 4.1: Simplified theoretical model of an evolutionary criminology approach.

genetics approaches. As can be seen, the contributions of sociologically based research are also integrated into this model. Given the consistent finding of non-genetic environmental effects on the variance in numerous phenotypes the inclusion of such factors derived from a socio- cultural/structural approach is required. Note, however, that these effects are only properly derived after the effects of genetics have been statistically controlled.

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The simplified model illustrated in Figure 4.1 is a representation of similar arguments echoed elsewhere in the relevant literature. For example, Ferguson (2010) notes, “behavioral genetics research actually provides evidence in support of the mechanism through which the natural selection of evolutionary psychology influences human behavior, both in regards to commonalities and differences” (p. 163). The point made by Ferguson highlights the evolutionary criminology approach employed in the current project. Similar points have been expressed by both evolutionary psychologists (Buss, 2011) and behavioral genetics researchers

(Bouchard and Loehlin, 2001; Segal, 2010). The value of an integrative approach to understanding human behavior, including criminal behavior, is clear (Cullen, 2011).

Consequently, the current study represents a synthesis between evolutionary psychology, life history theory, and behavioral genetics. In short, the current project employs the methods of behavioral genetics to explore hypotheses derived from an evolutionary psychology approach and refined and organized by life history theory to assess a topic derived from criminology. The conceptual and methodological approach of the study is further demonstrated in Figure 4.2. As illustrated, the current project, and the new field of evolutionary criminology, is derived from the intersection of the three overarching areas of inquiry. It should be noted that a behavioral genetic methodology is not the only manner by which to test hypotheses derived from an evolutionary behavior genetics approach (see Keller et al., 2011). However, due to the nature of the sample employed and the research questions of interest a behavioral genetic methodology was deemed most appropriate (this issue is addressed again in Chapter 7). The next chapter outlines the data, methods, and analytical plan employed in the current project.

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Evolutionary

biology (LHT)

EvCrim

Behavioral Criminology

genetics

Figure 4.2: Conceptual framework for an evolutionary criminology (EvCrim) approach.

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CHAPTER 5

METHODS

The discussion thus far has illustrated two main points: 1) the methods employed in contemporary criminology (i.e., SSSMs) are inadequate due to their inability to account for the influence of genetic factors, and 2) when genetic factors are taken into account there is a high likelihood of obtaining results that are vastly different from a research design that is not genetically sensitive. More specifically, genetically sensitive designs have consistently indicated that SSSMs overestimate the influence of environmental factors (i.e., shared environmental factors); additionally, such designs have shown the influential effects of genetic factors (Beaver,

2009; Harris, 2009; Rowe, 1994). Consequently, a biosocial approach is required when assessing the relative influence of genes and environmental factors for various phenotypes.

Additionally, the discussion from Chapters 3 and 4 highlight the benefits to including recognition of individual differences in evolutionary analyses. Accordingly, the current chapter will outline the methods by which these points will be taken into account in testing the inquiries raised in the aforementioned research questions. The chapter will begin with an overview of the sample employed in the current study. Next, a description of the measures will be provided. The chapter will conclude with coverage of the statistical methods to be used in the current study.

5.1 Data

Data for the current study came from the National Longitudinal Study for Adolescent

Health (Add Health; Harris et al., 2009). The Add Health is a nationally representative prospective study of American youth enrolled in grades 7 through 12 during the 1994-1995 school year. The cohort was followed for a period of approximately 14 years and the study

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included four waves of data collection. The Add Health contains a vast amount of information on a wide range of issues including social interactions, economics, psychological and physical well- being as well as information on various behaviors, peer relations, and neighborhood factors

(Harris et al., 2009). Details of the sampling procedure are described elsewhere (see Harris et al., 2009 or Harris, Halpren, Smolen, and Haberstick, 2006), but for the purposes of the current study is important to note that the sampling procedure involved the selection of high schools across a range of urban, suburban, and rural areas in the country. Additionally, the original sample consisted of 80 high schools that were stratified by region, urbanicity, school type, ethnic mix, and size. Consequently, the Add Health contains data from a wide variety of contexts within America.

The first wave of data was collected during the 1994-1995 school year and involved a multi-step process. First, over 90,000 students from the sampled schools received in-school questionnaires. The in-school questionnaires collected information on such factors as school context, school activities, future expectations, various health conditions, and friendship networks. The in-school questionnaire also served as a method for selecting students for the supplementary in-home questionnaire. The second step, therefore, involved selecting students from school rosters and the in-school questionnaire for inclusion in a 90-minute in-home interview which served as the Wave 1 in-home sample. Importantly, the in-home sample was selected in such a way as to be self-weighting and nationally representative of American youth in grades 7 to 12. Also included within the first wave of data collection was a questionnaire given to the parent (usually the mother) or primary caregiver of the respondent. The size of the in-

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home portion of data collection sample, after the addition of over-sampled groups, is N = 20,745 adolescents and 17,670 parents (Harris et al, 2006).29

Germane to the current study, the first wave of data collection also included an over- sampling of twins and siblings for the in-home sample (Harris et al., 2006). If a selected youth indicated that he or she was a twin during the in-school portion of the survey they were selected for the in-home sample at a rate of 100 percent. Additionally, different forms of kinship pairs were also over-sampled (e.g., half-siblings, foster children, stepsiblings, and adopted adolescents). In total, the first wave of data contained over 3,000 pairs of adolescents with varying levels of genetic relatedness (Harris et al., 2006). Consequently, the Add Health presents a collection of data specifically designed for a behavioral genetic analysis and is especially suited for the analyses included in the current study.

In addition to the size and constituents of the sample, the content covered in the Add

Health study also provides advantages for its use in the current study. The in-home interview covered a myriad of topics including sexual behaviors, drug and alcohol use, physical activities and general interests, as well as questions about antisocial and delinquent behaviors. The survey design employed the use of computer-assisted interviews (Czaja and Blair, 2005). This procedure involved the interviewer reading questions from a laptop aloud to the respondent and the answers were then recorded on the computer. Importantly, for those questions that were deemed to be of a sensitive nature (e.g., sexual and antisocial behaviors) the respondent read and keyed in his or her responses directly using the laptop. This method of interviewing has been

29 The initial core sample of youth included in the in-home sample contained N = 12,105 cases. A number of different groups based on ethnicity, genetic similarity, adoption status, and disability were over-sampled. After these over-sampled groups were added to the core sample the overall in-home sample reached N = 20,745 cases (Harris et al., 2006). 111

shown to reduce the amount of bias associated with collecting information on behaviors and attitudes considered to be sensitive or personal in nature (Turner et al., 1998).

After an elapse of approximately one year, the second wave of data collection commenced. Wave 2 data collection took place in 1996, beginning in April and continuing through August. Respondents who were enrolled in grades 7 through 11 during the Wave 1 interviews and those from the oversampled groups were reinterviewed at Wave 2. Those respondents who were in the 12th grade or part of the disabled sample at Wave 1 were not reinterviewed at Wave 2. All of the Wave 2 data was collected via in-home interviews and included almost 15,000 respondents, a 90 percent response rate (N = 14,738; Harris et al., 2006).

Given that only one year had passed for the majority of the respondents most of the questions at

Wave 2 remained relatively unchanged from Wave 1. The questions sought information on issues and topics germane to adolescence and covered a similar variety of topics as Wave 1.

Given the relative proximity in terms of time, data from waves 1 and 2 represent the adolescent period of the Add Health study. In addition to the information about respondents themselves, the first two waves also include information on school context, family context, peer and romantic and sexual relationship networks, spatial networks, and community context (Harris,

2006). The networks data allows for the analysis of similarities and differences amongst peer and sexual and/or romantic relationships. Such data can be employed in gene-environment interplay studies as outlined in Chapter 2. Additionally, the context data provides for inclusion of macro level variables of interest in order to assess contextual influences on outcomes of interest. The contextual data came from multiple sources. The school context data came from school administrators as well as the in-school self-report surveys. Identification of the spatial location of a respondent’s home came from hand-held global positioning system (GPS) devices

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used by the interviewers during the in-home interviews, or from recording the respondent’s actual address. Finally, the community/neighborhood contextual data came from linking the spatial data to over 2,500 variables derived from the US Census, the Centers for Disease Control and Prevention, the Federal Bureau of Investigation, the National Council of Churches, and the

National Center for Health Statistics (Harris, 2006).

The third wave of data is referred to as the ‘transition to adulthood phase’ and took place between August 2001 and April 2002, approximately six years after the Wave 1 interviews

(Harris, 2011). As indicated by the moniker for this period, the majority of respondents in Wave

3 were young adults ranging in age from 18 to 28 years. Consequently, the questionnaires were altered to include age-appropriate questions. For example, the interviews included questions on such topics as the labor market, sexual and romantic relationships, higher education, marital status, parenting, civic participation, community involvement, and criminal behaviors (Harris,

2011). In addition to these new questions, Wave 3 also saw the inclusion of various biomarkers collected from respondents. Biological data came from urine (analyzed for the presence of sexually-transmitted diseases), saliva (for DNA analyses), and saliva collected specifically for detecting the presence of HIV antibodies (Harris et al., 2009). Efforts were made to both increase the response rate of respondents from Wave 1 and decrease missing data on questions of a sensitive nature. These goals were accomplished by taking extra steps to locate respondents

(e.g., conducting interviews in jails or ) and by the use of laptop computers for the entire survey (Harris et al., 2006). The efforts to increase respondent inclusion resulted in a sample size of 15,197 respondents, a response rate of 77 percent (Harris et al., 2006).

The final wave of data was conducted in 2007 and 2008 when the respondents were 24 to 34 years of age. Continued efforts to increase response rates and reduce missingness were

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employed and as a result, Wave 4 included an 80.3 percent response rate (N = 15,701). As was the case for Wave 3 data collection, the questionnaire used in Wave 4 was altered to include age- appropriate questions. Additionally, the purpose of the final wave of data was to link the experiences observed in the earlier waves with outcomes related to social, economic, reproductive, psychological, and health circumstances in adulthood (Harris, 2011). As a result, respondents were asked a wide-array of questions related to these various circumstances.

Germane to the current study, respondents were asked a large number of questions related to sexual and reproductive factors. These questions were asked both in terms of lifetime and past

12 month timeframes. In addition, respondents also provided an increased number (relative to

Wave 3) of biological measures including anthropometric (height, weight, BMI, and waist circumference), cardiovascular (blood pressure and pulse), metabolic processes (lipids, glucose, and glycosylated hemoglobin), immune functioning (EBV), inflammatory processes (CRP), and genetic (10 candidate loci; Harris, 2011). Importantly, these biomarkers were collected from the entire Wave 4 sample. The outcome measures employed in the current study are derived from the fourth wave of data. Consequently, the current study employs a longitudinal research design and the respondents included are those who have non-missing data at the relevant waves (i.e., waves 1, 2, and 4).

5.1.1 Analytical Sample Creation

As illustrated above, the shortcomings of various behavioral genetic methodologies are overcome via the use of a combined methods approach. An example is the inclusion of family members in a twin-based approach. This approach integrates a wider variety of kinship pairs beyond only MZ and DZ twin pairs. Behavioral genetic studies have illustrated that the inclusion of non-twin kinship pairs provides a greater ability to more accurately detect common 114

environmental (i.e., shared environment) effects on phenotypes of interest (Medland and Hatemi,

2009). Therefore, studies which employ both twin and non-twin kinship pairs provide the most conservative tests of the influence of genetic factors on phenotypic variance. Given the inclusion of multiple types of kinship pairs nested within the Add Health, the current study employs such a combined approach. The various kinship pairs used in the current study are illustrated in Table

5.1.

As can be seen in Table 5.1, the information is provided for both individuals and pairs. The reason for this type of display is that the data for this study was restructured following what behavioral genetics researchers call a ‘double-entering’ procedure (Plomin et al., 2013). In order to conduct twin (kinship) pair analyses, a dataset must be structured such that each row represents a kinship pair and therefore, the twin pair becomes the unit of analysis (Beaver, 2009).

In order to restructure the dataset to meet this requirement, the two respondents within a kinship pair must be identified in some manner – this is typically done by selecting one respondent as twin 1 and the other as twin 2. While this application of twin number identification is typically random in data collection samples it still leaves open the possibility for bias if the twin identified as twin 1 is done so in a systematic manner. Consequently, behavior genetics researchers have identified multiple methods to avoid such a problem. One such method that is commonly used is the double-entering procedure. This procedure essentially creates a mirrored image of a dataset where the upper half has twin 1’s data appearing first followed by twin 2’s data (on the same row) and the lower half has twin 2’s data appearing first which is followed by twin 1’s data (on the same row). Given that analyses based on double-entered data can suffer from biases due to clustering effects, robust standard errors are employed in all analyses in the current study

(Kohler and Rodgers, 2001; Smith and Hatemi, 2012).

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The final analytical sample was derived by following a number of steps. First, the full Add

Health sample was reduced to kinship pairs only by dropping all respondents who were missing on a measure of genetic relatedness (this measure indicates that a respondent is a member of a kinship pair). The second step in the creation of the analytical sample was completed to best match the research questions guiding the analyses. As a reminder, in Chapter 1 it was noted that the current study seeks to assess the effect of antisocial conduct in adolescence on sexual and reproductive behaviors in adulthood. Consequently respondents who were 19 years or older at

Wave 1 were dropped from the sample. The cutoff point of 19 was chosen over 18 (the legal cutoff for adulthood) as many individuals who are 18 years old are still in high school and living the life of an adolescent. Additionally, recall that the Add Health is a school-based sample.

Therefore, individuals who are 19 years or older and still in high school may differ in some systematic way from the younger respondents at Wave 1. In order to best conform to the study’s research questions and avoid bias produced by the inclusion of adults (i.e., 19 years or older at

Wave 1) at Wave 1 the sample was limited by age at Wave 1. Third, the kinship pairs were then limited to those respondents who were an MZ twin, a DZ twin, a full sibling, or a half-sibling

(i.e., all cousin kinship pairs were dropped). Fourth, in order to reduce the influence of sex on the associations under study those respondents who were part of a different-sex kinship pair were also dropped from the sample. This step is a conventional step in behavioral genetic analyses

(Plomin et al., 2013). The final step of analytical sample preparation was based on the need for all kinship pairs to be identifiable by a family identification number. This measure is of crucial importance when creating the double-entered restructured dataset outlined above. Consequently, those respondents who were missing a family identification number were dropped from the sample. These steps resulted in an analytical sample of N = 2,344. A summary of the results of

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this process of analytical sample creation is provided in Table 5.2. Further breakdown of the analytical sample is provided in Table 5.3 which displays the sample composition by kinship pair type, genetic relatedness, and sex. Additionally, the age distribution of the analytical sample by wave is illustrated in Table 5.4. As indicated in Table 5.4, the majority of respondents were less than 19 years of age across the first two waves and were in their late-twenties at Wave 4.

Additionally, as is also illustrated in Table 5.4, the average span of time for the analytical sample is approximately 13 years between adolescence and adulthood.

5.1.2 The External Validity of the Add Health Twin Subsample

Recall from the discussion of twin studies that results of empirical analyses of twins may not be generalizable to the general population (i.e., non-twins). Recall as well that a number of phenotypes that have been associated with various behaviors were shown to be similar between twins and singletons (see Plomin et al., 2013). Consequently, twin studies analyzing the genetic and environmental influences on the variance of these phenotypes can indeed be safely generalized to non-twin populations.

Importantly, authors have assessed the external validity of the results of twin studies using the Add Health twin subsample. For example, Jacobson and Rowe (1998) provided evidence that the twin subsample does not differ from the nationally representative full sample on such demographic characteristics as sex, race, and age. As a result, to the extent that these characteristics influence variance in the phenotype of interest it is methodologically sound to generalize the twin study results to non-twins. Additionally, Beaver’s (2008) assessment of family influence as part of the nonshared environment on antisocial behaviors also included an analysis of the similarities between MZ twins and non-twins in the Add Health. Beaver’s (2008) study showed that MZ twins in the Add Health are not significantly different from non-twins on 117

measures such as levels of self-control, parental socialization experiences, and number of delinquent peers. A limitation of Beaver’s (2008) analysis is that it included only MZ twins. In recognition of this limitation a recent study by Barnes and Boutwell (2013) assessed the difference between singletons (i.e., non-twins) and both MZ and DZ twins on a number of measures tapping developmental, behavioral, and cognitive phenotypes. In addition to assessing the mean difference between singletons and twins in the Add Health, Barnes and Boutwell

(2013) also evaluated the difference in effect sizes associated with the measures between twins and singletons. The authors found that the twin subsample of the Add Health was similar both in mean score and effect size for the vast majority of the measures analyzed. Therefore, the results of Barnes and Boutwell (2013), Beaver (2008), and Jacobson and Rowe (1998) all converge to illustrate that the external validity of the twin subsample of the Add Health is strong and results ascertained from analyses of the twins can be generalized to non-twin populations. While this generalizability is appropriate it should still be done with caution as the fact remains that the twin subsample was not collected in a manner to ensure national representativeness. We will return to this issue in Chapter 7. At this point, it is sufficient to conclude that twin subsample does not appear to vary in any significant manner from the non-twin respondents within the Add Health.

5.2 Measures

5.2.1 Delinquent and Criminal Behaviors

As outlined in the introductory chapter, the current study seeks to assess the effect of adolescent antisocial conduct on sexual and reproductive behaviors in adulthood. Consequently, the measures of antisocial behaviors are limited to waves 1 and 2. Additionally, rather than including only cumulative measures covering all of adolescence (i.e., combined waves 1 and 2)

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the current study incorporates items that tap antisocial conduct at two stages of adolescence (i.e.,

Wave 1 and Wave 2) as well as across adolescence (i.e., combined waves 1 and 2). Such a methodology allows for the assessment of the association between the independent and dependent variables at multiple points in time.

Following the coding scheme outlined in Barnes et al. (2011) and Beaver et al. (2013) nine different delinquent/criminal behavior indexes were created. First, during Wave 1 interviews respondents were asked a series of questions that tapped their involvement in delinquent and antisocial behaviors. Given that these questions were considered of a sensitive nature, the respondents indicated their answers directly on a laptop computer (Harris et al.,

2006). Respondents provided the number of times in the past year they had engaged in such behaviors as damage property, shoplift, get into a serious fight, sell drugs, or steal something worth more than $50. In all, respondents provided answers to 17 different delinquent activities.

A list of all of the specific questions for the Wave 1 and Wave 2 delinquency indexes is provided in Appendix A. Each item was coded such that 0 = never, 1 = one or two times, 2 = three or four times, and 3 = five or more times. Each respondent’s answers to these 17 questions were summed to form the Wave 1 delinquency index (α = .86). Second, at Wave 2 respondents were asked a very similar set of questions regarding delinquent behavior. Once again, the 17 questions were coded similarly and summed to create the Wave 2 delinquency index (α = .83).

Third, following Boutwell et al. (2013) the delinquency indexes for waves 1 and 2 were summed to form the adolescent delinquency index (α = .90), where higher scores indicate a greater involvement in delinquency during adolescence.

Researchers have indicated that individuals who engage in serious violent offending may possess traits that differentially influence the likelihood of employing an r-selected life history

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beyond that of nonviolent offenders (Ferguson, 2010; Moffitt, 1993; Rushton, 2000; Rushton and

Whitney, 2002).30 Consequently, an adolescent violence index was constructed to differentiate serious violent offenders from nonviolent offenders. Extracted from the first two waves of data were 12 items that measured serious physical violence (see Appendix A for a list of the items).

These items were then summed to create three measures: the Wave 1 adolescent violence index

(α = .78), the Wave 2 adolescent violence index (α = .76) and the combined waves 1 and 2 violence index (α = .83) where higher scores indicate a greater involvement in violent behaviors during adolescence. Importantly, this coding scheme has been employed by other researchers analyzing the Add Health (Beaver et al., 2013).31 To further explore the differential effects of violent versus nonviolent offending, an adolescent nonviolent offending index was also constructed. The adolescent nonviolent offending index was created in a similar fashion to the violent offending index such that items from the first two waves of data tapping nonviolent were selected (e.g., graffiti, theft, selling drugs; see Appendix A for a full list; Wave 1 nonviolent index α = .81; Wave 2 nonviolent index α = .76). These 18 items were summed together to form the adolescent nonviolent offending index (α = .85) where higher values indicate a greater involvement in nonviolent offending during adolescence.32

30 Recall that the r/K strategies are not dichotomous but represent separate ends of a continuum. Therefore, it may be the case that individuals who engage in serious violent behavior may be closer to the r-selected end of the continuum than those individuals who engage in a less serious constellation of delinquent behaviors. 31 In order to reduce missingness on the adolescent delinquency and adolescent violence measures the Stata command “rowtotal” was used with the option “missing” specified. This command and option works to sum the values of the constituent items even if there is missing data on one of the items (whereas simply summing the items would drop any case with missing on any one of the items which drops over half of the sample). 32 Importantly, the items comprising the violent and nonviolent offending indexes were first subjected to confirmatory factor analyses. These analyses resulted in the constituent items loading solely on one factor (i.e., the violent offending items loaded on one factor [Eigenvalue > 1.00] while the nonviolent offending items loaded on one factor [Eigenvalue > 1.00]) for each wave and for the combined waves. Additionally, for each individual wave index and the combined wave indexes the factor loadings for the constituent items ranged from 0.45 to 0.75 and removal of any single item from any of the indexes did not result in an increase in the value of Cronbach’s alpha. 120

5.2.2 Involvement with the Criminal Justice System During Adolescence

Number of Arrests as a Juvenile. During the Wave 4 interview, respondents indicated the number of times they were arrested before the age of 18 years. This measure of juvenile arrests was coded continuously with higher values indicating a great number of arrests prior to age 18.

Given that this question was preceded by a feeder question asking “Have you ever been arrested” those respondents who indicated that they had never been arrested in their lifetime were coded as

‘0’ on the number of juvenile arrests item (i.e., they were ‘legitimate skips’ on the number of juvenile arrests item) rather than as missing.

Ever Arrested as a Juvenile. The number of arrests as a juvenile measure was dichotomized to create the ever arrested as a juvenile item. This dichotomous item was coded such that 0 = never arrested prior to age 18 years and 1 = arrested at least once prior to age 18 years.

Arrested More Than Once as a Juvenile. The number of arrests as a juvenile measure was dichotomized to create this measure. The dichotomous arrested more than once as a juvenile measure was coded such that 0 = never arrested or arrested only once prior to age 18 years and 1

= arrested more than once prior to age 18 years.

5.2.3 Sexual Behavior Measures

Sexual Activity Index. During the Wave 4 interviews respondents were asked three questions about their past sexual experiences. Each respondent was asked if they had ever had vaginal intercourse, if they had ever had oral sex, and if they had ever had anal sex. Each of these dichotomous items were coded such that 1 = yes and 0 = no. The three items were then

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summed to form the sexual activity index (α = .40). The sexual activity index has been employed by past researchers using the Add Health (Nedelec and Beaver, 2012).

Age of Sexual Debut. In addition to providing reports on their varied sexual experiences respondents also provided the age at which they first engaged in vaginal sex, oral sex, and anal sex. This information was collected during the Wave 4 interviews. Each of these continuous variables was coded such that lower values represent earlier onset of the specific sexual activity.33

Number of Sex Partners. At Wave 4 respondents reported the total number of partners with whom they had ever had vaginal intercourse. Additionally, respondents reported the total number of partners with whom they had ever had vaginal intercourse prior to the age of 18 years.

Given that sexual behavior is not limited to vaginal intercourse, respondents were also asked to report the number of partners (male or female) with whom they had engaged in any sexual activity both in the past year and prior to the age of 18 years. In all, the current study includes one measure indicating the lifetime number of vaginal sex partners, one measure of the number of partners with whom any sexual activity was ever shared, and one measure of the number of partners with whom any sexual activity was shared prior to the age of 18 years.

Short Term Sexual Involvement. In order to tap engagement in sexual behaviors representing differential involvement in sexual strategies outlined by life history theory a measure of short term sexual involvement was employed. At Wave 4 respondents were asked to respond to the following question: “Considering all types of sexual activity, with how many

33 Each of these age of sexual debut measures contained very early ages (i.e., from 0 to 9 years of age). Therefore, the age of sexual debut for vaginal sex, oral sex, and anal sex measures were truncated with a minimal value of 10 years of age. This cutoff point was chosen for two reasons; first, a measure of age of sexual debut at Wave 3 had a minimum age of 10 years, and second, less than one percent (0.80 percent) of the sample indicated sexual debut prior to age 10. 122

partners, male or female, have you had sex on one and only one occasion?”. This item was coded continuously such that higher values indicate a greater number of one-time sexual partners.

Risky Sexual Behaviors. In order to tap engagement in risky sexual behaviors a number of measures were employed. First, during the Wave 4 interviews respondents indicated whether, in the past year, either they or their sexual partner used any of twenty different methods of birth control or disease prevention (e.g., condoms, birth control pills, diaphragm, spermicide, etc.). A dichotomous measure of safe-sex practices was created where 1 = employed one or more method and 0 = no method employed. Second, a measure of promiscuity was created based on one question asked at Wave 4 to which respondents provided the number of times in the past year they had sex with more than one partner at around the same time. This dichotomous measure was coded such that 1 = yes and 0 = no. Third, during the Wave 4 interviews respondents were asked “In the past 12 months, how many times have you paid someone to have sex with you or has someone paid you to have sex with them?”. This categorical item was recoded into a dichotomous measure of prostitution where 1 = paid/was paid to have sex and 0 = did not engage in prostitution. Finally, these items were summed to create the risky sexual behaviors index (α =

.21) where higher values indicate a riskier sexual lifestyle (note that the safe-sex item was first reverse-coded). Researchers employing the Add Health data have created similar measures to tap risky sexual behavior (Harden and Mendle, 2011; Trejos-Castillo and Vazsonyi, 2009).

5.2.4 Relationship and Reproductive Measures

Marriages. Two measures regarding the respondents’ marital history are employed in the current study. First, at Wave 4 respondents indicated the number of people to which they were ever married (including any current spouses). The number of spouses measure was coded 123

continuously with higher values indicating a larger number of spouses. Second, the number of spouses measure was then dichotomized to indicate if the respondent was ever married where 1 = married one or more times and 0 = never married.

Cohabitation. A single measure of cohabitation was provided during the Wave 4 interviews. Respondents were asked the following question, “How many romantic or sexual partners have you ever lived with for one month or more?”. This question excluded any partners that the respondent married. The cohabitation measure was coded continuously such that higher values indicate a greater number of people with whom the respondent had lived.

Relationship Length. Two measures of relationship length are employed in the current study. First, at Wave 4 respondents were asked to provide the number of romantic or sexual relationships that lasted six months or more since 2001 (i.e., in the past six to seven years). The measure of number of long-term relationships was coded continuously such that higher values indicate a greater number of relationships lasting six months or more. Second, respondents were asked to indicate how many romantic or sexual partners with whom they had a relationship that lasted less than six months. The short-term relationship item was coded continuously such that higher values indicate a greater number of relationships lasting less than six months.

Number of Extra-Pair Sexual Partners. At Wave 4 respondents were asked to report the number of persons other than their current serious relationship partner (e.g., spouse or cohabitant) with whom they had a sexual or romantic relationship. This measure was coded continuously such that higher values indicate a greater number of extra-pair sexual or romantic relationships. A second measure of extra-pair sexual partners was also created. The number of extra-pair sexual partners item was recoded to create a dichotomized item where 1 = currently engaged in at least one extra-pair relationship and 0 = not currently engaged in an extra-pair

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relationship. Importantly, both of these measures treated those who were not in a serious relationship at Wave 4 as missing.

Pregnancies and Live Births. During the Wave 4 interview respondents were asked the following question, “Thinking about all the relationships and sexual encounters you have ever had, how many times have you ever been pregnant/how many times have you ever made a partner pregnant?”. This question included any pregnancies regardless of their outcome. This continuous measure was coded such that higher values indicate a larger number of pregnancies.

Additionally, respondents were also asked to report how many of the pregnancies resulted in live births. This item was coded as continuous such that higher values indicate a greater number of live births.

Pregnancies out of Wedlock. At Wave 4 respondents were asked to provide the number of pregnancies that resulted from sexual relationships which were not a part of a marriage or cohabitation relationship. This measure was then dichotomized such that 1 = ever had a pregnancy out of wedlock and 0 = no pregnancies out of wedlock.

Child Mortality. A measure tapping whether the respondent had a child who died after childbirth was derived from the Wave 4 interview. The respondents were asked to report the number of children from live births who were still alive. Matching the number of children still alive to the number of live births an item indicating whether the respondent was a parent to a child who died was created. This dichotomous item was coded such that 0 = all children still alive and 1 = one or more children died after birth.

Parental Dissatisfaction Index. In order tap respondents’ attitudes towards parenting an additive index based on four different items was created. At Wave 4 respondents were asked to indicate their level of agreement (where 1 = Strongly agree and 5 = Strongly disagree) with the

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following statements: “I am happy in my role as a parent”, “I feel close to my child(ren)”, “The major source of stress in my life is my child(ren)” (reverse coded), and “I feel overwhelmed by the responsibility of being a parent” (reverse coded). The four items were summed to create the parental dissatisfaction index (α = .57) where higher values represent greater dissatisfaction with being a parent. The parental dissatisfaction index has been employed in the past by researchers using the Add Health data (Nedelec, Schwartz, Connolly, and Beaver, 2012).

5.2.5 Composite Sexual Behavior Index and Composite Reproductive Behavior Index

Recall that life history theory holds that organisms employ life strategies wherein the emphasis of one collection of behaviors (e.g., mating effort) leads to a reduction in an emphasis on another collection of behaviors (e.g., parenting effort). Consequently, the current study includes measures which tap these diverging collections of behavioral strategies. In order to do so, a number of inter-related steps were taken. First, each of the sexual behavior measures and the relationship and reproductive measures were included in an exploratory factor analyses. The exploratory factor analyses were conducted using polychoric correlational matrices due to the inclusion of mixed data types within the analyses. Furthermore, researchers recommend that when including ordinal and dichotomous variables within an exploratory or confirmatory factor analyses polychoric correlational analyses produce more accurate results than those based on

Pearson matrices (Holdago-Tello, Chacón-Moscoso, Barbero-García, and Vila-Abad, 2010).

Table 5.3 displays the results of the factor analyses. As illustrated, the factor analyses resulted in seven different factors with Eigenvalues greater than the conventional cutoff point of 1.00.

However, the first three factors accounted for almost 50 percent of the cumulative variance in the factor analyses. The second step of the exploratory analysis involved focusing on the first three factors represented in the first step. Table 5.4 illustrates the factor loadings for all of the items 126

included in the factor analysis for the first three factors. As illustrated, factor 1 seems to represent sexual activities as those items related to sexual behaviors loaded most heavily on this factor. Additionally, it would appear that factors 2 and 3 represent relationship and reproductive behaviors as those items loaded most heavily with those factors. These results informed the next step of the analysis.

The third step of this process entailed the creation of the two composite measures. For the sexual behavior index those items which loaded on factor 1 and had a factor loading greater than .47 were standardized and then summed to create the composite sexual behavior index (α =

.75)34. Those items loading on factors 2 and 3 were utilized to create the composite reproductive behaviors index. Continued correlational analyses of these items indicated that the alpha level was significantly increased by dropping three items (number of short relationships, number of cohabitation partners, and ever parent a child who died). Consequently, the composite reproductive behaviors index is comprised of the four remaining items (ever married, number of times married, number of pregnancies, and number of pregnancies resulting in a live birth) which were standardized and summed together (α = .74).

The composite measures are operationalized following the tenets of life history theory outlined in the above discussion (see Chapter 2). Consequently, the sexual behavior index can be conceptualized as representing the r-selected portion of the r/K continuum. In other words, those respondents who score higher on the composite sexual behaviors measure are engaging in

34 While 0.47 is below the conventional factor loading cutoff of 0.70 this value was chosen as it allowed for the inclusion of those measures which were theoretically (as well as statistically) related (i.e., all of the items represent sexual behaviors). Furthermore, bivariate analyses indicated that the items included in the composite sexual behaviors index were all statistically associated at the 0.05 level. Additionally, the sexual activity index (comprised of three dichotomous items referring to vaginal sex, oral sex, and anal sex) was not included in the factor analyses as it caused the model to fail to converge. However, the sexual activity index was still included in the composite sexual behaviors index as it has theoretical relevance to the overall measure and was significantly associated at the bivariate level to the other items comprising the composite index. 127

behavioral strategies which represent an emphasis on mating efforts. In contrast, the reproductive behaviors index can be conceptualized as representing the K-selected portion of the continuum, where those who score higher on this measure place more emphasis on parenting (or reproductive) efforts. A key benefit to these measures (and the individual items upon which they are based) is that they are based on actual behaviors and not just attitudes or perceptions.

Therefore, the items can be considered valid measurements of the behavioral strategies employed by the respondents.

5.2.6 Control Variables

Demographics. In order to account for the potential confounding effects of age, race, and sex each of these variables were included in the analyses. Age was calculated for each wave of data collection by subtracting the respondent’s birth year from the interview year. Sex was derived from the Wave 1 interview and coded dichotomously such that 0 = female and 1 = male.

Race was also derived from the Wave 1 interview and was coded such that 0 = nonwhite and 1 = white.

Low Self-Control. Given that low self-control/impulsivity has been linked with both delinquency and sexual behaviors (Boutwell et al., 2013; Gottfredson and Hirschi, 1990; Nedelec and Beaver, 2012) and has been shown to be relevant to life history strategy (Copping,

Campbell, and Muncer, 2013) we include a measure tapping low self-control. The low self- control item is a continuous measure and a composite of low self-control indexes generated from variables at waves 1 and 2. The Wave 1 measure included items derived from both the parent interview and the in-home respondent interview. The parent provided such information on the child as whether the child has a bad temper, whether the child gets along well with others, and if the child is trustworthy. The respondent provided information to such questions as how often 128

they get upset with difficult problems, how often they rely on ‘gut feelings’, how often they have trouble paying attention or doing their homework. Twenty-three items in all were summed to create the Wave 1 low self-control index (α = .71). The items were coded such that higher values on the index indicate greater levels of low self-control (i.e., more impulsive). The Wave 2 low self-control index was generated from 20 questions that were almost identical to those asked at Wave 1 (see Appendix A for a list of all of the questions for both waves). Once again, the

Wave 2 index (α = .73) was coded such that higher values indicate a greater level of low self- control. To create the overall measure of low self-control the waves 1 and 2 indexes were summed (α = .73). Importantly, all three of these measures have been used by other researchers employing the Add Health dataset (Beaver et al., 2010; Beaver et al., 2008). Only the composite, overall index of low self-control is included in the analyses of the current study.

IQ. Cognitive functioning and general intelligence has been linked to both delinquency and sexual behaviors (Barnes et al., 2013; Beaver and Wright, 2011; Rowe, 2002; Rushton,

2004; Walsh, 2011). Additionally, Rushton (2004) notes that intelligence plays an important role in the selection into various life history strategies. Consequently, a control for IQ is included in the current analyses. To obtain measures of IQ, participants were administered an abbreviated version of the Peabody Vocabulary Test (PVT) during the Wave 1 interview. The PVT is a standardized instrument employed to assess verbal skills and receptive vocabulary (Rowe,

Jacobson, and van den Oord, 1999). The IQ measure was coded such that higher values represent a higher score on the PVT (i.e., a higher IQ).

Future Outlook. Recall that life history theory holds that the likelihood of adopting a particular type of reproductive and lifestyle strategy (i.e., r versus K) is partially contingent on particular aspects of the immediate environment. Specifically, cues derived from the

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environment which indicate an increased probability of a shortened lifespan are likely to lead to adoption of an r-selected life strategy (Brumbach et al., 2009). In this vein, researchers have found that possessing a negative future outlook is related to engaging in a suite of behaviors that could be categorized as an r-typical life history strategy, including sexual behavior and delinquency (Daly and Wilson, 2005; Nedelec and Beaver, 2012; Wilson and Daly, 1997).

Consequently, an item tapping respondents’ views of the future in terms of life certainty was included in the analyses. The future outlook index is comprised of three items derived from the

Wave 1 interviews. Respondents indicated the likelihood that they would live to be age 35

(reverse coded), that they would be married by age 25 (reverse coded), that they would be killed by age 21, and that they would contract HIV/AIDS. The items were coded such that 1 = low and

5 = high and were summed to produce the future outlook index (α = .43) where higher scores indicate a more pessimistic view of the future. This index has been employed by past researchers using the Add Health (Caldwell, Wiebe, and Cleveland, 2006; Nedelec and Beaver,

2012).

Socioeconomic Status (SES). An item tapping the SES of respondents at Wave 1 was included in the current study. During the Wave 1 in-home interviews, the parent (typically the mother) provided information on her education level and income. These items were standardized and summed to create the SES control variable where higher values indicate a higher SES level.

Importantly, this coding scheme matches the methods employed by prior researchers using the

Add Health (Rowe et al., 1999).

The descriptive statistics for all the measures included in the study are displayed in Table

5.7. The descriptive statistics are for the entire sample (i.e., all kinship pairs).

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5.3 Analytical Plan

The analyses employed in the current project follow a series of related steps that are conducted for each research question. Note that while the research questions are related via the theoretical scaffold provided in the literature review they each represent a specific test of an assertion differentiated from the other questions. Consequently, there are different analytical steps employed for each specific question. Therefore, the analytical plan is arranged by research question wherein the steps are outlined by question. It is worth reiterating that the current project is concerned not only with testing the postulates of life history theory but is primarily focused on assessing the relative impact of antisocial behaviors on the adoption of particular reproductive strategies within the boundaries of an evolutionary perspective.

Before outlining each research question in detail it is important to note the bifurcation of antisocial behavior measures employed in the current study. An overview of the measures section will illustrate that respondents provided self-reports of both criminal/delinquent behaviors and involvement with the criminal justice system. These two types of measures will be employed separately to assess the relative influence on reproductive strategies. There are two main reasons why both of these types of measures are included in the analyses. First, criminologists have long recognized the difference between ‘crime’ and ‘criminality’. The various acts that are committed and can come to the attention of criminal justice system agents

(data known as ‘official statistics’) fall under the categorization of ‘crime’; whereas,

‘criminality’ refers to one’s propensity to commit acts considered criminal or antisocial

(Gottfredson and Hirschi, 1990). This difference is recognized in the current study where the items in section 5.2.1 represent measures of criminality and the items in section 5.2.2 represent measures of crime. The second reason for this differentiation is statistical. Although the

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recognition of the difference between crime and criminality is provided in the current study it still is the case that the respondents provided self-reports of both behaviors (criminality) and involvement with the criminal justice system (crime). Therefore, it may be the case that there is in fact no differentiation. However, correlational analyses illustrate that while the behavioral measures are significantly associated with each of the crime measures there is still considerable variance that remains (see Table 6.1). Consequently, both types of measures are likely tapping different forms of antisocial behaviors. Therefore, by including both types of measures, the current study provides a comprehensive assessment of the potential association between antisocial conduct and reproductive strategies over the life course.35

5.3.1 Research Question 1

The first research question asked, “Do adolescent antisocial behaviors influence reproductively relevant outcomes (i.e., sexual behaviors and relationship measures)?”. In order to assess this research question the following steps will be taken. First, zero-order (bivariate) correlations will be provided for all of the variables in the analysis. While the zero-order associations will be provided for all variables, including the control variables, attention will be focused on the relationship between the measures of antisocial behaviors and the various sexual and reproductive outcomes. Second, in order to assess whether the zero-order relationships are impacted by potential confounding variables each sexual and reproductive outcome will be regressed on each measure of antisocial conduct while holding constant the influence of the control variables. These multivariate models will be conducted using OLS (continuous), logistic

(dichotomous), and Poisson (count) regression techniques, depending on the nature of the

35 It should be noted here that all data preparation was completed in the statistical package Stata (version 12). Additionally, the analyses for research questions 1 and 3 were also conducted in Stata. However, the analyses for research question 2 were conducted using Mplus (version 7.0; Muthén and Muthén, 2012). 132

outcome. As indicated above, robust standard errors are employed in order to correct for correlated error terms due to the nature of the sample (i.e., kinship pairs).

It is of worth to note here that a typical SSSM assessment of the association between antisocial conduct and sexual/reproductive behaviors would not got beyond this step. Indeed, in their examination of the association between early sexual debut and delinquency Armour and

Haynie (2007) conduct negative binomial regression analyses without controlling for genetic influence on any of the variables. The authors are not unique in this regard as a defining component of an SSSM approach is its inability to control for the influence of genetic factors

(Beaver, 2009).

5.3.2 Research Question 2

The second research question asked, “Are antisocial behaviors and sexual/reproductive behaviors influenced by genetic factors and to what extent is the proportional influence of genetic factors relative to environmental factors?”. In order to assess Research Question 2 a series of behavioral genetics techniques will be employed: cross-twin correlations and univariate

ACE decomposition models. 36

5.3.3 Cross-Twin Correlations

The cross-twin correlations will provide an initial indication of the extent of the influence of genetic factors on each of the measures employed in the analyses. Cross-twin correlations represent a preliminary step that is often taken in behavioral genetic research (Plomin et al.,

2013). The cross-twin correlation (or intraclass correlation) will provide a statistical measure of

36 While the term “cross-twin” is employed it should be remembered that the analyses will be conducted on both twin and non-twin kinship pairs. The phrase “cross-twin” is being used herein as a matter of convenience but the method is the same for both twins and other types of kinship pairs. 133

the covariation on a single phenotype within each kinship pair.37 More specifically, a sibling’s score on a phenotype will be compared to his or her co-sibling’s score on the same phenotype.

Using the logic underlying the combined twin methodology (see Chapter 3) if those kinship pairs that are more genetically related display a greater covariance then it can be concluded that there is a genetic effect influencing the measured phenotypic variance. Stated differently, if a genetic influence is evident then following will be expected: rMZ > rDZ/rFS > rHS (where: r denotes the value of the correlation coefficient, MZ = monozygotic twins, DZ = dizygotic twins, FS = full siblings, HS = half-siblings). While the cross-twin correlation can provide initial evidence of an influence of genetic factors, it does not provide an indication of the relative influence of genetic and environmental factors. In order to do so, ACE decomposition models are employed.

5.3.4 Univariate ACE Decomposition Model

The ACE decomposition model is a structural equation modeling technique that compares the phenotypic variance between members of a kinship pair and allows for an estimation of the variance that is due to genetic factors (h2), shared environmental factors (c2), and nonshared environmental factors (e2). The ACE model is widely used in behavioral genetics research including twin, family, and combined research designs (Plomin et al., 2013). The structural equation modeling employed in the ACE model relates the observed variables (i.e., phenotypes) to the unobserved latent genetic and environmental variables (i.e., h2, c2, and e2;

Evans et al., 2002). The ACE model is a univariate model as it assesses the variance in only one observed phenotype. The ACE model is typically presented as a path diagram and the values of the three latent genetic and environmental components are derived by employing the rules of

37 Following the methods employed by past researchers (Barnes et al., 2011) tetrachoric correlation techniques will be employed for those phenotypic outcomes that are measured dichotomously, while Pearon’s r will be employed for those outcomes that are measured continuously. 134

path analysis (Evans et al., 2002). The ACE model is illustrated in Figure 5.1.38 The path diagram of the ACE model contains a number of important features necessary to understanding how the various estimates of variance are produced. First, the A portion of the model represents the genetic component (h2), the C portion corresponds to the shared environment component (c2), and the E represents the nonshared environment component (e2). As a reminder, these three components capture 100 percent of the variation in an observed phenotype and the path coefficients (i.e., a1, a2, c1, c2, e1, and e2) provide estimates of the three components. As mentioned above, the A, C, and E components are all unobserved latent variables and are therefore represented by circles conforming to conventional structural equation modeling displays (Plomin et al., 2013). The second feature illustrated in the path diagram is represented by the two rectangles, the observed/measured phenotypes. These items represent the scores for each sibling within a kinship pair on the measured phenotype. Third, the ACE model diagram highlights the manner in which the technique takes into account the variation in genetic relatedness when producing estimates of the variance components. Note that the A components

(i.e., the genetic factors [h2]) are connected via a double-headed arrow. This double-headed arrow indicates a correlation between the two siblings’ genetic component. Note further that there are four different values associated with the correlation; these values represent the varying levels of genetic relatedness of the kinship pairs included in the current study (i.e., MZ = 1.00;

DZ and FS = .50; and HS = .25). These varying levels of genetic relatedness are taken into account when calculations are conducted to produce variance estimates for the latent components. Similarly, it is evident that the C component (i.e., shared environment [c2]) is also connected via a double-headed arrow. As a result of the nature of the shared environment the

38 A less complex manner to conceptualize the ACE model is provided in Figure 5.2 where it can be seen that the three latent components (A, C, and E) comprise the entirety of variance in two hypothetical phenotypes. 135

1.00

1.00; .50; .25

E C A A C E

c1 c2

e1 a1 a2 e2

Phenotype Phenotype Sibling 1 Sibling 2

Figure 5.1: ACE model path diagram.

Figure 5.2: The ACE decomposition model for two hypothetical phenotypes.

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correlation between two siblings in a kinship pair for the C component is, by definition, set to

1.00 (i.e., 100 percent). Finally, note that the E components are not connected between the two siblings. Recall that the nonshared environment captures environmental factors that are unique to each sibling. Therefore, the E component in an ACE model is left free to vary as there is no association between siblings on nonshared environmental effects (Barnes et al., 2011). The ACE decomposition model will be applied to each variable of interest included in the current analyses and will provide an estimate of the relative influence of genetic and environmental factors on the variance of each phenotype.39

Before moving on to the next research question, a number of important points regarding the ACE modeling technique need to be addressed. The ACE model provides not only a coefficient for each variance component but it also provides an indication of the significance of influence for each component. The ACE model allows for the estimation of nested models which can assess the relative importance of the individual components. The nested models are represented by the exclusion of one or more of the three components (i.e., A, C, E, or a combination of two components are dropped from the model). Through the production of model fit statistics the nested/constrained models can be compared to the full model to assess whether a more parsimonious model (i.e., fewer components) produces a comparable fit to the full model

(Medland and Hatemi, 2009). This comparison is statistically assessed through the use of a number of model fit statistics. The most common statistics are the chi-square (χ2) statistic test

39 It should be noted that for variables coded dichotomously a variation of the standard ACE model will be employed. Termed a ‘threshold ACE model’, the calculations are essentially identical with the exception of informing the statistical package (in the current study’s cases, Mplus version 7.0) that the outcome measure is categorical/dichotomous (Muthén and Muthén, 2012). Additionally, the threshold ACE model does not produce an AIC statistic (see below). Therefore, in deciding model fit the change in chi-square statistic as well as the root mean square error approximation (RMSEA) will be employed. The RMSEA provides an overall indication of model fit and the lower the RMSEA value the better the model fit. Convention holds that RMSEA values of less than 0.10 indicate a good fit and RMSEA values of less than 0.05 indicate a very good fit (Hudziak, Rudiger, Neale, Heath, and Todd, 1999). 137

and the Akaike information criterion (AIC; Barnes et al., 2013). However, there is some controversy in the behavior genetics literature as to which model fit statistic is to be preferred. In an analysis of optimal cutoff points for various model fit statistics, Hu and Bentler (1999) found that sample size is a crucial variable when deciding which model fit statistic to use and what value should be considered as optimal for selecting among different models. In a follow-up to

Hu and Bentler (1999), Marsh, Hau, and Wen (2009) re-analyzed a number of model fit statistics and concluded that even during sample size fluctuation the chi-square statistic provided the most robust method of assessing model fit. Consequently, the current study will employ the chi- square statistic when assessing model fit. However, to supplement decisions based on the chi- square statistic and to conform to current biosocial criminology analyses the AIC will also be employed. Two points must be made about these statistics. First, the value of each statistic is important in determining the best fitting model. Consequently, the smaller the value of either statistic the greater the model fit (in general). Second, in relation to the first point the change from the saturated or full model to the constrained or nested models provides the ultimate indication of model fit. Therefore, if a nested model (i.e., a model with one or more latent components dropped/constrained) produces model fit statistics which are not significantly different from the fit statistics produced in the full (saturated) model then the nested model is said to be a more parsimonious and better fitting model (Barnes et al., 2013).

As outlined, the nested models are compared to the full/saturated model. Consequently, the analyses unfold in a sequential fashion. While there is no mathematically relevant reason for the sequence of model constraint, convention holds that researchers first estimate a full model

(i.e., ACE), followed by an AE model, followed by a CE model, and finally an E model (Barnes et al., 2011). Consequently, all of the results of the ACE analyses for each phenotype in the

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study will be presented (i.e., each table will include multiple models with parameter estimates and model fit statistics). While the results of the ACE model analysis will afford an answer to

Research Question 2 and provide a unique contribution to the literature they will also guide the steps taken in addressing the next research question.

5.3.5 Research Question 3

The final research question asked, “Does the association between antisocial behaviors and reproductively relevant outcomes remain after controlling for the influence of genetic and environmental confounds?”. Two observations can be made about the focus of this research question: first, it necessitates that a significant association (covariation) be observed and second, it necessitates the use of genetically sensitive designs which control for the influence of genetic and environmental factors in the assessment of an association between the measures. The first requirement will be satisfied in the analyses that result from Research Question 1. Therefore, those associations which are observed to be substantially and/or statistically significant at the bivariate and multivariate levels will be subjected to further analysis to determine if the association is confounded by shared genetic factors and/or shared environmental factors (i.e., the second observation outlined above). In order to address Research Question 3, two modeling strategies will be employed: first, DeFries-Fulker (DF; 1985) regression analyses and second, monozygotic (MZ) difference score regression analyses (aka, the discordant MZ twin approach;

Vitaro et al., 2009). These techniques are described in the next two sections.

5.3.6 DeFries-Fulker (DF) Models

The regression-based DF model has been widely employed in the behavioral genetics literature and remains one of the most popular methods of assessing the relative influence of

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genetic and environmental factors on phenotypic variance (Beaver, 2008; Medland and Hatemi,

2009; Smith and Hatemi, 2012). While the DF model is often used to decompose the variance of a phenotype it can also be utilized to isolate the effects of components of the nonshared environment on phenotypic variance. Given that the current study employs ACE models in order to decompose phenotypic variance the DF models will only be used to isolate specific components of the nonshared environment and assess their effect on the phenotypes included in the study. In other words (and as pointed out in note 2 in Chapter 1), the DF model will be used to assess whether shared genetic and shared environmental factors are confounding the associations observed between antisocial behavior in adolescence and sexual/reproductive behaviors in adulthood. To see how this will be done more specificity in terms of what the DF model entails is required.

The DF model to be employed in the current study takes the following form:

K1 = b0 + b1(K2 – Km) + b2(R * (K2 – Km)) + b3(ENVDIF) + e (5.1)

In this equation, K1 represents the score on an outcome measure for one sibling/twin in a kinship pair (i.e., twin 1). Therefore, it can be seen that the equation includes variables employed to predict the score for twin 1 on an outcome measure of interest. This point should render it apparent, then, that the unit of analysis in the DF model is twin/sibling pair, a point to keep in mind when interpreting the results of such analyses. To continue explanation of the DF model,

K2 represents the score on the same measure as K1, but for twin 2. Km is the mean for K2 and is employed to mean-center twin 2’s score. R represents the measure of genetic relatedness for the sibling pair and follows the degrees of genetic relatedness outlined in Table 3.1 (i.e., R = 1.0 for

MZ twins, R = .50 for DZ twins and full siblings, and R = .25 for half-siblings). The multiplicative interaction term, R * (K2 – Km) is created by multiplying the degree of genetic

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relatedness by twin 2’s mean-centered score on the outcome measure. The final term, ENVDIF represents the difference between the two twins within a kinship pair on a specific measure.

ENVDIF is calculated by subtracting one twin’s score on a measure from the co-twin’s score on the same measure. It is this portion of the DF model that represents the isolated component of the nonshared environment. The regression coefficients within the DF model represent the variance components parameter estimates. Specifically, b0 is the constant and is not interpreted, b1 is the unstandardized regression coefficient representing the proportion of variance in K1 that is accounted for by the shared environment, b2 indicates the proportion of variance in K1 that is explained by genetic factors, and b3 is an indication of the proportion of variance in K1 that is due to the isolated component of the nonshared environment. If the p-value associated with b3 is statistically significant it can be concluded that the ENVDIF measure has a nonshared influence on the phenotype. Finally, the error term (e) represents the remaining influence of non-measured components of the nonshared environment and measurement error (Beaver, 2009; Rodgers and

Kohler, 2005; Rodgers et al., 2001).40

As mentioned, while the DF model can be employed to determine the variance estimates for shared genetic and shared environmental components it will only be employed in the current study as a way of holding those influences constant while the antisocial behavior measures are isolated as components of the nonshared environment. 41 Another way to conceptualize this is the following: if the regression coefficient associated with the specific antisocial behavior measure is statistically significant it means that the unique level of antisocial behavior (i.e., the

40 For the sexual/reproductive items that are coded dichotomously, logistic regression DF analysis will be employed. While this technique alters the interpretation of the variance components, the interpretation of the isolated nonshared component still rests on statistical significance. 41 Therefore, the results pertaining to the variance estimates produced by the DF models will not be displayed. It is worth noting here that researchers have shown that results obtained from structural equation modeling techniques such as univariate ACE models are typically similar to those produced from DF analyses (Smith and Hatemi, 2012). 141

difference in antisocial behavior) between the siblings within a kinship pair has an effect on the sexual/reproductive behavior measure in adulthood, beyond the influence that shared genetic factors and shared environmental factors have on both the antisocial measure and the sexual/reproductive outcome. When conceptualized in this manner, it becomes apparent why difference scores are employed for the DF model. In other words, when twin 2’s score on a measure is subtracted from twin 1’s score on a measure all that remains are those influences which are unique to either twin (i.e., the difference score eliminates the influences of shared genetic and shared environmental factors leaving only the nonshared genetic and nonshared environmental factors as influential variables).42

An additional feature of the DF model owing to its regression-based form is its ability to account for multiple variables as components of the nonshared environment (Rodgers and

Kohler, 2005; Rodgers et al., 2001). Therefore, a researcher can include measures (as difference scores) on items that may further confound the association between the items under analysis.

Consequently, in order to ensure that the observed association is not confounded by differences in age, race, low self-control, IQ, or future outlook each of these items are included as difference scores in the DF models employed in the current study.43

5.3.7 MZ Difference Score Models

While the DF model represents a robust and rigorous method to control for the effects of shared genetic and shared environmental factors on the variance in a phenotype there is a subtlety about the method which allows for variance to be left unaccounted. Given that kinship

42 It should reiterated here that the nonshared environment is not limited to factors traditionally considered to comprise the environment from a sociological perspective. As outlined in Chapter 3, the nonshared environment represents all social and biological/genetic factors that are not captured by h2 and c2. 43 Given that the unit of analysis is the twin pair, sex is not included as a control variable in the DF models as there is no difference evident among the kinship pairs in the study on this variable. 142

pairs who are not monozygotic possess different genetic material, the DF model in Equation 5.2 does not allow for the control of nonshared genetic effects. In other words, there may be nonshared genetic factors confounding any observed associations between the isolated component of the nonshared environment (e.g., antisocial conduct) and the phenotype of interest

(e.g., sexual/reproductive behavior). This issue can be resolved with the use of the MZ difference score approach.

As introduced in Chapter 3, the MZ difference score approach rests of the fact that MZ twins share 100 percent of their DNA (and, by definition 100 percent of their shared environment). Thus, to the extent that genetic factors have an influence on a phenotype those genetic factors can be completed accounted for by using an MZ difference score (the logic is the same for shared environmental influence). The MZ difference score is constructed in the same manner as the ENVDIF term outlined above: twin 2’s score on a measure is subtracted from twin

1’s score on the same measure. The variance that remains (captured by the difference score) is due solely to the nonshared environment. The model is then computed as an OLS regression model.44

The MZ difference score approach has also been widely used in behavioral genetics and is considered the ‘gold standard’ in behavioral genetic modeling (Asbury, Dunn, Pike, and

Plomin, 2003; Beaver, 2008; Vitaro et al., 2009). Thus, the MZ difference score approach is well-suited to the purpose underlying Research Question 3. Accordingly, each DF model that

44 Given that difference scores are employed as outcome measures as well as predictor variables the outcomes are continuous in nature, even for dichotomously coded variables (i.e., dichotomous variables will range from -1 to +1; Beaver, 2008). 143

produces a statistically significant result for the antisocial behavior difference score will be subjected to an MZ difference score analysis.45

5.3.8 Summary of Analytical Plan

The steps taken in the current analysis represent a progressive approach in examining the association between adolescent antisocial behaviors and sexual/reproductive behaviors in adulthood. With each successive step the analysis provides a more thorough and rigorous assessment of the association. These steps are taken for two primary reasons: first, each step is specifically structured to tackle the inquiries raised by the three research questions; and second, the progression represents the utility of a biosocial approach in assessing a topic central to criminological analyses. Taken together, the analyses in the current project provide a unique and rigorous assessment of the long-term effect of adolescent antisocial behaviors on sexual and reproductive outcomes in adulthood that is lacking from the current literature (see Chapter 1).

45 One limitation of the MZ difference score method is a lack of statistical power due to small sample size. As illustrated in Tables 5.1 and 5.2, MZ twins represent 22 percent of the kinship pairs in the sample and due to the use of listwise deletion in the OLS models the analytical sample can be drastically reduced in MZ difference models. Therefore, following past researchers (Beaver, 2008) and convention for sample size (Cohen and Cohen, 1983) those models where N < 100 are dropped from the analyses. 144

Table 5.1: Sample composition by kinship pair type.

Degree of Genetic Kinship Pair Type Individuals (Pairs) Percentage Relatedness

MZ twin 1.00 524 (262) 22 DZ twin 0.50 464 (232) 20 Full sibling 0.50 1,038 (519) 44 Half-sibling 0.25 318 (159) 14

Total -- 2,344 (1,172) 100

Notes: MZ - monozygotic twin pair; DZ - dizygotic twin pair.

Table 5.2: Analytical sample creation by sample reduction step.

Reduction Number of Individual Type of Case Dropped Sample Size Step Cases Dropped

------20,775

1 Non-kinship pair 15,629 5,146

2 19 years or older at Wave 1 357 4,789

3 Cousins 815 3,974

4 Different-sex kinship pair 1,493 2,481

5 Missing family identification number 137 2,344

Total analytical sample size 2,344

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Table 5.3: Sample composition by kinship pair type, genetic relatedness, and sex.

Sample Characteristic Females Males Total

Kinship Pair Type (Genetic Relatedness) MZ (1.00) 268 (51) 256 (49) 524 (22) DZ (0.50) 216 (47) 248 (53) 464 (20) FS (0.50) 510 (49) 528 (51) 1,038 (44) HS (0.25) 172 (54) 146 (46) 318 (14)

Genetic Relatedness 1.00 268 (51) 256 (49) 524 (22) 0.50 726 (48) 776 (52) 1,502 (64) 0.25 172 (54) 146 (46) 318 (14)

Total 1,166 (50) 1,178 (50) 2,344

Notes: Percentages in parentheses; MZ - monozygotic twin pair; DZ - dizygotic twin pair; FS - full sibling pair; HS – half-sibling pair.

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Table 5.4: Age distributions of analytical sample by wave. Wave 1 Wave 2 Wave 4 Age Freq. Percent Cum. % Freq. Percent Cum. % Freq. Percent Cum. %

12 17 0.73 0.73 ------

13 182 7.76 8.49 17 0.73 0.78 ------

14 316 13.48 21.97 170 7.25 8.60 ------

15 427 18.22 40.19 300 12.80 22.40 ------

16 488 20.82 61.01 392 16.72 40.43 ------

17 469 20.01 81.02 456 19.45 61.41 ------

18 445 18.98 100 441 18.81 81.69 ------

19 ------389 16.60 99.59 ------

20 ------7 0.30 99.91 ------

21 ------2 0.09 100 ------

22 ------

23 ------

24 ------

25 ------16 0.68 0.82

26 ------148 6.31 8.43

27 ------283 12.07 22.97

28 ------350 14.93 40.96

29 ------412 17.58 62.13

30 ------375 16.00 81.40

31 ------347 14.80 99.23

32 ------13 0.55 99.90

33 ------2 0.09 100

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Table 5.4: Continued.

Age Wave 1 Wave 2 Wave 4

Mean 15.87 16.85 28.84

SD 1.57 1.58 1.59

Total 2,344 2,174 1,946

Notes: Freq. - frequency; Cum. % - cumulative percent (for age distribution per wave).

Table 5.5: Eigenvalues and proportional variance for items comprising the sexual behavior index and the reproductive behavior index.

Factor Eigenvalue Proportional Variance Cumulative Variance

1 4.59 0.23 0.23 2 2.59 0.13 0.36 3 2.26 0.11 0.47 4 1.70 0.09 0.56 5 1.57 0.08 0.64 6 1.21 0.06 0.70 7 1.02 0.05 0.75

Notes: N = 333; Only factors with an Eigenvalue > 1.00 are shown; All items are from wave 4. 148

Table 5.6: Factor loadings for items comprising the sexual behavior index and the reproductive behavior index.

(Item #) Item Factor 1 Factor 2 Factor 3

(1) Age of debut, vaginal sex (2) Age of debut, oral sex (3) Age of debut, anal sex (4) Number of vaginal sex partners 0.66 (5) Number of any sex activity partners 0.72 (6) Number of any sex activity partners, before 18 yrs. 0.51 (7) Number of one-time sexual partners 0.52 (8) Risky sexual behaviors index 0.48 (9) Number of times married 0.51 (10) Ever married 0.55 (11) Number of cohabitation partners 0.41 (12) Number of long relationships (6+ months) 0.40 (13) Number of short relationships (<6 months) -0.51 (14) Number of extra-pair sexual partners 0.70 (15) Currently have an extra-pair sexual partner 0.69 (16) Number of pregnancies/impregnations 0.36 (17) Number of pregnancies resulting in a live birth 0.70 (18) Number of pregnancies out of wedlock 0.59 (19) Ever parent a child who died 0.79 (20) Parental dissatisfaction index

Notes: N = 333; Polychoric correlational analyses were employed due to the mixture of types of data; Only those factor loadings > 0.35 are shown; All items are from Wave 4.

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Table 5.7: Descriptive statistics of all study variables for all kinship pairs.

Item N Mean SD Min. Max.

Delinquency Wave 1 2,317 4.92 6.29 0 51 Wave 2 2,153 3.29 4.83 0 41 Waves 1 & 2 2,133 8.17 9.78 0 75

Violent Offending Wave 1 2,323 1.31 2.49 0 18 Wave 2 2,160 0.77 1.86 0 18 Waves 1 & 2 2,144 2.05 3.75 0 32

Nonviolent Offending Wave 1 2,322 2.34 3.56 0 27 Wave 2 2,165 1.70 2.89 0 23 Waves 1 & 2 2,150 4.03 5.65 0 42

Criminal Justice System Items Ever arrested as a juvenile 2,344 0.03 0.18 0 1 Number of arrests as a juvenile 1,945 0.16 2.29 0 95 Arrested 1+ times as a juvenile 2,344 0.02 0.13 0 1

Sexual Behavior Items (Wave 4) Sexual activity index 1,914 2.28 0.72 0 3 Age of debut, vaginal sex 1,793 17.04 2.94 10 29 Age of debut, oral sex 1,737 17.91 3.10 10 30 Age of debut, anal sex 800 21.52 3.67 10 31 No. of vaginal sex partners 1,684 10.19 17.42 0 300 No. of any sex activity partners 1,869 12.01 20.57 0 312 No. of any sex activity partners, before 18 yrs. 1,895 2.46 5.15 0 91 No. of one-time sexual partners 1,883 3.51 8.95 0 150 Risky sexual behaviors index 1,630 0.36 0.55 0 3 Sexual behavior index 1,946 -0.01 0.59 -1.88 4.79

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Table 5.7: Continued.

Item N Mean SD Min. Max.

Relationship & Reproductive Items (Wave 4) Number of times married 1,945 0.54 0.58 0 4 Ever married 1,945 0.50 0.50 0 1 Number of cohabitation partners 1,939 0.80 1.15 0 10 Number of long relationships (6+ months) 1,925 0.77 1.72 0 44 Number of short relationships (<6 months) 1,915 1.78 5.38 0 95 Number of extra-pair sexual partners 1,671 0.18 0.51 0 5 Currently have an extra-pair sexual partner 1,671 0.13 0.34 0 1 Number of pregnancies/impregnations 1,941 1.36 1.53 0 14 Reproductive behavior index 1,945 -0.06 0.76 -0.97 3.39

Notes: No. – number.

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CHAPTER 6

RESULTS

The results of the study are arranged by research question. Each section will begin with a review of the specific wording of the research question. Additionally, a brief reminder of the method(s) employed in the relevant analyses will also be provided for each research question.

For greater detail of the methods, see the analytical plan in Chapter 5. Unless otherwise specified, the findings covered will refer to the full sample of kinship pairs.

6.1 Research Question 1

The first research question asked, “Do adolescent antisocial behaviors influence reproductively relevant outcomes (i.e., sexual behaviors and relationship measures)?”. As outlined in Chapter 5 the analyses related to the first research question followed a two-step process whereby bivariate (zero-order) associations were produced and were succeeded by multivariate analyses.46

6.1.1 Bivariate Analyses

Given the large number of variables included in this study a conventional correlation matrix of all study variables would be impractical. Consequently, four different tables are provided which contain the zero-order associations between the adolescent antisocial conduct measures (Table 6.1), the sexual/reproductive behavior measures (Table 6.2), the control variables and the study variables (Table 6.3), and the phenotypic correlations between the

46 Due to the nature of the analyses following a progression of increased complexity, only those models (bivariate and multivariate) which produced a significant association between adolescent antisocial conduct and sexual/reproductive behaviors in adulthood will be displayed. Statistical significance was set at p < 0.05 for all models. 152

adolescent antisocial conduct measures and the sexual/reproductive measures in adulthood

(Table 6.4). As illustrated in Table 6.3, the control variables included in the study are significantly associated with a number of the antisocial measures and the sexual/reproductive outcomes. Consequently, the control variables appear to be statistically appropriate to include in the multivariate analyses.

The main focus of the bivariate analyses is represented by the results highlighted in Table

6.4 which displays the zero-order correlations between the adolescent antisocial behaviors and the measures tapping the sexual/reproductive behaviors in adulthood. In the vernacular of behavioral genetics, these associations are referred to as the phenotypic correlations. As a reminder, only the significant (p < .05) phenotypic correlations are displayed in Table 6.4. As illustrated, a substantial number of the adolescent antisocial measures are significantly associated with the sexual and reproductive outcomes in adulthood. Discerning an overall pattern, however, it appears that the antisocial measures are associated with measures of sexual behaviors more so than with the reproductive measures. This pattern is best exemplified by observing the associations between the antisocial measures and the composite sexual and reproductive indexes

(outcomes 22 and 23, respectively in Table 6.4). The composite sexual behavior index is significantly associated with each of the adolescent antisocial measures while the composite reproductive behavior index is only significantly associated with one measure of adolescent antisocial behavior (ever arrested as a juvenile).

6.1.2 Multivariate Analyses

Progressing to the multivariate analyses, Table 6.5 displays the results of regression analyses wherein the sexual and reproductive measures are regressed on the antisocial conduct items controlling for the effects of age, race, sex, low self-control, IQ, future outlook, and parent 153

socioeconomic status. Once again, only the findings of models which resulted in a statistically significant association are displayed. Similar to the findings of the bivariate analyses, the multivariate models illustrate that a number of measures of antisocial conduct in adolescence have a significant effect on various sexual and reproductive outcomes in adulthood. The three measures of delinquency (Wave 1, Wave 2, and waves 1 & 2) were all significantly associated with the sexual activity index, the three age of sexual debut measures, the number of any sexual activity partners during adolescence, and the composite sexual behaviors index. Conforming to the bivariate findings, the delinquency measures were not associated with any of the reproductive/relationship measures. In terms of the sexual behavior outcomes, both violent and nonviolent offending in adolescence exhibited similar patterns of association. For example, the violent and nonviolent offending in adolescence indexes were significantly related to age of sexual debut (vaginal, oral, and anal sex), the number of any sexual activity as a juvenile, and the composite sexual behaviors index. The violent and nonviolent offending measures also converged, for the most part, in terms of items with which they were not significantly associated.

However, some differences did arise. For example, the violent offending index (Wave 1 index and the composite Waves 1 & 2 index) were significantly associated with the number of long relationships, the number of pregnancies, and the number of pregnancies out of wedlock. In terms of divergent nonviolent offending associations with the sexual/reproductive outcomes were found between the Wave 1 nonviolent offending index and the number of one-time sexual partners, as well as between the Wave 2 nonviolent offending index and the number of cohabitation partners.

Thus far, the multivariate models indicate that the measures of criminality during adolescence relate primarily to sexual behavior measures and do not relate to the majority of the

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reproductive/relationship outcomes. The next constellation of measures tapping adolescent antisocial behaviors is represented by the criminal justice system items that represent criminal conduct. In line with the measures of criminality, the criminal justice system items display a pattern of association with sexual behavior but not reproductive/relationship behaviors. Overall, involvement with the criminal justice system as an adolescent is significantly associated with a more varied sexual lifestyle, a reduction in the age of sexual debut (vaginal, oral, and anal sex), a greater number of sexual partners as a juvenile, a greater level of dissatisfaction with parenthood, and a greater level of overall sexual activity as measured by the composite sexual behavior index. Serious criminal conduct during adolescence (as measured by multiple arrests as a juvenile) diverged from the other criminal justice items in a few models. Specifically, multiple arrests as a juvenile was significantly associated with an increase in the number of vaginal sex partners, an increase in the number of one-time sexual partners, and an increase in the number of extra-pair sexual partners.

The results presented in this section revealed that while the adolescent antisocial behaviors were associated with a number of the sexual/reproductive outcomes in adulthood at both the bivariate and multivariate levels, the associations were primarily centered on the sexual behaviors (rather than the reproductive behaviors). Indeed, only six of the 13 reproductive and relationship outcomes were statistically associated with any of the adolescent antisocial behavior measures. Additionally, the findings illustrated that involvement with the criminal justice system as a juvenile had similar associations with the sexual/reproductive behavior outcomes as the criminality measures (delinquency, violent offending, and nonviolent offending). Although the pattern between the two types of antisocial measures was similar in terms of significance, the effect sizes of the associations between the criminal justice items and the sexual/reproductive

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outcomes were much higher indicating a more substantive influence. The next step in the analysis is to establish if any of the items included in the study are influenced by genetic factors.

This step is essential to determine if genetic factors may be confounding the association between adolescent antisocial behaviors and sexual/reproductive behaviors in adulthood.

6.2 Research Question 2

The second research question asked, “Are antisocial behaviors and sexual/reproductive behaviors influenced by genetic factors and to what extent is the proportional influence of genetic factors relative to environmental factors?”. In order to assess this research question behavioral genetic methods are required. As indicated in Chapter 5, the behavioral genetic analysis will employ a multi-step process incorporating cross-twin (intraclass) correlations and

ACE decomposition models.

6.2.1 Cross-Twin Correlations

Recall from Chapter 3 that behavioral genetic analyses rest upon the varied degrees of genetic relatedness among kinship pairs. If more genetically related kinship pairs display a greater level of intraclass correlation on an observed phenotype than other kinship pairs, it can be assumed that genetic factors likely influence variance in the phenotype. As outlined, evidence of genetic influence on phenotypic variance is provided when the following pattern of intraclass correlations arises: rMZ > rDZ/FS > rHS (where r = the intraclass correlation coefficient). The findings of the cross-twin correlation analyses are bifurcated by antisocial behavior in adolescence and sexual/reproductive behavior in adulthood.

Table 6.6 displays the results of the intraclass correlation analyses for the adolescent antisocial measures. As illustrated, the majority of antisocial behavior measures conform to a

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pattern that illustrates a potential genetic influence on phenotypic variance. Furthermore, apart from ever being arrested as a juvenile and the number of arrests as a juvenile the intraclass correlations among MZ twins were greater than the intraclass correlations among DZ twins, full siblings, and half-siblings. However, further investigation of these intraclass associations reveals that the effect sizes for MZ twins matched DZ twins on both the ever arrested as a juvenile measure and the number of arrests as a juvenile item. Overall, the findings in Table 6.6 illustrate that there is likely a genetic influence on the variance in all of the adolescent antisocial measures.

The cross-twin correlations for the sexual/reproductive and relationship measures are displayed in Table 6.7. Conforming to the findings for the antisocial behavior measures, the pattern of results for the intraclass correlations illustrated in Table 6.7 indicates that the majority of the measures are likely influenced by genetic factors. More specifically, for 17 of the 23 cross-twin correlations MZ twins had a greater (and significant) correlation coefficient than MZ twins, full siblings, and half-siblingss. In some cases, the discrepancy between the kinship pair types was quite high. For example, for each of the four measures tapping number of sexual partners over the life course the correlation coefficient was at least twice as high for the MZ twins as it was for the DZ/FS pairs. Overall, the findings displayed in Table 6.7 indicate that the sexual/reproductive measures are influenced, although to varying degrees, by genetic factors.

6.2.2 Univariate ACE Models

As outlined above, the cross-twin correlations provide initial evidence that phenotypic variance is influenced by genetic factors. However, the intraclass correlations are unable to indicate the proportion of phenotypic variance that is due to genetic factors and environmental factors. Such a finding can be determined via the use of univariate ACE modeling. Given that

Research Question 2 is concerned with the proportional influence of genetic and environmental 157

factors for each phenotype included in the study, univariate ACE modeling was employed.

Recall that univariate ACE modeling is a structural equation modeling strategy that decomposes the observed variance in a phenotype into three latent factors: A (genetic factors), C (shared environment factors), and E (nonshared environment factors). In addition to the parameter estimates associated with each latent factor, the ACE model also provides indications of significance associated with each latent factor (via confidence intervals). Additionally, model fit statistics are produced allowing for the determination of optimum model fit in a saturated model analysis. As outlined in Chapter 5, both the chi-square statistic and the AIC statistic (in the case of threshold models, the RMSEA statistic) will be used to determine the best fitting model. The best fitting model in a saturated model design is indicated by a non-statistically significant change in model fit statistic (i.e., Δχ2). In order to highlight this process, each individual phenotype included in the study will subjected to univariate ACE modeling analysis and the results of the entire saturated model assessment will be displayed (the best fitting model will be highlighted in bold).47

While each univariate ACE analysis is provided in Tables A.1, A.2, and A.3 a summary of the entire analysis is provided in Table 6.8. The findings presented in Table 6.8 represent the best fitting models for each univariate ACE analysis per phenotype. There are three general observations to be derived from Table 6.8. First, the majority of the items included in the study were influenced by genetic factors. Indeed, only four of the 35 study variables lacked evidence of a genetic influence (as indicated by the best fitting model). Additionally, for the majority of items that do show a genetic effect the magnitude of the effect is substantial. For example, the

47 To reduce the complexity of displaying the univariate ACE model analyses, the ACE model results will be arranged by antisocial measures, sexual behavior measures, and reproductive measures (i.e., three tables each consisting of multiple panels that display the individual univariate ACE model results). 158

range of genetic influence on the antisocial behavior phenotypes is .16 to .95. Second, while genetic factors influenced the majority of the analytical measures the nonshared environment accounted for the bulk of the variance in most of the phenotypes. This was particularly true for the sexual and reproductive items where the proportion of variance accounted for by the nonshared environment did not fall below 40 percent. Third, the shared environmental effect on phenotypic variance across all of the items was minimal and was nonexistent for the majority of the measures. For only one variable, ever parent a child who died, the shared environment component accounted for the greatest proportion of phenotypic variance.

The findings of the cross-twin correlation and univariate ACE model analyses illustrate that genetic factors do influence phenotypic variance for the majority of the study variables.

Additionally, the ACE model results indicate that the nonshared environment contributes a substantial proportion of the causal effect on phenotypic variance. Consequently, analytical strategies which incorporate genetically sensitive designs that also allow for the examination of the nonshared environment are required. The methods employed in the final analytical steps of the current project provide such an analysis.

6.3 Research Question 3

The final research question asked, “Does the association between antisocial behaviors and reproductively relevant outcomes remain after controlling for the influence of genetic and environmental confounds?”. Recall that the bivariate and multivariate analyses illustrated a significant association between some of the antisocial measures and the sexual/reproductive outcomes. Additionally, the intraclass (cross-twin) correlation and univariate ACE model analyses illustrated that for the majority of the phenotypes of interest in the current study genetic factors contributed to the observed variance and the environmental effect was largely due to 159

nonshared environmental influences. Therefore, according to the methodological principles outlined in Chapter 3 in order to gain a robust understanding of the association between antisocial conduct and sexual/reproductive behaviors a genetically sensitive design which allows for the examination of the nonshared environment is necessary. The DF model and the MZ twin difference approach represent two modeling techniques that provide such an examination.

6.3.1 DF Models

Recall that the DF regression modeling technique treats the kinship pair as the unit of analysis. Therefore, the score on an observed phenotype for one twin/sibling is used to predict the co-twin/sibling’s score on the same phenotype while simultaneously controlling for shared genetic and shared environmental factors. Additionally, recall that the DF model allows for the inclusion of difference scores which also serve to control for the influence of shared genetic and shared environmental factors on the difference score variable as well as for the association between the difference score variable and the predicted phenotypic outcome. The primary difference score of interest in the current analysis is antisocial behavior in adolescence.

Consequently, each DF model includes a difference score for each measure of antisocial conduct

(this difference score also represents a unique, isolated component of the nonshared effect on the outcome variable). Additionally, given that multiple types of kinship pairs are included in the analyses the DF models also control for the effect of differences in age, race, low self-control,

IQ, and future outlook. Finally, given that sex was significantly associated as a control variable in a number of the multivariate analyses (results not shown) and there are solid theoretical reasons to do so, the results of the DF analyses are presented for the full sample, males only

(male-male kinship pairs), and females only (female-female kinship pairs). In order to highlight the effect of sex on the observed associations the DF results are presented in one table (only 160

those models which resulted in a significant association between the antisocial difference score variable and the sexual/reproductive outcome are displayed in Table 6.9).

The results of the DF model analyses are displayed in Table 6.9. As illustrated, there are a number of models which indicate a significant association between various antisocial difference score measures and the sexual/reproductive behavior outcomes. Recall that the DF model is an extremely conservative test of an association between phenotypes and therefore statistically significant associations in a DF model can be considered robust. In addition to this general observation, there are a number of other findings which warrant attention. These observations will be organized by antisocial measure type in the following sections.

6.3.2 DF Model Results for Adolescent Antisocial Behaviors

The findings illustrated in the first section of Table 6.9 indicate that, overall, differences in adolescent antisocial behaviors (delinquency, violent offending, and nonviolent offending) have an effect on a number of sexual behaviors beyond the influence of shared genetic and shared environmental factors. In the first model it can be seen that delinquency at Wave 1 does not have an association with lifetime number of vaginal sex partners for females or for the full sample but does for male-male kinship pairs. More specifically, the positive regression coefficient (b = 0.38) indicates that within each kinship pair the sibling with the higher score on the delinquency measure at Wave 1 also reported a greater number of lifetime vaginal sex partners at Wave 4. This was the only model which exhibited a significant association between differences in delinquency at Wave 1 and lifetime number of vaginal sex partners.

The second model to illustrate a significant association was found only for females. The measure of delinquency at Wave 2 was found to be significantly associated with variance in the sexual activity index for female kinship pairs, although the effect size was small (b = 0.03). 161

Consistent with the multivariate models outlined above, some of the antisocial behavior measures were significant predictors of age of sexual debut. However, this association was exhibited only in the male-male kinship dyads. More specifically, those males within a sibling pair who reported a higher level of delinquency at Wave 2 (b = -0.09) and on the composite waves 1 and 2 item (b = -0.04) also reported an earlier age of sexual debut for oral sex. This association was not found for the other age of sexual initiation measures (vaginal sex and anal sex). Finally, an association between the composite delinquency measure (waves 1 and 2) and the lifetime number of long relationships (more than 6 months) was found for female kinship pairs. Among female-female siblings, the sibling who reported a greater level of delinquency over the course of her adolescence also reported a greater number of long-term relationships (b =

0.02), relative to her sister.

Differences in violent offending within kinship pairs were not significantly associated with the majority of the sexual/reproductive outcomes. Only two models exhibited a significant association. The first of these models was between violent offending at Wave 2 and the lifetime number of cohabitation partners. This significant association (b = 0.08) was found for the full sample but not for either male-male or female-female kinship pairs. The second model illustrating a significant association between violent offending and sexual/reproductive outcomes was between the composite violent offending measure (waves 1 and 2) and the lifetime number of short relationships (less than 6 months). Notably, this association (b = 0.13) was isolated to female kinship pairs where those siblings reporting a greater amount of violent offending during adolescence also reported a greater number of short relationships during their lives. This finding is in contrast to the model that found a positive association between adolescent delinquency and long-term relationships.

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Nonviolent offending during adolescence significantly predicted variance in a number of sexual and reproductive measures in adulthood. These findings also varied markedly by sex.

For males, a significant association was found between nonviolent offending and age of oral sex initiation where those male siblings reporting a greater level of nonviolent offending also reported an earlier age of oral sex debut (b = -0.15). The remaining significant associations were limited to female kinship pairs. Those female siblings who reported higher levels of nonviolent offending at Wave 1 reported a reduced level of sexual activity (b = -0.03) and an increased number of long-term relationships (b = 0.04), relative to their co-sibling. Additionally, differences in nonviolent offending among sibling pairs over the course of adolescence was found to be positively associated with lifetime number of long relationships for the full sample (b

= 0.02) and female kinship pairs (b = 0.03), but not males.

6.3.3 DF Model Results for Adolescent Criminal Behavior

Overall, measures of adolescent criminal behavior (involvement with the criminal justice system) were more often associated with the sexual and reproductive outcomes than the adolescent delinquency measures. Close inspection of Table 6.9 reveals that save for a few models, the significant associations in the full sample column appear to be driven by the associations among either the male kinship pairs or the female kinship pairs rather than both (i.e., the results once again diverged based on sex). Therefore, the description of these findings will proceed by sex.

For female-female kinship pairs significant results were only revealed for the first two criminal justice measures. For instance, within female-female kinship pairs the sister who reported being arrested as a juvenile also reported an earlier age of vaginal sex debut (b = -1.02) but a fewer number of sexual partners as a juvenile (b = -4.79). This criminal justice measure 163

was also associated with a significant reduction in the number of times married among the female sibling pairs (b = -0.77). In terms of the number of arrests experienced as a juvenile, those siblings reporting a higher number of arrests during adolescence also reported an older age of vaginal sex debut (b = 1.02), a reduced number of sexual partners during adolescence (b = -

4.80), and a reduced number of times married (b = -0.77) relative to their sisters. There was insufficient variation on the arrested more than once as a juvenile measure among the female- female kinship pairs and thus there are no results to report for those models.

The findings revealed for male-male differences in the criminal justice measures contrasted drastically from the females. The only model which exhibited a similar pattern across the sexes was the significant negative association between being arrested as a juvenile and an earlier age of vaginal sexual debut. Overall, those male siblings who reported being arrested as a juvenile also reported a more varied sexual lifestyle (sexual activity index; b = 0.43), a greater number of lifetime sexual partners (b = 5.77), a greater number of sexual partners during adolescence (b = 3.16), a greater number of one-time sexual partners (b = 4.05), and riskier sexual behavior (b = 0.34) than their brothers who were not arrested as a juvenile.

The association with sexual and reproductive behaviors was not limited to one-time arrests in the male kinship pairs. The number of times arrested measure was associated with a variety of sexual and reproductive outcomes. In all, the sibling who reported a greater number of arrests during adolescence also reported an earlier age of vaginal sex debut (b = -1.37), a greater number of sexual partners (b = 4.22), a greater number of sexual partners during adolescence (b

= 2.61), a riskier sexual lifestyle (b = 0.27), and a higher score on the composite sexual behaviors index (b = 0.27) relative to their male sibling. The effect was not limited to sexual behaviors, however, as the sibling with the greater number of arrests as a juvenile also reported a higher

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frequency of marriages (b = 0.27), and a higher score on the composite reproductive behavior index (b = 0.26).

The above pattern of results for male kinship pairs was similar for the multiple arrest measure of adolescent criminal behavior. Once again, the sibling who reported multiple arrests as an adolescent also reported an earlier age of vaginal sex initiation (b = -2.70), a greater number of lifetime vaginal sex partners (b = 8.31), a greater number of sexual partners (b =

8.79), a higher frequency of one-time sexual encounters (b = 7.58), a riskier sexual lifestyle (b =

0.63), a higher score on the composite sexual behaviors measure (b = 0.65), a greater number of marriages (b = 0.43), a greater likelihood of being married (b = 1.96; Odds Ratio = 7.08), and a higher score on the composite reproductive behavior item (b = 0.51), relative to their male sibling who was never arrested or arrested only once as a juvenile.

The findings thus far indicate that antisocial behaviors during adolescence have a significant effect on sexual and reproductive items measured during adulthood. However, the pattern of results appears to vary based on sex and the type of antisocial measure employed. To further explore the associations a final step in the analysis was taken. The next section outlines the results of the MZ difference score analyses.

6.3.4 MZ Difference Score Models

Recall that the MZ difference score model construction is similar to the DF model in a number of ways. First, both are regression-based techniques wherein a twin’s score on the outcome measure is predicted by his or her co-twin’s score (while controlling for other influences). Second, the difference score creation process is the same for both models: twin 2’s phenotypic score is subtracted from twin 1’s phenotypic score on the same measure. This difference score accounts for shared genetic and shared environmental factors with the remaining 165

variance representing the nonshared environmental influence. Finally, both the DF and the MZ difference score models treat the twin pair as the unit of analysis.

Despite these similarities there is one key difference between the DF and MZ difference score model: the MZ difference score technique employs only kinship pairs wherein individuals share 100 percent of their DNA. Consequently, the magnitude of shared genetic effect that is accounted for in the difference score can be considerable. It is this feature which provides the

MZ difference score the strong methodological reputation in behavioral genetics (see Vitaro et al., 2009). The MZ difference score was employed in the current analyses both for its methodological rigor but also to assess whether the unaccounted genetic variance (i.e., nonshared genetic factors) present in the DF model strategy had an influence on the observed associations.48

Of importance in employing the MZ difference approach are two key factors. First, in order for the models to be of methodological use there must be variation among the MZ twins included in the analytical sample. Therefore, assessment of phenotypic variation was conducted prior to the MZ difference score analyses. This type of analysis is typically done by examining the descriptive statistics for the MZ difference scores (Beaver, 2008), therefore the descriptive statistics for the MZ difference scores are provided in Table 6.10. As illustrated in Table 6.10, while most of the difference scores hover around an average of zero (which is to be expected) there is variance as evidenced by the ranges associated with each measure. Therefore, there appears to be variation in the MZ difference scores in the analytical sample.

The second key factor when conducting an MZ difference score is the ability to generalize the results beyond the MZ twin sample. This topic was thoroughly covered in

48 It should also be noted here that the MZ difference score dataset was not double-entered prior to the analyses, following convention in behavioral genetic analyses (Beaver, 2008). However, in order to ensure against a systematic bias twin identification (i.e., twin 1 versus twin 2) was completed by a randomized process. 166

Chapter 3 and will be discussed in the next chapter as well. Germane to the current project is not just the ability to generalize beyond the MZ twin sample but the ability to indicate that the results of the MZ difference analyses are applicable to the rest of the non-MZ twins included in the current study. In other words, given that the MZ difference score analyses represent the final step in the progression of analytical steps it is important to provide evidence that the modeling strategy is relevant to those steps by which it was preceded.49 Therefore, analyses were conducted to assess whether the MZ twins presented any average differences from the non-MZ twins included in the overall analytical sample. The results of these analyses are provided in

Table 6.11. As illustrated in Table 6.11 the MZ twins do not differ, on average, in a significant manner from the rest of the analytical sample on the majority of study variables. Indeed, only four of the 39 study variables presented a significant difference with the MZ twins scoring lower on average on three of the four measures (they were slightly older, on average, than the rest of the analytical sample).

On establishing that the MZ twins in the analytical sample do not significantly differ from the rest of the sample and exhibit sufficient phenotypic variation on the observed measures, the MZ difference score analyses were conducted. Recall that the MZ difference score analysis entails predicting twin 1’s difference score on an outcome measure by regressing it on twin 2’s difference score on the same measure. A significant regression coefficient represents a nonshared environmental factor effect. The results of the MZ difference score analyses are presented in Table 6.12. Following the organization of the DF results section the following sections will be bifurcated by adolescent antisocial behavior measure.

49 This concern is not as crucial as the first (i.e., phenotypic variation) or the concern of generalization beyond the analytical sample as the MZ twins were incorporated into the DF analysis, indeed they are a necessary component. 167

6.3.5 MZ Difference Score Models for the Adolescent Delinquency Measures

As displayed in Table 6.12, the MZ difference score analyses resulted in a drastic drop in the overall number of models that produced significant associations. However, 12 models revealed a significant association between antisocial behaviors measured at adolescence and sexual/reproductive outcomes measured in adulthood. Violent offending at Wave 1 was the only measure of violence to predict variation in sexual/reproductive behaviors. Specifically, the MZ twin reporting a greater level of violent offending in early adolescence also reported a more varied sexual lifestyle (sexual activity index; b = 0.05). The remaining significant associations were between nonviolent offending and the outcome measures. Nonviolent offending at Wave 1 was positively associated with having an extra-pair sexual partner in adulthood. Therefore, the twin exhibiting higher levels of nonviolent offending during early adolescence later reported an increased likelihood (b = 0.02), relative to his/her co-twin, of having an extra-pair sexual relationship. Nonviolent offending at Wave 2 was significantly associated with the number of sexual partners as a juvenile and the number of lifetime cohabitation partners. Specifically, the

MZ twin reporting a greater amount of nonviolent offending reported having fewer sexual partners during adolescence (b = -0.48) but reported cohabitating with a romantic or sexual partner more often than his/her co-twin (b = 0.08). Finally, this last association was also seen between the composite adolescent nonviolent offending index (waves 1 and 2) and lifetime number of cohabitation partners (b = 0.04).

6.3.6 MZ Difference Score Models for the Adolescent Criminal Behavior Measures

In similar fashion to the DF models, the criminal justice system measures exhibited a greater number of associations with the sexual/reproductive outcomes than the adolescent

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delinquency items. However, only 7 models reached statistical significance in the MZ difference score analyses. The twin who reported at least one arrest as a juvenile also reported an older age of oral sex initiation (b = 2.15). This was, however, the only age of sexual debut outcome measure to be statistically associated with any of the antisocial measures. Additionally, the MZ twin who reported a greater number of arrests also reported a more varied sexual lifestyle (sexual activity index; b = 0.25), a greater number of pregnancies out of wedlock (b = 0.10), and a higher score on the overall/composite sexual behaviors index (b = 0.11), relative to his/her co-twin.

Finally, the twin who reported being arrested more than once as a juvenile also reported a greater number of sexual partners during adolescence (b = 9.44), engaging in a riskier sexual lifestyle (b

= 1.54), and a greater score on the overall/composite sexual behaviors index (b = 0.81) than his/her co-twin who was never arrested or arrested only once.

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Table 6.1: Zero-order correlation matrix for all adolescent antisocial behavior measures.

Item (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

(1) Delinquency - W1 1 (2) Delinquency - W2 0.58* 1 (3) Delinquency - W1 &W2 0.91* 0.86* 1 (4) Violent offending - W1 0.79* 0.41* 0.70* 1 (5) Violent offending - W2 0.44* 0.72* 0.64* 0.50* 1 (6) Violent offending - W1 &W2 0.74* 0.63* 0.77* 0.90* 0.83* 1 (7) Nonviolent offending – W1 0.92* 0.54* 0.85* 0.58* 0.33* 0.54* 1 (8) Nonviolent offending – W2 0.52* 0.92* 0.78* 0.30* 0.50* 0.44* 0.56* 1 (9) Nonviolent offending - W1 &W2 0.83* 0.81* 0.93* 0.51* 0.46* 0.56* 0.91* 0.86* 1 (10) Ever arrested as a juvenile 0.23* 0.18* 0.22* 0.20* 0.17* 0.21* 0.22* 0.16* 0.22* 1 (11) Number of arrests as a juvenile 0.22* 0.15* 0.25* 0.20* 0.17* 0.25* 0.23* 0.03 0.16* 0.35* 1 (12) Arrested 1+ times as a juvenile 0.23* 0.15* 0.21* 0.21* 0.19* 0.24* 0.23* 0.12* 0.20* 0.75* 0.42* 1

Notes: * p < 0.05; W – wave.

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Table 6.2: Zero-order correlation matrix for all sexual and reproductive behavior measures.

Item (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

(1) Sexual activity index 1

(2) Age of debut, vaginal sex -0.18* 1

(3) Age of debut, oral sex -0.25* 0.65* 1

(4) Age of debut, anal sex 0.11* 0.27* 0.41* 1

(5) No. of vaginal sex partners 0.26* -0.23* -0.22* -0.10* 1

(6) No. of any sex activity partners 0.22* -0.23* -0.25* -0.17* 0.84* 1

(7) No. of any sex activity partners, before 18 0.16* -0.39* -0.35* 0.20* 0.46* 0.48* 1

(8) No. of one-time sexual partners 0.18* -0.15* -0.17* -0.10* 0.57* 0.68* 0.39* 1

(9) Risky sexual behaviors index 0.09* -0.16* -0.10* -0.13* 0.24* 0.22* 0.19* 0.19* 1

(10) Sexual behavior index 0.44* -0.33* -0.27* -0.16* 0.78* 0.79* 0.59* 0.66* 0.46* 1 (11) No. of times married 0.15* -0.01 -0.01 0.06 -0.08* -0.12* -0.01 -0.06* -0.04 -0.10* (12) Ever married 0.14* 0.02 -0.01 0.07 -0.10* -0.14* -0.02 -0.07* -0.04 -0.13* (13) No. of cohabitation partners 0.17* -0.25* -0.18* -0.07 0.28* 0.25* 0.21* 0.15* 0.13* 0.31* (14) No. of long relationships (6+ months) 0.08* -0.05 -0.04 0.04 0.20* 0.17* 0.05* 0.13* 0.09* 0.23* (15) No. of short relationships (<6 months) 0.09* -0.07* -0.10* -0.05 0.27* 0.40* 0.09* 0.33* 0.08* 0.33* (16) No. of extra-pair sexual partners 0.07* -0.09* 0.01 -0.07 0.26* 0.25* 0.12* 0.12* 0.18* 0.59* (17) Have an extra-pair sexual partner 0.06* -0.10* 0.01 -0.03 0.19* 0.20* 0.11* 0.11* 0.15* 0.56* (18) No. of pregnancies/impregnations 0.09* -0.27* -0.11* 0.01 0.10* 0.04 0.17* 0.04 0.08* 0.17* (19) No. of live births -0.07* -0.12* -0.06 0.07 -0.05 -0.07* 0.02 -0.03 0.02 -0.04 (20) No. of pregnancies out of wedlock 0.02 -0.18* -0.06* -0.03 0.22* 0.19* 0.16* 0.11* 0.09* 0.41* (21) Ever parent a child who died 0.02 -0.10 -0.03 -0.03 0.11* 0.12* 0.01 0.02 0.01 0.07* (22) Parental dissatisfaction index 0.06 -0.08* -0.10* 0.05 0.09* 0.07* 0.07* 0.03 0.08* 0.12* (23) Reproductive behavior index 0.14* -0.13 -0.06* 0.06 -0.03 -0.09* 0.06* -0.04 0.01 -0.01

171

Table 6.2: Continued.

Item (11) (12) (13) (14) (15) (16) (17) (18) (19) (20)

1 (11) No. of times married 0.94* 1 (12) Ever married -0.22* -0.25* 1 (13) No. of cohabitation partners -0.20* -0.21* 0.09* 1 (14) No. of long relationships (6+ months) -0.15* -0.17* 0.16* 0.18* 1 (15) No. of short relationships (<6 months) -0.20* -0.22* 0.11* 0.29* 0.24* 1 (16) No. of extra-pair sexual partners -0.22* -0.24* 0.12* 0.24* 0.16* 0.87* 1 (17) Have an extra-pair sexual partner 0.30* 0.26* 0.17* -0.13* -0.04 -0.03 -0.04 1 (18) No. of pregnancies/impregnations 0.15 0.13* -0.01 0.15* -0.08* -0.07* 0.62* 0.62* 1 (19) No. of live births (20) No. of pregnancies out of wedlock -0.13* -0.15* 0.14* 0.06* 0.09* 0.15 0.14* 0.32* 0.07* 1 (21) Ever parent a child who died 0.05 -0.03 0.03 0.05 0.03 0.01 0.01 0.06 0.20* 0.08* (22) Parental dissatisfaction index -0.04 -0.08* 0.09* 0.03 0.01 0.07* 0.08* 0.08* 0.12* 0.09* (23) Reproductive behavior index 0.85* 0.84* -0.10* -0.22* -0.15* -0.18* -0.20* 0.68* 0.67* 0.06*

Item (21) (22) (23)

1 (21) Ever parent a child who died 0.01 1 (22) Parental dissatisfaction index 0.10* 0.02 1 (23) Reproductive behavior index

Notes: * p < 0.05; No. – number.

172

Table 6.3: Zero-order correlations between the antisocial measures, the sexual/reproductive measures, and the control variables. Future Item Age Sex Race LSC IQ SES Outlook

-0.01 0.17* -0.06* 0.33* -0.03 0.19* -0.05* Delinquency -W1 -0.10* 0.14* -0.03 0.33* 0.01 0.08* -0.03 Delinquency -W2 -0.06* 0.18* -0.05* 0.37* -0.01 0.16* -0.04* Delinquency -W1 & W2 -0.03 0.20* -0.11* 0.23* -0.10* 0.19* -0.07* Violent offending - W1 -0.02* 0.17* -0.08* 0.22* -0.07* 0.09* -0.04 Violent offending - W2 -0.05* 0.22* -0.11* 0.25* -0.10* 0.17* -0.06* Violent offending - W1 & W2 0.01 0.13* -0.01 0.29* 0.01 0.15* -0.04 Nonviolent offending -W1 -0.11* 0.12* 0.02 0.31* 0.04 0.05* -0.02 Nonviolent offending -W 2 -0.07* 0.15* 0.01 0.33* 0.03 0.12* -0.04 Nonviolent offending -W 1 & W2 -0.04 0.11* -0.01 0.07* -0.03 0.04 -0.03 Ever arrested as a juvenile -0.03 0.06* -0.04 0.08* -0.03 0.06* -0.03 Number of arrests as a juvenile -0.04 0.10* -0.02 0.06* -0.04 0.06* -0.05* Arrested 1+ times as a juvenile 0.09* 0.03 0.07* -0.18* 0.16* -0.09* 0.08* Age of debut, vaginal sex 0.11* -0.15* -0.17* -0.12* -0.01 -0.01 -0.05 Age of debut, oral sex 0.22* -0.09* -0.10* -0.10* 0.03 0.01 -0.01 Age of debut, anal sex 0.06* 0.14* -0.04 0.18* -0.01 0.06* -0.01 No. of vaginal sex partners 0.04 0.16* -0.04 0.11* 0.01 0.05* 0.01 No. of any sex activity partners -0.05* 0.03 -0.01 0.18* -0.06* 0.09* -0.04 No. of any sex activity partners, before 18

173

Table 6.3: Continued.

Future Item Age Sex Race LSC IQ SES Outlook

0.04 0.12* 0.01 0.06 0.04 0.05* 0.01 No. of one-time sexual partners 0.07* 0.01 -0.07* 0.14* -0.10* 0.06* -0.10* Risky sexual behaviors index 0.06* 0.09* -0.09* 0.16* -0.03 0.09* -0.04 Sexual behavior index 0.19* -0.09* 0.18* -0.05 0.01 -0.08* 0.03 No. of times married 0.18* -0.09* 0.18* -0.06 0.01 -0.09* 0.05 Ever married 0.01 0.01 -0.02 0.14* -0.06* 0.11* -0.09* No. of cohabitation partners -0.01 0.09* -0.09* 0.04 0.03 -0.01 0.01 No. of long relationships (6+ months) 0.01 0.14* -0.01 0.04 0.06* 0.06* -0.01 No. of short relationships (<6 months) 0.04 0.08* -0.10* 0.02 -0.04 0.06* -0.03 No. of extra-pair sexual partners 0.04 0.04 -0.11* 0.03 -0.05 0.05* -0.02 Have an extra-pair sexual partner 0.11* -0.19* -0.07* 0.04 -0.21* 0.05* -0.11* No. of pregnancies/impregnations 0.03 -0.11* -0.05 0.03 -0.13* 0.02 -0.07* No .of pregnancies resulting in a live birth 0.04 -0.05* -0.20* 0.02 -0.12* 0.08* -0.08* No. of pregnancies out of wedlock 0.01 -0.02 -0.07* 0.02 -0.03 0.05 -0.02 Ever parent a child who died 0.04 0.06 -0.02 0.19* -0.06 0.10* -0.02 Parental dissatisfaction index 0.18* -0.16* 0.09* -0.02 -0.11* -0.03 -0.03 Reproductive behavior index

Notes: * p < 0.05; All control variables are from wave 1; LSC - low self-control (waves 1 & 2); SES - parent's socioeconomic status; Sex is coded where 0 = Female, 1 = Male; Race is coded where 0 = nonwhite, 1 = white; W – wave; No. – Number.

174

Table 6.4: Phenotypic correlations (zero-order) between adolescent antisocial measures and sexual/reproductive behavior outcomes for the entire study sample.

Sexual/Reproductive and Relationship Outcomes Adolescent Antisocial Measures 1 2 3 4 5 6 7 8 9 10 11 12

Delinquency -W1 0.12 -0.22 -0.20 -0.10 0.17 0.15 0.22 0.12 0.09 NS -0.05 0.15 Delinquency -W2 0.12 -0.19 -0.17 -0.10 0.12 0.11 0.17 0.10 0.09 NS -0.06 0.15 Delinquency -W1 & W2 0.14 -0.23 -0.22 -0.12 0.18 0.15 0.21 0.12 0.10 NS -0.05 0.17

Violent offending - W1 0.06 -0.20 -0.18 -0.14 0.15 0.14 0.20 0.11 0.09 NS NS 0.12 Violent offending - W2 NS -0.19 -0.15 -0.10 0.10 0.10 0.17 0.11 0.10 NS -0.06 0.13 Violent off. - W1 & W2 0.06 -0.22 -0.19 -0.15 0.16 0.14 0.21 0.12 0.11 NS NS 0.14

Nonviolent offending -W1 0.13 -0.18 -0.18 -0.08 0.15 0.13 0.17 0.10 0.08 NS -0.05 0.13 Nonviolent offending -W2 0.13 -0.16 -0.16 -0.08 0.11 0.09 0.14 0.07 0.08 NS -0.06 0.15 Nonviolent off. -W1 & W2 0.14 -0.19 -0.20 -0.10 0.15 0.13 0.17 0.09 0.09 NS -0.06 0.16

Ever arrested as a juvenile 0.08 -0.15 -0.14 NS 0.07 0.10 0.15 0.05 0.07 -0.05 -0.15† 0.08 No. of arrests as a juvenile NS -0.08 -0.08 NS 0.05 0.05 0.08 NS NS NS NS NS Arrested 1+ times as a juv. 0.05 -0.16 0.14 NS 0.08 0.11 0.18 0.06 0.09 -0.06 -0.21† 0.08

175

Table 6.4: Continued.

Sexual/Reproductive and Relationship Outcomes Adolescent Antisocial Measures 13 14 15 16 17 18 19 20 21 22 23

NS 0.06 NS NS 0.08 NS 0.05 NS 0.10 0.20 NS Delinquency -W1 0.07 0.09 NS NS NS NS 0.05 NS 0.13 0.17 NS Delinquency -W2 0.05 0.08 NS NS 0.05 NS 0.05 NS 0.11 0.20 NS Delinquency -W1 & W2

0.05 0.05 0.07 0.06 0.09 NS 0.06 NS NS 0.18 NS Violent offending - W1 0.05 0.08 NS 0.06 0.06 NS 0.09 0.07 0.08 0.16 NS Violent offending - W2 0.05 0.06 0.06 0.05 0.08 NS 0.08 NS NS 0.19 NS Violent off. - W1 & W2

NS 0.06 NS NS 0.05 NS NS NS 0.10 0.16 NS Nonviolent offending -W1 Nonviolent offending -W2 0.05 0.09 NS NS NS NS NS NS 0.14 0.15 NS

NS 0.08 NS NS NS NS NS NS 0.12 0.17 NS Nonviolent off. -W1 & W2

0.06 NS NS NS NS NS 0.05 NS 0.10 0.25 -0.06 Ever arrested as a juvenile NS NS NS NS NS NS NS NS NS 0.05 NS No. of arrests as a juvenile NS 0.05 0.06 0.05† 0.06 NS 0.22† NS NS 0.13 NS Arrested 1+ times as a juv.

Notes: Significance set at p < .05; NS - Nonsignificant; † tetrachoric correlation; No. - Number; Legend: 1 - Sexual activity index; 2 - Age of debut, vaginal sex; 3 - Age of debut, oral sex; 4 - Age of debut, anal sex; 5 - Number of vaginal sex partners, ever; 6 - Number of any sex activity partners, ever; 7 - Number of any sex activity partners, before 18; 8 - Number of one-time sexual partners; 9 - Risky sexual behaviors index; 10 - Number times married; 11 - Ever married; 12 - Number of cohabitation partners; 13 - Number of long relationships (6+ months); 14 - Number of short relationships (<6 months); 15 - Number of extra-pair sexual partners; 16 - Currently have extra-pair sexual partner; 17 - Number of pregnancies/impregnations; 18 - Number of pregnancies resulting in live birth; 19 - Number of pregnancies out of wedlock; 20 - Ever parent a child who died; 21 - Parental dissatisfaction index; 22 - Sexual behavior index; 23 - Reproductive behavior index.

176

Table 6.5: Significant multivariate associations between antisocial conduct in adolescence and sexual and reproductive behaviors in adulthood.

Sexual/Reproductive and Relationship Outcomes (Wave 4) Adolescent Antisocial Measures 1^ 2^ 3^ 4^ 5§ 6§ b SE b SE b SE b SE b SE b SE

Delinquency 0.14 0.01 -0.10 0.02 -0.10 0.02 -0.10 0.04 NS -- NS -- Wave 1 0.02 0.02 -0.11 0.02 -0.10 0.02 -0.11 0.05 NS -- 0.02 0.01 Wave 2 0.01 0.01 -0.07 0.01 -0.07 0.01 -0.07 0.03 NS -- NS -- Waves 1 & 2

Violent Offending 0.03 0.02 -0.22 0.04 -0.25 0.04 -0.22 0.10 NS -- NS -- Wave 1 -0.23 0.06 -0.17 0.06 -0.36 0.14 NS -- NS -- Wave 2 NS -- -0.15 0.03 -0.15 0.03 -0.20 0.07 NS -- NS -- Waves 1 & 2 NS --

Nonviolent Offending NS -- -0.10 0.03 -0.12 0.03 -0.14 0.06 NS -- NS -- Wave 1 0.03 0.01 -0.14 0.03 -0.14 0.03 NS -- NS -- NS -- Wave 2 0.01 0.01 -0.08 0.02 -0.09 0.02 -0.10 0.04 NS -- NS -- Waves 1 & 2

Criminal Justice Items 0.58 0.08 -1.80 0.38 -1.63 0.40 -2.21 0.81 NS -- NS -- Ever arrested as a juvenile 0.13 0.03 -0.34 0.14 NS -- NS -- NS -- NS -- No. of arrests as a juvenile 0.60 0.09 -1.90 0.63 -1.60 0.64 NS -- 0.48 0.24 NS -- Arrested 1+ times as a juv.

177

Table 6.5: Continued.

Sexual/Reproductive and Relationship Outcomes (Wave 4) Adolescent Antisocial Measures 7§ 8§ 9^ 10§ 11† 12§ b SE b SE b SE b SE b SE b SE

Delinquency 0.03 0.01 0.02 0.01 NS -- NS -- NS -- NS -- Wave 1 0.04 0.01 NS -- NS -- NS -- NS -- NS -- Wave 2 0.02 0.01 NS -- NS -- NS -- NS -- NS -- Waves 1 & 2

Violent Offending 0.07 0.02 NS -- NS -- NS -- NS -- NS -- Wave 1 0.09 0.03 NS -- NS -- NS -- NS -- NS -- Wave 2 0.05 0.02 NS -- NS -- NS -- NS -- NS -- Waves 1 & 2

Nonviolent Offending 0.03 0.01 0.04 0.02 NS -- NS -- NS -- NS -- Wave 1 0.06 0.01 NS -- NS -- NS -- NS -- 0.03 0.02 Wave 2 0.03 0.01 NS -- NS -- NS -- NS -- NS -- Waves 1 & 2

Criminal Justice Items 0.73 0.24 NS -- NS -- NS -- NS -- NS -- Ever arrested as a juvenile 0.12 0.05 0.15 0.07 NS -- NS -- NS -- NS -- No. of arrests as a juvenile 0.98 0.33 0.95 0.34 NS -- NS -- NS -- NS -- Arrested 1+ times as a juv.

178

Table 6.5: Continued.

Sexual/Reproductive and Relationship Outcomes (Wave 4) Adolescent Antisocial Measures 13§ 14§ 15§ 16† 17§ 18§ b SE b SE b SE b SE b SE b SE

Delinquency NS -- NS -- NS -- NS -- NS -- NS -- Wave 1 NS -- NS -- NS -- NS -- NS -- NS -- Wave 2 NS -- NS -- NS -- NS -- NS -- NS -- Waves 1 & 2

Violent Offending 0.06 0.03 NS -- NS -- NS -- 0.05 0.02 NS -- Wave 1 NS -- NS -- NS -- NS -- NS -- NS -- Wave 2 0.03 0.02 NS -- NS -- NS -- 0.03 0.01 NS -- Waves 1 & 2

Nonviolent Offending NS -- NS -- NS -- NS -- NS -- NS -- Wave 1 NS -- NS -- NS -- NS -- NS -- NS -- Wave 2 NS -- NS -- NS -- NS -- NS -- NS -- Waves 1 & 2

Criminal Justice Items NS -- NS -- NS -- NS -- NS -- NS -- Ever arrested as a juvenile NS -- NS -- NS -- NS -- NS -- NS -- No. of arrests as a juvenile NS -- NS -- 1.46 0.66 NS -- NS -- NS -- Arrested 1+ times as a juv.

179

Table 6.5: Continued.

Sexual/Reproductive and Relationship Outcomes (Wave 4) Adolescent Antisocial Measures 19§ 20† 21^ 22˟ 23˟ b SE b SE b SE b SE b SE

Delinquency NS -- NS -- NS -- 0.01 0.01 NS -- Wave 1 NS -- NS -- NS -- 0.02 0.01 NS -- Wave 2 NS -- NS -- NS -- 0.01 0.01 NS -- Waves 1 & 2

Violent Offending 0.08 0.04 NS -- NS -- 0.03 0.01 NS -- Wave 1 NS -- NS -- NS -- 0.03 0.01 NS -- Wave 2 0.05 0.02 NS -- NS -- 0.02 0.01 NS -- Waves 1 & 2

Nonviolent Offending NS -- NS -- NS -- NS -- NS -- Wave 1 NS -- NS -- NS -- 0.02 0.01 NS -- Wave 2 NS -- NS -- NS -- 0.01 0.01 NS -- Waves 1 & 2

Criminal Justice Items NS -- NS -- 2.13 0.93 0.32 0.13 NS -- Ever arrested as a juvenile NS -- NS -- 0.79 0.27 0.08 0.04 NS -- No. of arrests as a juvenile NS -- NS -- 1.80 0.73 0.66 0.23 NS -- Arrested 1+ times as a juv.

Notes: Significance set at p < 0.05; NS - Nonsignificant; ^ OLS regression model; † logistic regression model; § Poisson regression model; All models controlled for age, sex, race, low self-control (waves 1 & 2), IQ (wave 1), future outlook (wave 1), and parent SES (wave 1); All models employed Huber-White standard errors; No. – Number; Legend: 1 - Sexual activity index; 2 - Age of debut, vaginal sex; 3 - Age of debut, oral sex; 4 - Age of debut, anal sex; 5 - Number of vaginal sex partners, ever; 6 - Number of any sex activity partners, ever; 7 - Number of any sex activity partners, before 18; 8 - Number of one-time sexual partners; 9 - Risky sexual behaviors index; 10 - Number times married; 11 - Ever married; 12 - Number of cohabitation partners; 13 - Number of long relationships (6+ months); 14 - Number of short relationships (<6 months); 15 - Number of extra-pair sexual partners; 16 - Currently have extra-pair sexual partner; 17 - Number of pregnancies/impregnations; 18 - Number of pregnancies resulting in live birth; 19 - Number of pregnancies out of wedlock; 20 - Ever parent a child who died; 21 - Parental dissatisfaction index; 22 - Sexual behavior index; 23 - Reproductive behavior index.

180

Table 6.6: Cross-twin (intraclass) correlations for adolescent antisocial behavior measures by level of genetic relatedness. DZ Twins & Full Antisocial Measures All Pairs MZ Twins Half-Siblings Siblings

Delinquency Wave 1 0.33* 0.47* 0.29* 0.26* Wave 2 0.28* 0.33* 0.30* 0.15* Waves 1 & 2 0.37* 0.49* 0.37* 0.15* Violent Offending Wave 1 0.35* 0.60* 0.27* 0.19* Wave 2 0.28* 0.32* 0.25* 0.28* Waves 1 & 2 0.40* 0.58* 0.36* 0.23* Nonviolent Offending Wave 1 0.28* 0.42* 0.23* 0.27* Wave 2 0.26* 0.40* 0.26* 0.13* Waves 1 & 2 0.34* 0.48* 0.32* 0.23* Criminal Justice Items Ever arrested as a juvenile† 0.29* 0.38 ‡ 0.38* -1.00 No. of arrests as a juvenile 0.01 0.22* 0.22* -0.01 Arrested 1+ times as a juv. † 0.34* 0.65* 0.33 -1.00

Notes: * p < 0.05; ‡ p = 0.07; † tetrachoric correlation; MZ - monozygotic twins; DZ - dizygotic twins.

181

Table 6.7: Cross-twin (intraclass) correlations for sexual/reproductive and relationship measures by level of genetic relatedness.

DZ Twins & Sexual/Reproductive and Relationship Measures All Pairs MZ Twins Half-Siblings Full Siblings

Sexual activity index 0.21* 0.32* 0.19* 0.06 Age of debut, vaginal sex 0.42* 0.53* 0.39* 0.15* Age of debut, oral sex 0.27* 0.38* 0.27* 0.03 Age of debut, anal sex 0.12* 0.33* 0.13 -0.32 No. of vaginal sex partners 0.25* 0.46* 0.21* 0.12 No. of any sex activity partners 0.22* 0.48* 0.16* 0.29* No. of any sex activity partners, before 18 yrs. 0.16* 0.22* 0.10* 0.16* No. of one-time sexual partners 0.06* 0.13* 0.06 -0.02 Risky sexual behaviors index 0.04 0.18* -0.03 0.11 No. of times married 0.24* 0.29* 0.24* 0.15* Ever married† 0.31* 0.33* 0.32* 0.24* No. of cohabitation partners 0.30* 0.37* 0.37* -0.05 No. of long relationships (6+ months) 0.08* 0.22* 0.16* -0.03 No. of short relationships (<6 months) 0.08* 0.09 0.08* 0.07 No. of extra-pair sexual partners 0.01 0.05 0.03 -0.11 Currently have an extra-pair sexual partner† 0.08 0.27* 0.09 -0.22 No. of pregnancies/impregnations 0.31* 0.46* 0.31* 0.11

182

Table 6.7: Continued.

DZ Twins & Sexual/Reproductive and Relationship Measures All Pairs MZ Twins Half-Siblings Full Siblings

No. of pregnancies resulting in a live birth 0.27* 0.24* 0.27* 0.23* No. of pregnancies out of wedlock 0.23* 0.20* 0.29* -0.02 Ever parent a child who died† -1.00 -1.00 -1.00 -1.00 Parental dissatisfaction index 0.15* 0.22* 0.12* 0.15 Sexual behavior index 0.23* 0.29* 0.23* 0.11 Reproductive behavior index 0.26* 0.30* 0.26* 0.21*

Notes: * p < 0.05; † tetrachoric correlations; MZ - monozygotic twins; DZ - dizygotic twins; No. – Number.

183

Table 6.8: Summary of univariate ACE model analysis for all study variables. Parameter Estimate Item A C E

Adolescent Delinquency Items Delinquency - W1 0.50 0.00 0.50 Delinquency - W2 0.48 0.00 0.52 Delinquency - W1 & W2 0.56 0.00 0.44 Violent offending - W1 0.58 0.00 0.43 Violent offending - W2 0.16 0.22 0.62 Violent offending - W1 & W2 0.37 0.20 0.43 Nonviolent offending - W1 0.28 0.13 0.60 Nonviolent offending - W2 0.48 0.00 0.51 Nonviolent offending - W1 & W2 0.54 0.00 0.46

Criminal Justice Items Ever arrested as a juvenile 0.51 0.00 0.49 No. of arrests as a juvenile 0.95 0.00 0.05 Arrested 1+ times as a juvenile 0.64 0.00 0.36

Sexual Behavior Items (Wave 4) Sexual activity index 0.34 0.00 0.66 Age of debut, vaginal sex 0.25 0.28 0.47 Age of debut, oral sex 0.00 0.29 0.71 Age of debut, anal sex 0.29 0.00 0.71 No. of vaginal sex partners 0.53 0.00 0.47 No. of any sex activity partners 0.52 0.00 0.49 No. of any sex activity partners, before 18 yrs. 0.08 0.14 0.78 No. of one-time sexual partners 0.12 0.00 0.88 Risky sexual behaviors index 0.09 0.00 0.91 Sexual behavior index 0.26 0.10 0.65

Relationship & Reproductive Items (Wave 4) No. of times married 0.15 0.15 0.70 Ever married 0.00 0.33 0.67 No. of cohabitation partners 0.54 0.00 0.46 No. of long relationships (6+ months) 0.54 0.00 0.46 No. of short relationships (<6 months) 0.14 0.00 0.86 No. of extra-pair sexual partners 0.05 0.00 0.95 Currently have an extra-pair sexual partner 0.23 0.00 0.77 No. of pregnancies/impregnations 0.58 0.00 0.42 No. of pregnancies resulting in a live birth 0.00 0.29 0.71 No. of pregnancies out of wedlock 0.43 0.00 0.57 Ever parent a child who died 0.00 0.52 0.48 Parental dissatisfaction index 0.13 0.08 0.78 Reproductive behavior index 0.13 0.19 0.69

Notes: Parameter estimates are derived from the best fitting model; No. – number.

184

Table 6.9: DF analyses of the effect of adolescent antisocial conduct on sexual/reproductive behaviors in adulthood.

Full Sample Males Females Modela b N b N b N

Adolescent Delinquency Measures

0.15 498 0.38* 220 -0.07 280 Delinquency (W1) → No. of vaginal sex partners (0.13) (0.19) (0.20)

0.01 526 -0.01 240 0.03* 286 Delinquency (W2) → Sexual activity index (0.01) (0.01) (0.01)

-0.07* 460 -0.09* 210 -0.06 250 Delinquency (W2) → Age of debut, oral sex (0.03) (0.04) (0.05)

-0.04* 460 -0.04* 210 -0.03 250 Delinquency (W1 & W2) → Age of debut, oral sex (0.02) (0.02) (0.03)

0.01* 538 0.01 240 0.02* 298 Delinquency (W1 & W2) → No. of long relationships (0.01) (0.04) (0.01)

0.08* 546 0.06 244 0.10 302 Violent offending (W2) → No. of cohabitation partners (0.04) (0.05) (0.06)

-0.04 530 -0.07 236 0.13* 294 Violent offending (W1 & W2) → No. of short relationships (0.04) (0.04) (0.06)

Nonviolent -0.02 534 -0.01 244 -0.03* 290 offending (W1) → Sexual activity index (0.01) (0.01) (0.01)

0.03 646 0.02 244 0.04* 302 Nonviolent offending (W1) → No. of long relationships (0.02) (0.02) (0.02)

al sex -0.07 464 -0.15* 212 -0.03 252 Nonviolent offending (W2) → Age of debut, or (0.05) (0.06) (0.08)

185

Table 6.9: Continued.

Full Sample Males Females Model b N b N b N

Adolescent Delinquency Measures

hips 0.02* 544 0.01 242 0.03* 302 Nonviolent offending (W1& W2) → No. of long relations (0.01) (0.01) (0.01)

Criminal Justice System Items

0.41* 534 0.43* 244 0.02 290 Arrested as a juvenile → Sexual activity index (0.18) (0.19) (0.33)

ut, vaginal sex -1.68* 476 -1.94* 212 -1.02* 264 Arrested as a juvenile → Age of deb (0.57) (0.63) (0.36)

5.09* 508 5.77* 228 2.23 280 Arrested as a juvenile → No. of any sex activity partners (1.99) (2.57) (1.72)

efore 18 2.36 528 3.16* 240 -4.79* 288 Arrested as a juvenile → No. of any sex activity partners, b (1.58) (1.54) (1.81)

-time sexual partners 3.13 524 4.05* 242 -2.32 282 Arrested as a juvenile → No. of one (1.69) (1.93) (2.88)

0.34* 410 0.34* 192 0.25 218 Arrested as a juvenile → Risky sexual behaviors index (0.12) (0.12) (0.12)

0.34 556 0.44 248 -0.77* 308 Arrested as a juvenile → No. of times married (0.24) (0.25) (0.33)

0.28* 534 0.27 244 0.02 290 No. of arrests as a juvenile → Sexual activity index (0.13) (0.14) (0.33)

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Table 6.9: Continued.

Full Sample Males Females Model b N b N b N

Criminal Justice System Items

-1.31* 476 -1.37* 212 1.02* 264 No. of arrests as a juvenile → Age of debut, vaginal sex (0.35) (0.40) (0.36)

No 3.94* 498 3.53 220 -- -- . of arrests as a juvenile → No. of vaginal sex partners (1.29) (1.23) --

4.22* 508 4.22* 228 2.23 280 No. of arrests as a juvenile → No. of any sex activity partners (1.18) (1.49) (1.73)

No. of arrests as a juven 2.30 528 2.61* 240 -4.80* 288 ile → No. of any sex activity part., before 18 (1.26) (1.26) (1.81)

-time sexual partners 2.81* 524 3.22 242 -2.33 282 No. of arrests as a juvenile → No. of one (1.29) (1.37) (2.88)

No. of arrests as a juvenil 0.26* 410 0.27* 192 -- -- e → Risky sexual behaviors index (0.06) (0.06) --

0.26* 556 0.25* 248 0.11 308 No. of arrests as a juvenile → Sexual behavior index (0.11) (0.11) (0.19)

0.25* 556 0.27* 248 -0.77* 308 No. of arrests as a juvenile → No. of times married (0.12) (0.12) (0.33)

0.27* 556 0.26* 248 -0.60 308 No. of arrests as a juvenile → Reproductive behavior index (0.11) (0.11) (0.45)

-2.87* 476 -2.70* 212 -- -- Arrested 1+ times as a juvenile → Age of debut, vaginal sex (0.80) (0.91) --

9.64* 498 8.31* 220 -- -- Arrested 1+ times as a juvenile → No. of vaginal sex partners (2.24) (2.00) --

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Table 6.9: Continued.

Full Sample Males Females Model b N b N b N

Criminal Justice System Items

10.04* 508 8.79* 228 -- -- Arrested 1+ times as a juvenile → No. of sex activity partners (2.48) (3.05) --

6.66* 528 6.31 240 -- -- Arrested 1+ times as a juv. → No. of any sex activity part., before18 (3.34) (3.32) --

-time sexual partners 7.49* 524 7.58* 242 -- -- Arrested 1+ times as a juvenile → No. of one (3.36) (3.26) --

0.60* 410 0.63* 192 -- -- Arrested 1+ times as a juvenile → Risky sexual behaviors index (0.08) (0.08) --

Arrested 1+ times as 0.70* 556 0.65* 248 -- -- a juvenile → Sexual behavior index (0.27) (0.26) --

0.49* 556 0.43* 248 -- -- Arrested 1+ times as a juvenile → No. of times married (0.15) (0.16) --

† 2.27* 556 1.96* 248 -- -- Arrested 1+ times as a juvenile → Ever married (1.03) (1.03) --

0.63* 556 0.51* 248 -- -- Arrested 1+ times as a juvenile → Reproductive behavior index (0.18) (0.21) --

Notes: † logistic regression model; ª DF analyses control for the influence of shared genetic and shared environmental factors on the outcome measure; -- Indicates that the measure was dropped from the model due to collinearity (resulting from a lack of variation on the item); All models employ Huber-White standard errors (shown in parentheses); Models that did not attain statistical significance (p < 0.05) or were N < 100 are not displayed; All models controlled for within-pair differences in age, race, low self-control (W1 & W2), IQ (W1), and future outlook (W1).

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Table 6.10: Descriptive statistics for MZ difference score for all study variables.

Itema N Mean SD Min. Max.

Delinquency Wave 1 259 0.37 6.72 -47 33 Wave 2 232 0.61 5.05 -12 33 Waves 1 & 2 231 1.07 9.86 -47 54

Violent Offending Wave 1 261 0.07 2.44 -14 11 Wave 2 234 0.30 2.05 -6 11 Waves 1 & 2 233 0.46 3.63 -14 21

Nonviolent Offending Wave 1 259 0.13 3.90 -27 21 Wave 2 235 0.18 2.90 -10 16 Waves 1 & 2 234 0.29 5.72 -27 33

Criminal Justice System Items (Wave 4) Ever arrested as a juvenile 262 -0.03 0.22 -1 1 No. arrests as a juvenile 198 -0.08 0.81 -10 4 Arrested 1+ times as a juvenile 262 0.00 0.15 -1 1

Sexual Behavior Items (Wave 4) Sexual activity index 192 0.06 0.84 -3 3 Age of debut, vaginal sex 172 0.27 2.80 -10 8 Age of debut, oral sex 166 0.13 3.35 -12 9 Age of debut, anal sex 52 0.23 4.22 -9 11 No. of vaginal sex partners 180 0.52 15.44 -73 94 No. of any sex activity partners 182 -7.00 17.11 -73 94 No. of any sex activity part., before 18 189 0.78 6.49 -16 50 No. of one-time sexual partners 188 -0.13 11.79 -80 101 Risky sexual behaviors index 139 0.01 0.68 -2 2 Sexual behavior index 199 0.01 0.66 -2.68 3.74

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Table 6.10: Continued.

Item N Mean SD Min. Max.

Relationship & Reproductive Items (Wave 4) No. of times married 199 0.05 0.68 -2 1 Ever married 199 0.04 0.63 -1 1 No. of cohabitation partners 198 -0.15 1.12 -4 4 No. of long relationships (6+ months) 193 -0.03 1.47 -6 6 No. of short relationships (<6 months) 193 -0.39 6.51 -30 65 No. of extra-pair sexual partners 157 0.06 0.59 -1 5 Currently have an extra-pair sexual partner 157 0.03 0.42 -1 1 No. of pregnancies/impregnations 199 -0.03 1.37 -4 4 No. of pregnancies resulting in a live birth 199 -0.03 1.37 -4 4 No. of pregnancies out of wedlock 199 -0.07 0.64 -3 2 Ever parent a child who died 68 0.04 0.21 0 1 Parental dissatisfaction index 60 -0.15 3.00 -9 7 Reproductive behavior index 199 0.02 0.89 -2.54 1.91

Control Variables (Wave 1) Low self-control (Waves 1 & 2) 93 1.40 12.99 -49 35 IQ 244 0.66 9.96 -37 32 Future outlook 258 0.04 2.48 -7 8

Notes: MZ - monozygotic twins; ªMZ difference scores are obtained by subtracting twin 2's score from twin 1's score on each measure.

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Table 6.11: Tests of average differences between MZ twins and non-MZ twins on all study variables.

MZ pairs Non-MZ pairs Item t-value Mean SD Mean SD

Delinquency

Wave 1 4.76 6.52 4.96 6.22 0.64

Wave 2 3.00 4.38 3.38 4.95 1.51

Waves 1 & 2 7.70 9.80 8.30 9.80 1.17

Violent Offending

Wave 1 1.30 2.70 1.31 2.43 0.08

Wave 2 0.70 1.77 0.79 1.89 0.89

Waves 1 & 2 2.00 4.08 2.07 3.66 0.37

Nonviolent Offending

Wave 1 2.26 3.60 2.36 3.54 0.56

Wave 2 1.57 2.68 1.74 2.94 1.16

Waves 1 & 2 3.81 5.60 4.10 5.67 1.00

CJS Items (Wave 4)

Ever arrested as a juvenile 0.03 0.18 0.03 0.17 0.56

No. of arrests as a juvenile 0.12 0.86 0.18 2.56 0.49

Arrested 1+ times as a juvenile 0.02 0.13 0.02 0.13 0.60

Sexual Behavior Items (Wave 4)

Sexual activity index 2.29 0.73 2.27 0.72 -0.36

Age of debut, vaginal sex 17.43 2.90 16.92 2.94 -3.05*

Age of debut, oral sex 18.25 3.16 17.80 3.08 -2.54*

Age of debut, anal sex 21.50 3.67 21.52 3.68 0.10

No. of vaginal sex partners 9.34 15.45 10.44 17.94 1.13

No. of any sex activity partners 11.26 20.07 12.22 20.71 0.84

No. of any sex activity partners, before 18 2.19 5.07 2.55 5.17 1.27

No. of one-time sexual partners 2.98 8.55 3.66 9.06 1.38

Risky sexual behaviors index 0.32 0.54 0.36 0.55 1.22

Sexual behavior index -0.05 0.56 -0.01 0.60 1.49

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Table 6.11: Continued.

MZ pairs Non-MZ pairs Item t-value Mean SD Mean SD

Reproductive & Relationship Items (Wave 4)

No. of times married 0.55 0.57 0.53 0.58 -0.63

Ever married 0.52 0.50 0.50 0.50 -0.83

No. of cohabitation partners 0.73 1.12 0.83 1.19 1.61

No. of long relationships (6+ months) 0.72 1.19 0.79 1.84 0.77

No. of short relationships (<6 months) 1.51 4.81 1.86 5.53 1.18

No. of extra-pair sexual partners 0.15 0.46 0.18 0.53 1.06

Currently have an extra-pair sexual partner 0.12 0.33 0.14 0.35 0.63

No. of pregnancies/impregnations 1.21 1.32 1.40 1.59 2.33*

No. of pregnancies resulting in a live birth 1.53 1.20 1.63 1.18 1.25

No. of pregnancies out of wedlock 0.21 0.55 0.23 0.67 0.52

Ever parent a child who died 0.03 0.01 0.17 0.14 -0.77

Parental dissatisfaction index 6.78 2.33 6.98 2.51 1.01

Reproductive behavior index -0.07 0.74 -0.05 0.77 0.60

Control Variables (Wave 1)

Age 15.96 1.44 15.84 1.61 -1.52

Sex (1 = Male; 0 = Female) 0.49 0.50 0.51 0.50 0.73

Race (1 = White; 0 = Nonwhite) 0.63 0.48 0.65 0.48 1.02

Low self-control (Waves 1 & 2) 83.55 13.07 86.22 13.18 2.85*

IQ 99.68 14.02 99.90 14.30 0.31

Future outlook 7.48 2.14 7.52 2.17 0.40

Parent socioeconomic status 49.78 66.07 43.80 51.50 -1.92

Notes: * p < 0.05, two-tailed test; MZ - monozygotic twins (N = 524); Non-MZ pairs (N = 1,820) includes dizygotic twins (N = 464), full siblings (N = 1,038), and half-siblings (N = 318).

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Table 6.12: The effect of adolescent antisocial behaviors on sexual/reproductive behaviors in adulthood using monozygotic twin difference score models. Robust Modela b Beta p-value N SE .

Adolescent Delinquency Measures

Violen 0.05 0.02 0.16 0.006 191 t offending (W1) → Sexual activity index Nonviolent offending (W1) → Currently have an extra-pair sexual partner 0.02 0.01 0.14 0.035 155 Nonviolent offending (W2) → No. of any sex activity partners, before 18 -0.48 0.22 -0.21 0.030 174 No. of cohabitation partners 0.08 0.04 0.18 0.028 182 Nonviolent offending (W2) → Nonviolent offending (W1 & W No. of cohabitation partners 0.04 0.02 0.19 0.035 181 2) →

Criminal Justice System Items

Arrested as a juvenile → Age of debut, oral sex 2.15 0.93 0.14 0.021 166 No. 0.25 0.09 0.24 0.004 191 of times arrested as a juvenile → Sexual activity index No. of times arrested as a juvenile → No. of pregnancies out of wedlock 0.10 0.02 0.13 <0.001 198 No. of times arrested as a juvenile → Sexual behaviors index 0.11 0.02 0.14 <0.001 198 Arrested 1+ times as a juvenile → No. of any sex activity partners, before 18 9.44 2.56 0.21 <0.001 189 Arrested 1+ times as a juvenile → Risky sexual behaviors index 1.54 0.36 0.27 <0.001 139 Arrested 1+ times as a juvenile → Sexual behaviors index 0.81 0.13 0.18 <0.001 199

Notes: All models are OLS regression models employing Huber-White standard errors and listwise deletion; Models that did not attain statistical significance (p < 0.05) and/or were N < 100 are not displayed; ªMZ difference score models control for the effects of shared genetic and shared environmental factors on the association between the variables in the model; No. – number; W – Wave; Male-male pairs = 62 (128 individuals), female-female pairs = 67 (134 individuals), total MZ pairs = 129 (258 individuals).

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CHAPTER 7

DISCUSSION

This final chapter has three main objectives. First, a summary of the results will be provided. While the summary will be broad in scope, specific findings will be highlighted and discussed within the context of life history theory, evolutionary psychology, and biosocial criminology. Second, some limitations will be put forth by which the findings presented should be tempered. As with any systematic analyses there are factors which warrant special recognition. Finally, the chapter will conclude by offering suggestions for future research paths in terms of assessing the relationship between sexual behaviors and antisocial conduct.

Additionally, the discussion will also highlight how the current project provides a template for the manner in which criminology can be informed via evolutionary theory and how empirical examinations of the postulates of evolutionary criminology can be conducted.

7.1 Summary of Results

The previous chapter provided a large amount of information regarding the analyses of the current project. Therefore, this summary will be broad in scope but will highlight some specific findings relevant to the research questions under study and the theoretical scaffold upon which the study was built.

First, it will help to review the research questions guiding the analyses conducted in the previous chapter. Briefly, the first research question asked whether there was an association between antisocial conduct during adolescence and sexual/reproductive behaviors in adulthood.

The findings revealed that there was indeed an association across a wide number of bivariate and multivariate models. Conforming to past SSSM research assessing this association (e.g., Armour

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and Haynie, 2007) adolescent antisocial conduct was associated with increases in a variety of sexual behaviors across the life course, including age of sexual debut, number of sexual partners, and overall sexual conduct. The adolescent antisocial measures were less associated with the relationship and reproductive outcomes at the bivariate and multivariate levels, with few exceptions (e.g., all of the criminal justice system items were associated with an increase in parental dissatisfaction as an adult). Overall, the findings of the current study match those of the relevant literature (although as outlined above, a number of the models included in the current study represent original additions to the literature). However, as noted throughout this project analyses which do not account for the potential confound effects of genetic and nonshared environmental factors are uninterpretable. The analyses conducted to respond to the second research question underscore this assertion.

The second research question could be rephrased, in an informal fashion, “why should our analyses worry about genetic factors?”. The findings produced by the univariate ACE models illustrate that for the majority of measures included in the study genetic factors have considerable influence on their observed variance. Of the 39 variables exposed to the univariate

ACE modeling technique only four were found to have a negligible genetic effect. The remaining 35 variables had heritability estimates that ranged from .09 to .95. To contextualize these results within the broader criminological field, mainstream/traditional criminological analyses of such variables (recall that the current study included 12 items tapping antisocial behaviors which are common in the criminological literature) are completed in such as way as to treat the genetic influence on their variance as nil. The results of the decomposition analyses also bear relevance to evolutionary and life history theory. To the extent that the measures included in the current study tap the concepts and postulates of evolutionary approaches such as

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life history theory analyses of these ideas are also incomplete without recognition of the influence of genetic factors. Although evolutionary perspectives on behavior are inherently biological, the incorporation of a genetically sensitive design is not a forgone conclusion.

Indeed, as the literature review revealed there have been only two studies of life history theory wherein an explicit genetic analysis was conducted (Figueredo et al., 2004; Figueredo and

Rushton, 2009). Herein lies yet another benefit of the approach outlined in Chapter 4: evolutionary criminological analyses incorporate both the theoretical scaffold of evolutionary biology but also the methodological rigor of behavioral and molecular genetics. Returning to the underlying reason to ask Research Question 2, the results reveal that in order to assess the association between antisocial conduct and sexual/reproductive outcomes in a manner that will produce interpretable results a researcher must employ a genetically sensitive design. Hence, the overall findings of the univariate ACE modeling provide the impetus for the methodologies followed in assessing the final research question.

The final research question represented the main empirical purpose of the current project: an assessment of the association between antisocial behaviors and sexual/reproductive outcome using a genetically sensitive design. Overall, the results of the DF and MZ difference score models indicate that even after controlling for shared genetic and shared environmental influences antisocial conduct has an effect on some sexual and reproductive behaviors.

Additionally, this effect appears to be most pronounced with sexual behaviors. While the results of the DF and MZ difference score models, in general, appear to conform to SSSM analyses of the association a closer inspection reveals that the situation is more nuanced. For example, as mentioned the primary focus in both non-biosocial and biosocial studies of the association between sex and antisocial conduct has been on age of sexual debut and number of sexual

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partners. The DF model analyses revealed inconsistencies with the multivariate (i.e., SSSM) results across the adolescent delinquency measures. For example, in the DF model only Wave 1 delinquency was related to number of vaginal sex partners while only age of oral sex debut, and not vaginal or anal sex was related to the measures of adolescent delinquency. In the multivariate models, only nonviolent offending at Wave 2 was not associated with age of debut for the three sex act measures. Once again, the need to account for the influence of genetic factors is highlighted in the current project.

Perhaps the most interesting overall finding revealed in the DF model analyses was the considerable difference between males and females. Overall, it appears that adolescent antisocial offending, as measured by delinquency and involvement with the criminal justice system, has an effect on sexual behaviors in males (as measured during adulthood) that is not observed in females. Certainly this finding does not come as a significant revelation. There is considerable evidence indicating the divergent sexual practices of men and women (see Buss, 2003).

However, none of this literature has assessed the effect of differential engagement in antisocial behavior on sexual behaviors in a genetically sensitive design. Indeed, doing so revealed some interesting findings for both sexes. For instance, contact with the criminal justice system as a juvenile appears to have strong effects on sexual and reproductive behaviors. Male twins who were arrested displayed an increase of over four sexual partners during adolescence in relation to their co-twins who were not arrested. This association for females was reversed; the twin in a female-female kinship pair who was arrested reported a decrease in the number of sexual partners during adolescence of almost five partners, relative to her co-twin who was not arrested.

While this finding for males conforms to expectations and past empirical work, the finding for females is perplexing as it does not fit with past research or the expectations of theory. Given

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that biosocial research has illustrated the considerable differences in the human brain between males and females (Raine, 2013), it may be the case that the effect of being arrested (beyond the influence of shared genetic and shared environmental effects) is tapping divergent neurological

(or other biological) processes in males and females which relate to manifestations of sexual behavior. Researchers have noted that juvenile females who experience arrest often exhibit signs of low self-esteem and depression (Lederman et al., 2004) and therefore it may be the case that those females fail to form relationships, even those based on sex. This conjecture highlights an important point that bears repeating: the nonshared environment can represent biological effects

(low self-esteem and depression have been linked to neuronal processes; Raine, 2013) and are not limited to external social factors.

Overall, the DF model illustrates that as an isolated component of the nonshared environment antisocial conduct during adolescence can have an effect on sexual and reproductive outcomes. However, as noted the structure of the DF model leaves an unknown portion of the variance in the outcome that may be due to nonshared genetic factors (due to the necessary inclusion of DZ twins, full siblings, and half-siblingss). Consequently, in order to have genetic factors entirely accounted for, the MZ difference score method was employed.

Summarizing the results of these analyses, it was shown that an effect remained in 12 separate models assessing the relationship between antisocial conduct and sexual behaviors. Two main points are to be made here. First, the change in significance from the DF models to the MZ difference score models indicates that there is a nonshared genetic effect in the DF model that is encapsulated within the nonshared environmental factor (recall that e represents unmeasured variance that can influence the outcome, as well as random error). To get an overall interpretation of the effect of continued method refinement, Table 7.1 summarizes the frequency

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of significant associations by model type. As illustrated, 25 models were found to be significant in the DF analyses that were not found to be significant in the MZ difference score analysis.

While some of these model differences may simply due to a lack of statistical power resulting from sample size reduction, it may also be an indication of the influence of nonshared genetic effects on the associations between antisocial conduct and sexual behaviors. Second, the results of the MZ difference score models underscore the difference in findings that can arise when the influence of genetic factors are taken into account. For example, in only one instance was antisocial behavior (arrested as a juvenile) associated with age of sexual debut; however, this association was with oral sex and not the typically assessed vaginal sex. Therefore, past assessments of the age of sexual debut for vaginal sex are likely misspecified.

Overall, the findings of the current study indicate that antisocial behaviors during adolescence can impact sexual/reproductive outcomes beyond the variance accounted for by shared genetic and shared environmental factors. In terms of how these findings fit within the evolutionary and life history perspective, four main points can be made. First, it is clear that both antisocial behaviors and sexual/reproductive behaviors are influenced by genetic factors.

Consequently, evolutionarily derived concepts such as assortative mating are reinforced by these findings. Recall that assortative mating refers to sexual liaisons by individuals who are similar on one or more phenotypes. Given that adolescent offending relates to a number of sexual behaviors it is likely that the genetic material underlying the association is being replicated and distributed in a systematic fashion (Beaver, 2013). Additionally, given such a dynamic and the results of past research (e.g., Wilson and Daly, 1997) it may be the case that the effect of antisocial behavior, as an isolated component of the nonshared environment, is affecting sexual conduct in such a way as to increase fitness within certain social milieus.

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Table 7.1: Number of significant associations between adolescent antisocial measures and any sexual/reproductive outcome item by analytical model.

Models

Adolescent Antisocial Items Bivariate Multivariate DF MZ Diff.

Delinquency -W1 16 7 1 0

Delinquency -W2 16 7 1 0

Delinquency -W1 & W2 17 6 2 0

Violent offending - W1 17 9 0 1

Violent offending - W2 18 5 1 0

Violent offending - W1 & W2 17 8 1 0

Nonviolent offending -W1 15 5 2 1

Nonviolent offending -W2 15 6 1 2

Nonviolent offending -W1 & W2 14 6 1 1

Ever arrested as a juvenile 15 7 7 1

Number of arrests as a juvenile 6 6 10 3

Arrested 1+ times as a juv. 17 9 10 3

Total 183 81 37 12

Second, recall that life history theory is derived from biological ecology and as such it holds that external cues as to the likelihood of success given the adoption of varying survival and reproductive strategies will manifest in behaviors related to reproduction. Arguably, these external cues should be a component of the shared environment as both siblings within a kinship pair would be equally exposed to such information. However, the analyses in the current study illustrated that the shared environment had little effect in the majority of the univariate ACE models. It is possible that life history theory is not supported by these findings. Alternatively, it

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may be the case that the influence of external cues is found in the nonshared rather than the shared environment. Behavioral genetics researchers have produced considerable discussion on whether the shared environment is a measurable component of the human condition or simply a statistical artifact (Plomin et al., 2013; Turkheimer and Waldron, 2000). For example, interpretation of the external cues provided by the social milieu is completed by a unique brain

(even MZ twins differ in the structure and functioning of their brain; Beaver, 2009). While the human brain’s structure and functioning is highly heritable, any phenotypic variance is likely a reflection of nonshared environmental influences (Beaver, 2009). Likewise, interpretation of identical external cues will be filtered through unique brains that perceive the cues in a non- identical fashion generating variance in the perceptions of external cues and the behavioral strategies that result. Therefore, the finding of a lack of effect of the shared environment may not counter the expectations of life history theory. Additionally, the findings highlight the importance of recognizing individual differences within an evolutionary perspective as is done in an evolutionary criminology approach (Hawley and Buss, 2011). Third, recall that life history theory asserts that when presented with cues of a reduced likelihood of longevity an organism will tend to display a more r-selected reproductive strategy (Rushton, 2004). To the extent that being arrested, for example, is a cue to a reduction in longevity and to the extent that the composite sexual behaviors measure is tapping an r-selected strategy, this assertion was supported in the current analyses. For example, for males in the DF analyses being arrested had very strong effects on sexual behaviors, serving to increase the variance in sexual partners among kinship pairs by almost ten-fold. Fourth, related to the third point, life history theory would also assert that those respondents exhibiting a more r-selected reproductive strategy would also display a reduced K-selected strategy. To the extent that the relationship and reproductive

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measures (including the composite reproductive index) are tapping a K-selected strategy this assertion was generally not supported in the current analyses. Given that these last two points relate to potential limitations in the operationalization of concepts in the current study, we now turn to the next section.

7.2 Limitations of the Current Study

While numerous steps were taken to ensure the most rigorous examination possible, there are a number of limitations facing the current study that warrant discussion. First, while the Add

Health was constructed to be a nationally representative sample of the American high school population in the early 1990s the construction of the analytical sample may have altered that characteristic of the overall sample. Limiting the sample to twins may have had an impact on the ability for the results to be generalized to the wider study sample and the nation. Although this is a serious concern multiple researchers have addressed this issue as it pertains to a wide swath of demographic, personality, and behavioral characteristics (see Chapter 5; Barnes and Boutwell,

2013; Beaver, 2008; Jacobson and Rowe, 1998). Additionally, the current study partially addressed this concern with an analysis of average differences between the MZ twins and the non-MZ twins in the analytical sample that revealed little in the way of difference.

Notwithstanding these studies and steps taken in the current study, generalization of the findings in this project should be done with caution.

The second limitation of the current study concerns the measurement strategy employed to assess the relationship between antisocial conduct and sexual behaviors within the framework of life history theory. While the findings revealed some consistency with life history theory, and evolutionary theory in general, there may be concern in terms of the validity of the measures tapping the r/K continuum component of life history theory. There has been only one other 202

study guided by life history theory that has employed the Add Health dataset and it was concerned with a different variety of research questions than the current study (i.e., there was little overlap in the analytical focus; Brumbach et al., 2009). Consequently, there is a possibility that the measures chosen in the current study do not accurately, or completely, tap factors associated with the r/K continuum. Therefore, the findings in this project should be applied cautiously to the postulates of life history theory. As research in evolutionary criminology moves forward, more refined measures of r/K strategies will likely be provided. The current project represents a modest initial step.

A third limitation derives from the Add Health sample itself. While the dataset is an incredibly rich and valuable source of information its longitudinal identity is derived from a focus on adolescence and young adulthood. To best test how both genetic and environmental factors can impact the manifestation of varying sexual and reproductive strategies over the life course it would be desirable to examine data from pre-birth to well past reproductive age (i.e., mid-adulthood). Moffit (1992, 2005) and others have argued that this long-term analytical methodology will afford the greatest understanding of the etiology of behavioral phenotypes.

Few datasets that meet this criterion exist, however, due primarily to the cost of such an endeavor. Additionally, longitudinal criminological datasets rarely include information of a genetically sensitive nature. Future research would do well to meet both of these key standards.

In the meantime, it is recognized that the current study is relatively incomplete due its inability to analyze factors from the respondents’ childhood and earlier which may have an impact on the findings.

The fourth limitation of the current study concerns the operationalization of adolescent antisocial behaviors. A long line of research has been produced wherein similar measures have

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been employed in the criminological and non-criminological literature. Nonetheless, although the criminal justice system measures (arrested as a juvenile, number of arrests as a juvenile, and arrested more than once as a juvenile) were created to tap official instances of offending they were still based on self-report. Additionally, the questions were asked at Wave 4 while the respondents were adults – a timespan of almost 15 years for some respondents. Therefore, while the likelihood of forgetting or misremembering an event such as an arrest (or multiple arrests) is low, confidence in the findings of the current study would be increased if official records of such events were available. Unfortunately, such records are not available in the Add Health data.

The fifth limitation of the current study relates to a common limitation of behavioral genetic analyses: the inability to distinguish the specific genetic material that is responsible for the observed genetic effects. Recall from the discussion in Chapter 3 that behavioral genetics is concerned with decomposing the variance of a phenotype into three latent factors (heritability, shared environment, and nonshared environment). There is no information in the decomposition analyses (e.g., univariate ACE models) that provides clues as to the specific gene(s) that is(are) influencing the observed phenotypic effect. In order to glean such information a molecular genetic analysis is required. Numerous biosocial criminology analyses have incorporated molecular genetics methodologies and a long line of findings has indicated a number of genetic polymorphisms as candidate genes for antisocial behaviors as well as sexual and reproductive behaviors (e.g., Beaver et al., 2010). Future research assessing the link between antisocial behavior and sexual/reproductive outcomes would benefit from an inclusion of molecular genetic methods. This is particularly true given the findings revealed in the current study indicating the robust effect of genetic factors throughout the analyses (illustrated in the univariate ACE models and the DF and MZ difference score models).

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The sixth limitation of the current study relates to the MZ difference score analyses.

While the MZ difference score procedure is a considerably conservative and rigorous method to assess an association between observed measures, the current study suffered from a lack of statistical power due to a relatively small sample size. This is particularly true given the inability to conduct the MZ difference score analyses by sex. The DF analyses indicated that the association between antisocial conduct and sexual/reproductive behaviors is moderated by sex, with results diverging in a number of ways. Therefore, future research would benefit from employing an MZ difference score approach wherein the analyses can be demarcated by the sex of the kinship pair.

7.3 Future Directions for Research and Criminological Theory

Notwithstanding the limitations outlined above, the current study provided a number of unique contributions to the criminological literature. One of the key aspects to the constellation of results derived from the current project is the recognition that genetic factors have an influence on the expression of a number of behavioral traits. The current project adds to an ever- expanding body of literature (see Chapter 3) which points to such a conclusion. However, the current project also illustrated the crucial importance of the nonshared environment as an influential factor in phenotypic variance. Indeed, the assessment of the association between adolescent antisocial conduct and sexual/reproductive behavior revealed that even after shared genetic and shared environmental factors are held constant antisocial behavior (as a component of the nonshared environment) has an effect on a number of sexual and reproductive outcomes.

These findings have at least three implications for criminology overall. First, they once again underscore the need for criminologists to incorporate genetically sensitive datasets and analyses into their research. While non-biosocial criminologists may confess that this continued plea is an 205

unnecessary and tiresome appeal one need only peruse the flagship journal of the field,

Criminology, to see the relative paucity with which genetically sensitive studies are incorporated into mainstream criminological research.50

As outlined, the findings of the current study also highlight the need for criminology to recognize the manner in which non-genetic factors need to be appropriately bifurcated. A significant proportion of traditional/mainstream criminological analysis confounds shared and nonshared environmental effects. Therefore, without the bifurcation seen in a behavioral genetic design the validity of this research is called into question as it is virtually uninterpretable

(Beaver, 2008, 2009). The nonshared environmental effect represents perhaps the most fruitful area of future research and the most digestible component of a biosocial approach for traditional criminologists. Future research on the connection between antisocial behaviors and sexual/reproductive outcomes would benefit considerably from inclusion of components of the nonshared environment. Indeed, this study represents the first biosocial analysis of this association which incorporated an isolated component of the nonshared environment as a moderating variable. Continued use of this methodology will highlight other aspects of the nonshared environment that are influencing the observed association beyond the effect of shared genetic and shared environmental factors.

Past examinations of the association between antisocial conduct and sexual and reproductive behaviors in the criminological literature have neglected genetic and nonshared environmental influences. Consequently, the findings of the literature are questionable at best and uninformative at worst (Harden et al., 2008). The past attempts which employed a SSSM did not recognize the potential (and actual) influence of genetic and nonshared environmental

50 To illustrate, at the time this project was composed (summer, 2013) the last genetically sensitive study published in Criminology was in 2011 (Barnes et al., 2011). 206

factors due to the fact that they were based on theoretical scaffolding which does not hold behavioral genetic factors as influential. As outlined in the introductory chapters, adherence to such theories (and related ideologies) limits the quality of the research produced and hinders our understanding of behaviors. The time for a paradigm shift in criminology has long past and this project presents logic, supporting literature, and empirical analyses that justify an adoption of a new perspective with novel hypotheses, methodologies, and avenues of future research.

Furthermore, the current project highlights the importance for alteration of not only the viewpoint of traditional criminology but also biosocial criminology as well. The current project provides a broad attempt at emphasizing the need to incorporate an evolutionary perspective into a biosocial analysis. Overall, the project supports the assertion made by Dobzhansky: given that it is inexorably linked to biology, analyses of behavior do not make sense unless in the light of evolution.

207

APPENDIX A

UNIVARIATE ACE MODEL TABLES

Table A.1: Univariate ACE model results for the adolescent antisocial measures. Panel A Delinquency - Wave 1 Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 AIC ΔAIC

ACE 0.30 0.16 0.54 101.43 -- 29921.33 --

(0.20 - 0.39) (0.09 - 0.23) (0.49 - 0.59)

AE 0.50 0.00 0.50 116.56 15.13 29934.47 13.14

(0.46 - 0.54) (0.00 - 0.00) (0.46 - 0.54)

CE 0.00 0.33 0.67 123.62 22.19* 29941.52 20.19

(0.00 - 0.00) (0.30 - 0.36) (0.64 - 0.70)

E 0.00 0.00 1.00 384.35 282.92* 30200.26 278.93

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: * p < 0.05; 95% Confidence intervals displayed in parentheses; best fitting model in bold.

208

Table A.1 Continued

Panel B Delinquency - Wave 2 Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 AIC ΔAIC

ACE 0.30 0.14 0.57 163.85 -- 25609.60 --

(0.18 - 0.41) (0.06 - 0.21) (0.51 - 0.63)

AE 0.48 0.00 0.52 172.68 8.83 25616.43 6.83

(0.43 - 0.53) (0.00 - 0.00) (0.47 - 0.57)

CE 0.00 0.29 0.71 179.65 15.80 25623.40 13.80

(0.00 - 0.00) (0.26 - 0.33) (0.67 - 0.74)

E 0.00 0.00 1.00 347.44 183.59* 25789.19 179.59

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: * p < 0.05; 95% Confidence intervals displayed in parentheses; best fitting model in bold.

Panel C Delinquency - Waves 1 and 2 Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 AIC ΔAIC

ACE 0.34 0.18 0.48 88.37 -- 31245.99 --

(0.24 - 0.44) (0.11 - 0.25) (0.43 - 0.53)

AE 0.56 0.00 0.44 104.85 16.48 31260.47 14.48

(0.52 - 0.60) (0.00 - 0.00) (0.40 - 0.48)

CE 0.00 0.38 0.63 115.88 27.51* 31271.51 25.52

(0.00 - 0.00) (0.34 - 0.41) (0.59 - 0.66)

E 0.00 0.00 1.00 409.41 321.04* 31563.03 317.04

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: * p < 0.05; 95% Confidence intervals displayed in parentheses; best fitting model in bold.

209

Table A.1 Continued

Panel D Violent Offending - Wave 1 Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 AIC ΔAIC

ACE 0.48 0.08 0.44 178.63 -- 21305.85 --

(0.39 - 0.57) (0.02 - 0.15) (0.40 - 0.48)

AE 0.58 0.00 0.43 182.67 4.04 21307.89 2.04

(0.54 - 0.61) (0.00 - 0.00) (0.39 - 0.46)

CE 0.00 0.36 0.65 249.30 70.67* 21374.52 68.67

(0.00 - 0.00) (0.33 - 0.39) (0.61 - 0.68)

E 0.00 0.00 1.00 552.76 374.13* 21675.99 370.14

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: * p < 0.05; 95% Confidence intervals displayed in parentheses; best fitting model in bold.

Panel E Violent Offending - Wave 2 Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 AIC ΔAIC

ACE 0.16 0.22 0.62 304.82 -- 17466.95 --

(0.05 - 0.28) (0.15 - 0.29) (0.56 - 0.68)

AE 0.45 0.00 0.55 328.85 24.03* 17488.98 22.03

(0.40 - 0.50) (0.00 - 0.00) (0.50 - 0.60)

CE 0.00 0.20 0.70 310.01 5.19 17470.13 3.18

(0.00 - 0.00) (0.27 - 0.34) (0.67 - 0.74)

E 0.00 0.00 1.00 482.40 177.58* 17640.53 173.58

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: * p < 0.05; 95% Confidence intervals displayed in parentheses; best fitting model in bold.

210

Table A.1 Continued

Panel F Violent Offending - Waves 1 and 2 Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 AIC ΔAIC

ACE 0.37 0.20 0.43 191.08 -- 23112.71 --

(0.28 - 0.46) (0.14 - 0.27) (0.39 - 0.47)

AE 0.60 0.00 0.40 213.96 22.88* 23133.59 20.88

(0.57 - 0.64) (0.00 - 0.00) (0.36 - 0.43)

CE 0.00 0.41 0.59 233.14 42.06* 23152.77 40.06

(0.00 - 0.00) (0.38 - 0.44) (0.56 - 0.62)

E 0.00 0.00 1.00 594.28 403.20* 23511.91 399.20

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: * p < 0.05; 95% Confidence intervals displayed in parentheses; best fitting model in bold.

Panel G Nonviolent Offending - Wave 1 Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 AIC ΔAIC

ACE 0.28 0.13 0.60 78.02 -- 24763.49 --

(0.17 - 0.39) (0.06 - 0.20) (0.54 - 0.65)

AE Model could not converge CE 0.00 0.28 0.72 94.48 16.46 24777.95 14.46

(0.00 - 0.00) (0.25 - 0.31) (0.69 - 0.75)

E 0.00 0.00 1.00 282.12 204.10* 24963.59 200.10

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: * p < 0.05; 95% Confidence intervals displayed in parentheses; best fitting model in bold.

211

Table A.1 Continued

Panel H Nonviolent Offending - Wave 2 Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 AIC ΔAIC

ACE 0.44 0.04 0.53 79.72 -- 21288.05 --

(0.32 - 0.55) (-0.04 - 0.11) (0.47 - 0.58)

AE 0.48 0.00 0.51 80.39 0.67 21286.72 -1.33

(0.44 - 0.54) (.00 - .00) (0.47 - 0.56)

CE 0.00 0.27 0.73 111.67 31.95* 21318.01 29.96

(0.00 - 0.00) (0.24 - 0.31) (0.70 - 0.76)

E 0.00 0.00 1.00 260.83 181.11* 21465.15 177.10

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: * p < 0.05; 95% Confidence intervals displayed in parentheses; best fitting model in bold.

Panel I Nonviolent Offending - Waves 1 and 2 Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 AIC ΔAIC

ACE 0.33 0.17 0.51 48.94 -- 26824.24 --

(0.22 - 0.44) (0.09 - 0.24) (0.46 - 0.56)

AE 0.54 0.00 0.46 62.07 13.13 26835.67 11.43

(0.50 - 0.58) (.00 - .00) (0.42 - 0.50)

CE 0.00 0.35 0.65 70.94 22.00* 26844.25 20.01

(0.00 - 0.00) (0.32 - 0.39) (0.62 - 0.68)

E 0.00 0.00 1.00 330.63 281.69* 27101.93 277.69

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: * p < 0.05; 95% Confidence intervals displayed in parentheses; best fitting model in bold.

212

Table A.1 Continued

Panel J Ever Arrested as a Juvenile Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 RMSEA ΔRMSEA

ACE 0.20 0.23 0.57 11.08 -- 0.03 --

(-0.46 - 0.86) (-0.20 - 0.66) (0.27 - 0.87)

AE 0.51 0.00 0.49 12.59 1.51 0.03 0.00

(0.28 - 0.74) (0.00 - 0.00) (0.26 - 0.49)

CE 0.00 0.34 0.66 11.63 0.55 0.03 0.00

(0.00 - 0.00) (0.19 - 0.48) (0.52 - 0.81)

E 0.00 0.00 1.00 26.10 15.02 0.05 0.02

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: 95% Confidence intervals displayed in parentheses; best fitting model in bold; threshold model employed; RMSEA - root mean square error of approximation.

Panel K Number of Arrests as a Juvenile Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 AIC ΔAIC

ACE 0.95 0.00 0.05 6083.21 -- 16920.62 --

(0.94 - 0.95) (0.00 - 0.00) (0.05 - 0.06)

AE 0.95 0.00 0.05 6083.21 0.00 16918.62 -2.00

(0.94 - 0.95) (0.00 - 0.00) (0.05 - 0.06)

CE 0.00 0.01 0.99 6654.81 571.60* 17490.22 569.60

(0.00 - 0.00) (-0.02 - 0.05) (0.95 - 1.02)

E 0.00 0.00 1.00 6655.12 571.91* 17488.53 567.91

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: * p < 0.05; 95% Confidence intervals displayed in parentheses; best fitting model in bold.

213

Table A.1 Continued

Panel L Arrested More than Once as a Juvenile Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 RMSEA ΔRMSEA

ACE 0.57 0.06 0.37 9.88 -- 0.03 --

(-0.08 - 1.22) (-0.47 - 0.58) (0.12 - 0.62)

AE 0.64 0.00 0.36 9.92 0.04 0.02 -0.01

(0.40 - 0.87) (0.00 - 0.00) (0.13 - 0.60)

CE 0.00 0.41 0.59 12.56 2.68 0.03 0.00

(0.00 - 0.00) (0.25 - 0.57) (0.43 - 0.75)

E 0.00 0.00 1.00 28.36 18.48 0.06 0.03

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: 95% Confidence intervals displayed in parentheses; best fitting model in bold; threshold model employed; RMSEA - root mean square error of approximation.

214

Table A.2: Univariate ACE model results for the sexual behavior measures. Panel A Sexual Activity Index Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 AIC ΔAIC

ACE 0.27 0.05 0.68 16.89 -- 8231.59 --

(0.12 - 0.42) (-0.05 - 0.15) (0.61 - 0.75)

AE 0.34 0.00 0.66 17.52 0.63 8230.23 -1.36

(0.28 - 0.39) (0.00 - 0.00) (0.61 - 0.72)

CE 0.00 0.21 0.79 24.92 8.03 8237.62 6.03

(0.00 - 0.00) (0.17 - 0.25) (0.75 - 0.83)

E 0.00 0.00 1.00 99.07 82.18* 8309.78 78.19

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: * p < 0.05; 95% Confidence intervals displayed in parentheses; best fitting model in bold.

Panel B Age of Debut, Vaginal Sex Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 AIC ΔAIC

ACE 0.25 0.28 0.47 115.68 -- 17600.01 --

(0.12 - 0.38) (0.19 - 0.37) (0.41 - 0.53)

AE 0.60 0.00 0.40 141.33 25.65* 17623.66 23.65

(0.56 - 0.64) (0.00 - 0.00) (0.36 - 0.44)

CE 0.00 0.43 0.57 124.92 9.24 17607.25 7.24

(0.00 - 0.00) (0.39 - 0.46) (0.54 - 0.61)

E 0.00 0.00 1.00 419.78 304.10* 17900.11 300.10

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: * p < 0.05; 95% Confidence intervals displayed in parentheses; best fitting model in bold.

215

Table A.2: Continued.

Panel C Age of Debut, Oral Sex Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 AIC ΔAIC

ACE 0.30 0.11 0.59 32.81 -- 17609.62 --

(0.15 - 0.46) (0.00 - 0.21) (0.52 - 0.66)

AE Model could not converge CE 0.00 0.29 0.71 42.51 9.70 17617.31 7.69

(0.00 - 0.00) (0.25 - 0.33) (0.67 - 0.76)

E 0.00 0.00 1.00 153.62 120.81* 17726.42 116.80

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: * p < 0.05; 95% Confidence intervals displayed in parentheses; best fitting model in bold.

Panel D Age of Debut, Anal Sex Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 AIC ΔAIC

ACE 0.29 0.00 0.71 7.81 -- 8697.09 --

(0.17 - 0.42) (0.00 - 0.00) (0.58 - 0.83)

AE Model could not converge CE 0.00 0.13 0.87 14.99 7.18 8702.27 5.18

(0.00 - 0.00) (0.04 - 0.21) (0.79 - 0.96)

E 0.00 0.00 1.00 20.62 12.81 8705.91 8.82

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: * p < 0.05; 95% Confidence intervals displayed in parentheses; best fitting model in bold.

216

Table A.2: Continued.

Panel E Lifetime Number of Vaginal Sex Partners Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 AIC ΔAIC

ACE 0.53 0.00 0.47 60.88 -- 31745.75 --

(0.48 - 0.59) (0.00 - 0.00) (0.41 - 0.52)

AE 0.53 0.00 0.47 60.88 0.00 31743.75 -2.00

(0.48 - 0.59) (0.00 - 0.00) (0.41 - 0.52)

CE 0.00 0.25 0.75 105.73 44.85* 31788.60 42.85

(0.00 - 0.00) (0.22 - 0.29) (0.71 - 0.79)

E 0.00 0.00 1.00 205.93 145.05* 31886.79 141.04

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: * p < 0.05; 95% Confidence intervals displayed in parentheses; best fitting model in bold.

Panel F Lifetime Number of Any Sexual Activity Partners Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 AIC ΔAIC

ACE 0.52 0.00 0.49 47.34 -- 33097.69 --

(0.45 - 0.58) (0.00 - 0.00) (0.42 - 0.55)

AE 0.52 0.00 0.49 47.34 0.00 33095.69 -2.00

(0.45 - 0.58) (0.00 - 0.00) (0.42 - 0.55)

CE 0.00 0.23 0.77 86.07 38.73* 33134.42 36.73

(0.00 - 0.00) (0.19 - 0.27) (0.73 - 0.81)

E 0.00 0.00 1.00 167.89 120.55* 33214.24 116.55

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: * p < 0.05; 95% Confidence intervals displayed in parentheses; best fitting model in bold.

217

Table A.2: Continued.

Panel G Number of Sexual Partners as a Juvenile Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 AIC ΔAIC

ACE 0.08 0.14 0.78 90.67 -- 23140.79 --

(-0.07 - 0.23) (0.04 - 0.24) (0.71 - 0.86)

AE 0.26 0.00 0.74 96.00 5.33 23144.12 3.33

(0.20 - 0.33) (0.00 - 0.00) (0.68 - 0.82)

CE 0.00 0.23 0.81 91.44 0.77 23139.56 -1.23

(0.00 - 0.00) (0.19 - 0.23) (0.77 - 0.86)

E 0.00 0.00 1.00 133.79 43.12* 23179.91 39.12

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: * p < 0.05; 95% Confidence intervals displayed in parentheses; best fitting model in bold.

Panel H Lifetime Number of One-Time Sexual Partners Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 AIC ΔAIC

ACE 0.12 0.00 0.88 15.24 -- 27188.44 --

(0.06 - 0.18) (0.00 - 0.00) (0.82 - 0.94)

AE 0.12 0.00 0.88 15.24 0.00 27186.44 -2.00

(0.06 - 0.18) (0.00 - 0.00) (0.82 - 0.94)

CE 0.00 0.06 0.94 18.10 2.86 27189.30 0.86

(0.00 - 0.00) (0.02 - 0.10) (0.90 - 0.98)

E 0.00 0.00 1.00 24.83 9.59 27194.02 5.58

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: 95% Confidence intervals displayed in parentheses; best fitting model in bold.

218

Table A.2: Continued.

Panel I Risky Sexual Behaviors Index Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 AIC ΔAIC

ACE 0.09 0.00 0.91 34.73 -- 5346.30 --

(0.01 - 0.17) (0.00 - 0.00) (0.83 - 0.99)

AE 0.09 0.00 0.91 34.73 0.00 5344.30 -2.00

(0.01 - 0.17) (0.00 - 0.00) (0.83 - 0.99)

CE 0.00 0.04 0.96 36.67 1.94 5346.24 -0.06

(0.00 - 0.00) (-0.01 - 0.09) (0.91 - 1.00)

E 0.00 0.00 1.00 38.46 3.73 5346.03 -0.27

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: 95% Confidence intervals displayed in parentheses; best fitting model in bold.

Panel J Composite Sexual Behaviors Index Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 AIC ΔAIC

ACE 0.26 0.10 0.65 70.25 -- 6822.53 --

(0.12 - 0.39) (0.01 - 0.18) (0.58 - 0.72)

AE 0.39 0.00 0.61 73.67 3.42 6823.96 1.43

(0.34 - 0.45) (0.00 - 0.00) (0.55 - 0.67)

CE 0.00 0.23 0.77 78.71 8.46 6828.99 6.46

(0.00 - 0.00) (0.20 - 0.27) (0.73 - 0.81)

E 0.00 0.00 1.00 172.59 102.34* 6920.88 98.35

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: * p < 0.05; 95% Confidence intervals displayed in parentheses; best fitting model in bold.

219

Table A.3: Univariate ACE model results for the reproductive/relationship behavior measures. Panel A Number of Times Married Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 AIC ΔAIC

ACE 0.15 0.15 0.70 8.62 -- 6636.38 --

(0.01 - 0.30) (0.06 - 0.25) (0.63 - 0.77)

AE 0.36 0.00 0.64 15.64 7.02 6641.40 5.02

(0.31 - 0.42) (0.00 - 0.00) (0.59 - 0.69)

CE 0.00 0.24 0.76 11.44 2.82 6637.20 0.82

(0.00 - 0.00) (0.20 - 0.28) (0.72 - 0.80)

E 0.00 0.00 1.00 116.25 107.63* 6740.01 103.63

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: * p < 0.05; 95% Confidence intervals displayed in parentheses; best fitting model in bold.

Panel B Ever Married Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 RMSEA ΔRMSEA

ACE 0.00 0.33 0.67 8.42 -- 0.02 --

(0.00 - 0.01) (0.27 - 0.39) (0.61 - 0.73)

AE 0.47 0.00 0.53 18.65 10.23 0.05 0.03

(0.38 - 0.56) (0.00 - 0.00) (0.44 - 0.62)

CE 0.00 0.33 0.67 9.81 1.39 0.02 0.00

(0.00 - 0.00) (0.27 - 0.39) (0.61 - 0.73)

E 0.00 0.00 1.00 88.07 79.65 0.12 0.10

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: 95% Confidence intervals displayed in parentheses; best fitting model in bold; threshold model employed; RMSEA - root mean square error of approximation.

220

Table A.3: Continued.

Panel C Number of Cohabitation Partners Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 AIC ΔAIC

ACE 0.48 0.05 0.47 177.42 -- 11902.32 --

(0.37 - 0.58) (-0.02 - 0.12) (0.42 - 0.53)

AE 0.54 0.00 0.46 178.74 1.32 11901.64 -0.68

(0.49 - 0.59) (0.00 - 0.00) (0.41 - 0.51)

CE 0.00 0.29 0.71 218.73 41.31* 11941.62 39.30

(0.00 - 0.00) (0.26 - 0.33) (0.67 - 0.75)

E 0.00 0.00 1.00 377.22 199.80* 12098.12 195.80

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: * p < 0.05; 95% Confidence intervals displayed in parentheses; best fitting model in bold.

Panel D Lifetime Number of Long Relationships Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 AIC ΔAIC

ACE 0.54 0.00 0.46 982.61 -- 15035.09 --

(0.47 - 0.61) (0.00 - 0.00) (0.39 - 0.53)

AE 0.54 0.00 0.46 982.61 0.00 15033.09 -2.00

(0.47 - 0.61) (0.00 - 0.00) (0.39 - 0.53)

CE 0.00 0.08 0.92 1033.73 51.12* 15084.21 49.12

(0.00 - 0.00) (0.04 - 0.12) (0.88 - 0.96)

E 0.00 0.00 1.00 1044.04 61.43* 15092.52 57.43

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: * p < 0.05; 95% Confidence intervals displayed in parentheses; best fitting model in bold.

221

Table A.3: Continued.

Panel E Lifetime Number of Short Relationships Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 AIC ΔAIC

ACE 0.07 0.04 0.89 32.92 -- 23745.05 --

(-0.11 - 0.27) (-0.07 - 0.15) (0.79 - 0.98)

AE 0.14 0.00 0.86 33.23 0.31 23743.37 -1.68

(0.07 - 0.20) (0.00 - 0.00) (0.80 - 0.93)

CE 0.00 0.08 0.92 33.35 0.43 23743.49 -1.56

(0.00 - 0.00) (0.04 - 0.12) (0.88 - 0.96)

E 0.00 0.00 1.00 45.11 12.19 23753.25 8.20

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: 95% Confidence intervals displayed in parentheses; best fitting model in bold.

Panel F Current Number of Extra-Pair Sexual Partners Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 AIC ΔAIC

ACE 0.05 0.00 0.95 39.90 -- 5028.74 --

(-0.03 - 0.14) (0.00 - 0.00) (0.86 - 1.03)

AE 0.05 0.00 0.95 39.90 0.00 5026.74 -2.00

(-0.03 - 0.14) (0.00 - 0.00) (0.86 - 1.03)

CE 0.00 0.01 0.99 40.80 0.90 5027.64 -1.10

(0.00 - 0.00) (-0.04 - 0.06) (0.95 - 1.04)

E 0.00 0.00 1.00 40.91 1.01 5025.76 -2.98

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: 95% Confidence intervals displayed in parentheses; best fitting model in bold.

222

Table A.3: Continued.

Panel G Currently have an Extra-Pair Sexual Partner Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 RMSEA ΔRMSEA

ACE 0.23 0.00 0.77 10.99 -- 0.04 --

(-0.28 - 0.75) (-0.36 - 0.36) (0.55 - 0.99)

AE 0.23 0.00 0.77 10.98 -0.01 0.03 -0.01

(0.06 - 0.41) (0.00 - 0.00) (0.59 - 0.94)

CE 0.00 0.14 0.87 12.26 1.27 0.03 -0.01

(0.00 - 0.00) (0.02 - 0.26) (0.74 - 0.99)

E 0.00 0.00 1.00 15.99 5.00 0.04 0.00

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: 95% Confidence intervals displayed in parentheses; best fitting model in bold; threshold model employed; RMSEA - root mean square error of approximation.

Panel H Lifetime Number of Pregnancies/Impregnations Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 AIC ΔAIC

ACE 0.56 0.02 0.43 79.55 -- 14114.94 --

(0.44 - 0.67) (-0.06 - 0.10) (0.37 - 0.48)

AE 0.58 0.00 0.42 79.65 0.10 14113.04 -1.90

(0.53 - 0.63) (0.00 - 0.00) (0.37 - 0.47)

CE 0.00 0.31 0.69 123.84 44.29* 14157.24 42.30

(0.00 - 0.00) (0.28 - 0.35) (0.65 - 0.73)

E 0.00 0.00 1.00 297.76 218.21* 14329.15 214.21

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: * p < 0.05; 95% Confidence intervals displayed in parentheses; best fitting model in bold.

223

Table A.3: Continued.

Panel I Number of Pregnancies Resulting in Live Births Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 AIC ΔAIC

ACE 0.09 0.24 0.67 20.35 -- 7365.63 --

(-0.15 - 0.32) (0.11 - 0.38) (0.55 - 0.79)

AE Model could not converge CE 0.00 0.29 0.71 20.70 0.35 7363.98 -1.65

(0.00 - 0.00) (0.23 - 0.35) (0.65 - 0.77)

E 0.00 0.00 1.00 77.39 57.04* 7418.67 53.04

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: * p < 0.05; 95% Confidence intervals displayed in parentheses; best fitting model in bold.

Panel J Number of Pregnancies out of Wedlock Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 AIC ΔAIC

ACE 0.38 0.03 0.59 74.28 -- 7383.44 --

(0.23 - 0.53) (-0.06 - 0.12) (0.51 - 0.67)

AE 0.43 0.00 0.57 74.61 0.33 7381.77 -1.67

(0.37 - 0.48) (0.00 - 0.00) (0.52 - 0.63)

CE 0.00 0.22 0.78 88.65 14.37 7395.81 12.37

(0.00 - 0.00) (0.19 - 0.26) (0.74 - 0.81)

E 0.00 0.00 1.00 180.99 106.71* 7486.15 102.71

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: * p < 0.05; 95% Confidence intervals displayed in parentheses; best fitting model in bold.

224

Table A.3: Continued.

Panel K Ever Have a Child Who Died Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 RMSEA ΔRMSEA

ACE 0.00 0.52 0.48 2.31 -- 0.00 --

(-0.88 - 0.88) (-0.06 - 1.10) (-0.13 - 1.08)

AE 0.59 0.00 0.41 2.64 0.33 0.00 0.00

(0.01 - 1.17) (0.00 - 0.00) (-0.17 - 0.99)

CE 0.00 0.52 0.48 2.31 0.00 0.00 0.00

(0.00 - 0.00) (0.12 - 0.92) (0.08 - 0.88)

E 0.00 0.00 1.00 4.92 2.61 0.00 0.00

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: 95% Confidence intervals displayed in parentheses; best fitting model in bold; threshold model employed; RMSEA - root mean square error of approximation.

Panel L Parental Dissatisfaction Index Parameter Estimates Model Fit Statistics A C E χ2 Δχ2 AIC ΔAIC

ACE 0.13 0.08 0.78 16.97 -- 9023.68 --

(-0.16 - 0.42) (-0.10 - 0.26) (0.64 - 0.93)

AE 0.26 0.00 0.75 17.55 0.58 9022.25 -1.43

(0.14 - 0.37) (0.00 - 0.00) (0.63 - 0.86)

CE 0.00 0.16 0.84 17.53 0.56 9022.24 -1.44

(0.00 - 0.00) (0.09 - 0.23) (0.77 - 0.91)

E 0.00 0.00 1.00 30.23 13.26 9032.94 9.26

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: 95% Confidence intervals displayed in parentheses; best fitting model in bold.

225

Table A.3: Continued.

Panel M Composite Reproductive Index Parameter Estimates Model Fit Statistics

A C E χ2 Δχ2 AIC ΔAIC

ACE 0.13 0.19 0.69 4.78 -- 8824.82 --

(-0.02 - 0.27) (0.10 - 0.28) (0.62 - 0.76)

AE 0.39 0.00 0.62 15.92 11.14 8833.96 9.14

(0.33 - 0.44) (0.00 - 0.00) (0.56 - 0.67)

CE 0.00 0.26 0.74 6.78 2.00 8824.83 0.01

(0.00 - 0.00) (0.22 - 0.30) (0.71 - 0.78)

E 0.00 0.00 1.00 127.71 122.93* 8943.75 118.93

(0.00 - 0.00) (0.00 - 0.00) (1.00 - 1.00)

Notes: * p < 0.05; 95% Confidence intervals displayed in parentheses; best fitting model in bold.

226

APPENDIX B

ITEMS FOR DELINQUENCY AND CRIMINAL INDEXES

Wave 1 Delinquency Index

1. In the past 12 months, how often did you paint graffiti or signs on someone else’s

property or in a public place?

2. In the past 12 months, how often did you deliberately damage property that didn’t belong

to you?

3. In the past 12 months, how often did you lie to your parents or guardians about where

you had been or whom you were with?

4. In the past 12 months, how often did you take something from a store without paying for

it?

5. In the past 12 months, how often did you get into a serious physical fight?

6. In the past 12 months, how often did you hurt someone badly enough to need bandages or

care from a doctor or nurse?

7. In the past 12 months, how often did you run away from home?

8. In the past 12 months, how often did you drive a car without its owner’s permission?

9. In the past 12 months, how often did you steal something worth more than $50?

10. In the past 12 months, how often did you go into a house or building to steal something?

11. In the past 12 months, how often did you use or threaten to use a weapon to get

something from someone?

12. In the past 12 months, how often did you sell marijuana or other drugs?

13. In the past 12 months, how often did you steal something worth less than $50?

227

14. In the past 12 months, how often did you take part in a fight where a group of your

friends was against another group?

15. In the past 12 months, how often did you act loud, rowdy, or unruly in a public place?

16. Have you ever carried a weapon at school?

17. Have you ever used a weapon in a fight?

Wave 2 Delinquency Index

1. In the past 12 months, how often did you paint graffiti or signs on someone else’s

property or in a public place?

2. In the past 12 months, how often did you deliberately damage property that didn’t belong

to you?

3. In the past 12 months, how often did you lie to your parents or guardians about where

you had been or whom you were with?

4. In the past 12 months, how often did you take something from a store without paying for

it?

5. In the past 12 months, how often did you run away from home?

6. In the past 12 months, how often did you drive a car without its owner’s permission?

7. In the past 12 months, how often did you steal something worth more than $50?

8. In the past 12 months, how often did you go into a house or building to steal something?

9. In the past 12 months, how often did you use or threaten to use a weapon to get

something from someone?

10. In the past 12 months, how often did you sell marijuana or other drugs?

11. In the past 12 months, how often did you steal something worth less than $50?

228

12. In the past 12 months, how often did you act loud, rowdy, or unruly in a public place?

13. In the past 12 months, how often did you take part in a fight where a group of your

friends was against another group?

14. In the past 12 months, how often did you get into a serious physical fight?

15. In the past 12 months, how often did you In the past 12 months, how often did you hurt

someone badly enough to need bandages or care from a doctor or nurse?

16. Since {month of last interview} have you used a weapon in a fight?

17. Since {month of last interview} have you carried a weapon at school?

Adolescent Violent Offending Index

Wave 1 Items

1. In the past 12 months, how often did you get into a serious physical fight?

2. In the past 12 months, how often did you hurt someone badly enough to need bandages or

care In the past 12 months, how often did you from a doctor or nurse?

3. In the past 12 months, how often did you use or threaten to use a weapon to get

something from someone?

4. In the past 12 months, how often did you take part in a fight where a group of your

friends was against another group?

5. In the past 12 months, how often did you pull a knife or gun on someone?

6. In the past 12 months, how often did you shoot or stab someone?

Wave 2 Items

1. In the past 12 months, how often did you get into a serious physical fight?

229

2. In the past 12 months, how often did you hurt someone badly enough to need bandages or

care from a doctor or nurse?

3. In the past 12 months, how often did you use or threaten to use a weapon to get

something from someone?

4. In the past 12 months, how often did you take part in a fight where a group of your

friends was against another group?

5. In the past 12 months, how often did you pull a knife or gun on someone?

6. In the past 12 months, how often did you shoot or stab someone?

Adolescent Nonviolent Offending Index

Wave 1 Items

1. In the past 12 months, how often did you paint graffiti or signs on someone else’s

property or in a public place?

2. In the past 12 months, how often did you deliberately damage property that didn’t belong

to you?

3. In the past 12 months, how often did you take something from a store without paying for

it?

4. In the past 12 months, how often did you drive a car without its owner’s permission?

5. In the past 12 months, how often did you steal something worth more than $50?

6. In the past 12 months, how often did you go into a house or building to steal something?

7. In the past 12 months, how often did you sell marijuana or other drugs?

8. In the past 12 months, how often did you steal something worth less than $50?

9. In the past 12 months, how often did you act loud, rowdy, or unruly in a public place?

230

Wave 2 Items

1. In the past 12 months, how often did you paint graffiti or signs on someone else’s

property or in a public place?

2. In the past 12 months, how often did you deliberately damage property that didn’t belong

to you?

3. In the past 12 months, how often did you take something from a store without paying for

it?

4. In the past 12 months, how often did you drive a car without its owner’s permission?

5. In the past 12 months, how often did you steal something worth more than $50?

6. In the past 12 months, how often did you go into a house or building to steal something?

7. In the past 12 months, how often did you sell marijuana or other drugs?

8. In the past 12 months, how often did you steal something worth less than $50?

9. In the past 12 months, how often did you act loud, rowdy, or unruly in a public place?

Low Self-Control Index

Wave 1 Items

1. All things considered, how is your child’s life going?

2. You get along well with your child.

3. You can trust your child.

4. Does your child have a bad temper?

5. You never argue with anyone.

6. When you get what you want, it is usually because you worked hard for it.

7. You never get sad or you felt sad.

231

8. You never criticize other people.

9. You usually go out of your way to avoid having to deal with problems in your life.

10. Difficult problems make you very upset.

11. When making decisions, you usually go with your “gut feeling” without thinking too

much about the consequences of each alternative.

12. When you have a problem to solve, one of the first things you do is get as many facts

about the problem as possible.

13. When attempting to find a solution to a program, you usually try to think of as many

different ways to approach the problem as possible.

14. When making decisions, you generally use a systematic method for judging and

comparing alternatives.

15. After carrying out a solution to a problem, you usually try to analyze what went right and

what went wrong.

16. You like yourself just the way you are.

17. You feel like you are doing everything just about right.

18. You feel socially accepted.

19. Do you have trouble getting along with your teachers?

20. Do you have trouble paying attention in school?

21. Do you have trouble keeping your mind focused?

22. Do you have trouble getting your homework done?

23. Do you have trouble getting along with other students?

Wave 2 Items

1. When you get what you want, it’s usually because you worked hard for it.

232

2. You never get sad or you felt sad.

3. You usually go out of your way to avoid having to deal with problems in your life.

4. Difficult problems make you very upset.

5. When making decisions, you usually go with your “gut feeling” without thinking too

much about the consequences of each alternative.

6. After carrying out a solution to a problem, you usually try to analyze what went right and

what went wrong.

7. You like yourself just the way you are.

8. You feel like you are doing everything just about right.

9. You feel socially accepted.

10. Do you have trouble getting along with your teachers?

11. Do you have trouble paying attention in school?

12. Do you have trouble getting your mind focused?

13. Do you have trouble getting your homework done?

14. Do you have trouble getting along with other students?

15. You enjoy life.

16. You feel happy.

17. You like to take risks.

18. You are sensitive to other people’s feelings.

19. You can pretty much determine what will happen in your life.

20. You live life without much thought for the future.

233

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BIOGRAPHICAL SKETCH

Joseph (Joe) L. Nedelec

Joe Nedelec received his undergraduate degrees (criminology and psychology) and his Master’s degree (criminology) from Simon Fraser University in Burnaby, British Columbia, Canada.

After three years as a faculty member in the Justice Systems department at Truman State

University (Kirksville, Missouri) Joe entered the doctoral program in the College of Criminology and Criminal Justice at Florida State University in 2009. During his time at FSU Joe published widely on topics related to biosocial and evolutionary criminology. In the fall of 2013, Joe will join the faculty at the University of Cincinnati in the School of Criminal Justice.

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