Social Centrality, , and Well-Being: Understanding the Immediate and Long-Term Relationships

A Dissertation Submitted to the Graduate School of the University of Cincinnati

In Partial Fulfillment of the Requirements for the Degree of

Doctorate of Philosophy (Ph.D.)

In the School of Criminal of the College of Education, , and Human Services

November 10, 2019 by Christopher P. Felts

B.A. Murray State University, 2010 M.A. Indiana University of Pennsylvania, 2011

J.C. Barnes, Ph.D. (Chair) Michael L. Benson, Ph.D. Dissertation Committee: Joseph L. Nedelec, Ph.D. Dylan Jackson, Ph.D.

Abstract Purpose

Juvenile deviance and its surrounding social factors are the bedrock of ’s historic, fundamental theoretical perspectives. Research involving this topic has spanned nearly a century. However, one concept that might lend insight to these perspectives has not been fully considered. That concept is the social centrality of youth inside their peer networks. By incorporating research from social networking analysis, this work seeks to more thoroughly explain adolescents’ prominence in their peer network and how that prominence relates to deviance. Additionally, this dissertation assesses how one’s level of centrality in a peer network during adolescence can affect one’s well-being into adulthood. Specifically, the well-being outcomes measured are alcohol use, depression, anxiety, aggression, and socioe- conomic status. Methods

This dissertation uses data from the Add Health Survey to conduct the analyses. Using a sample composing of 6,796 11–18-year-olds, multivariate regression analyses are conducted to test the effects of deviant behaviors on adolescents’ social centrality. The type of centrality selected for this study is proximity prestige — an in-degree, global measure of position within a network using friendship nominations. Well-being outcomes during adulthood were taken from both Waves 3 and 4 of the Add-Health Survey. Results

Findings show that younger adolescents’ (ages 11–14) social centrality is increased by adhering to prosocial behavioral expectations. Older adolescents (aged 15–18) see an increase in their centrality by exhibiting both prosocial and antisocial behaviors — specifically using alcohol and tobacco, having sex, and having trouble with teachers. Furthermore, analyses report that individuals who are central to their peer network in adolescence have higher educational success, higher incomes, and fewer feelings of depression.

i Conclusion

The analyses’ findings show measures of social centrality are related to both deviance and well-being later in life. Social centrality, while overlooked in past research, can help to clarify theoretical mechanisms and aid in detecting at-risk youth.

ii

Acknowledgments It has been a long, arduous road that has led to this acknowledgments section. While I am proud of having finished this degree, I also recognize that I could not have come to this destination on my own. There are many (too many) people to list here. But below are a few individuals I’d like to sincerely thank for their personal and professional contributions. First off, I would literally not exist if it were not for my parents. Looking back, I do not think that I could have chosen better parents to raise me. The two of you have provided me so much support and encouragement as I have meandered my way through life. You have also provided me with a model for how to be a good person. I am forever thankful for the two of you and love you both. In addition to my parents, I’d like to thank my sister, Kelly. You have taught me many things over the years — some good and some bad. But I am thankful for you and the knowledge that you will be there for me whenever I need you. I’d also like to thank my brother-in-law, Chris. Who not only puts up with my sister, but also loves and supports her. And last mentioned of my family, I want to thank you, niece Riley. I did not know I could feel so protective and proud of someone until you came along. You are an amazing young woman and I cannot wait to see where you go in life. In addition to my family, there are professors along my educational path who, without their encouragement, I never would have reached this end. Drs. Wann, King, Phaneuf, and Merlo, I have talked to you all at some point about graduate school and a career in academia. All of you have given guidance and have supported me in my decisions. I am eternally grateful to you all. I know I am lucky enough to have a job in academia while finishing this dissertation. And I genuinely cannot imagine starting my career at a better department that in the one where I was fortunate enough to land. Kelly, Kelly, and Beau, all of you are amazing and supportive colleagues. I look forward to many more years working and collaborating with all of you. The members of this dissertation committee were some of the best individuals I could have imagined to work with. Dr. Benson, you are actually one of the first faculty members I met at UC. Some of the best lessons I learned while at UC came from talking with you outside of white-collar class and during our meetings while I was your graduate assistant. Dr. Nedelec, I got to you know much better while working with you on this project. I really enjoy your sense of humor, but more importantly, I respect your knowledge and thoroughness as a scholar. Your comments shaped this work greatly. And lastly, Dr. Jackson, thank you so much for taking a chance on someone you had never met before. Your advice on this project was crucial and your willingness to give me your time is greatly appreciated. I can never thank the three of you enough. JC, I do not even know where to begin. I have learned so much from you over the course of the past few years. You have taught me so much of what it means to be a researcher, a teacher, and a mentor. There is not a single person on this Earth that I would rather have as my chair. I consider you a friend and look forward to working with you on future projects. And last but most certainly not least, I want to thank you, Natalie. There is no way I would have finished this degree without you. We have had our ups and downs (more ups, though) but I know now more than ever how much you mean to me. I cannot imagine my life without you. I look forward to starting the next chapter of our lives together.

iv 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 acknowledg- ment 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.

v Table of Contents

Abstract ...... i Acknowledgments ...... iv List of Tables ...... viii List of Figures ...... ix Chapter 1. Introduction ...... 1 Explanations of Key Terms ...... 2 Dissertation Road Map...... 4 Chapter 2. Popularity and Peer Influence ...... 6 What is Popularity?...... 6 Aggression and Popularity ...... 11 Neurological Research ...... 20 Health Outcomes and Popularity ...... 24 Summary ...... 25 Chapter 3. ...... 27 Evolutionary Theory ...... 27 and Psychology ...... 33 Summary ...... 47 Chapter 4. Moffitt and Adolescent Deviance ...... 49 ...... 50 Moffitt’s Taxonomy and Popularity ...... 56 Summary ...... 60 Chapter 5. Rule-Breaking and Social Centrality ...... 62 Correlates with Popularity...... 62 Confounding Variables ...... 70 Summary ...... 80 Chapter 6. The Current Study ...... 82 Research Questions ...... 82 Analysis Plan...... 82 Chapter 7. Methods ...... 85 Data Source ...... 85 Social Centrality ...... 86 Correlates with Popularity...... 94 Wave 1 Predictor Variables ...... 95 Wave 1 Controls ...... 97 Wave 3 Outcome Variables ...... 99 Wave 4 Outcome Variables ...... 100

vi Chapter 8. Results ...... 102 Descriptive Statistics ...... 102 Analysis Step #1 ...... 105 Analysis Step #2 ...... 107 Analysis Step #3 ...... 114 Chapter 9. Discussion...... 126 Research Question #1 ...... 126 Research Question #2 ...... 128 Research Question #3 ...... 131 Limitations & Directions for Future Research ...... 134 Policy Implications ...... 137 Conclusion ...... 138 References...... 140

vii List of Tables

8.1 Proximity Prestige Summary Statistics ...... 102 8.2 Descriptive Statistics for Analysis Steps #1 & #2 ...... 104 8.3 Regression of Proximity Prestige on Controls and Predictors — Full Sample 106 8.4 Regression of Proximity Prestige on Predictors — by Age ...... 108 8.5 Regression of Proximity Prestige on Predictors — Males by Age ...... 111 8.6 Regression of Proximity Prestige on Predictors — Females by Age ...... 113 8.7 Descriptive Statistics for Analysis Step #3 Outcome Variables ...... 115 8.8 Regression of Depression on Proximity Prestige ...... 116 8.9 Regression of SES at Wave 3 on Proximity Prestige ...... 119 8.10 Regression of SES at Wave 4 on Proximity Prestige ...... 121 8.11 Regression of Aggression on Proximity Prestige ...... 122 8.12 Regression of Alcohol Abuse on Proximity Prestige ...... 123 8.13 Regression of Anxiety on Proximity Prestige ...... 125

9.1 Popularity on Significant Predictors — by Age ...... 128

viii List of Figures

7.1 Simple Network Diagram ...... 88 7.2 Proximity Prestige in a 10-Node Network ...... 93

8.1 Proximity Prestige Histogram ...... 103

ix Chapter 1

Introduction

Ask any parent of a teenager if their child listens to what they say as well as they did before puberty. Most parents will laughingly tell you that their child does not listen to them at all. They will tell you that there has been a change. The opinions of the parents matter substantially less. What matters more to their teens is their friends (Steinberg & Morris, 2001). While many parents may dislike this trend it is actually considered by many to be a natural, healthy developmental period. In order for youth to grow into mentally healthy, functioning adults, they need to learn autonomy. And the most effective way for youth to do this is via peer interactions (Allen & Loeb, 2015). Interestingly, it is at this same time that association with peers (especially deviant ones) is one of the most powerful predictors for youth’s likelihood to commit crime (Osgood, Wilson, O’Malley, Bachman, & Johnston, 1996; Ragan, 2014). So youth, at this time, are at an interesting juxtaposition. In order to develop in the most healthy fashion, they must spend time with their peers. However, if they spend unsupervised time with their peers, they are more likely to commit deviant acts. This trend has become such an integral part of the field that criminology has developed myriad of explanations to explain the relationship of peer groups and deviance. Some contend that it is a learned behavior, others argue that it is the convergence of like individuals into a deviant group. Another group of theorists will argue that it is just a natural part of development for youth to offend. Regardless of the true explanation(s), the field of criminology has yet to fully consider one factor that could shed some light on the mechanisms between deviance and peer groups — social centrality. Among the most important new social skills that adolescents need to develop during this life stage is learning how to exist in a social hierarchy. The importance of peers’ opinions and rising within this pecking order is of paramount importance to adolescents (Allen & Loeb, 2015; Brown, 2004; LaFontana & Cillessen, 1998), for with being socially center comes access

1 to resources, prestige, and influence (Hawley, 2003). Unfortunately, the research assessing the relationship between social centrality and deviance is still in its formative stage. Due to this, this work draws parallels to two separate, but related, concepts from outside fields: popularity (from sociological literature) and status (from evolutionary psychology). Explanations of Key Terms

Both popularity and status have chapters discussing both their definition, as well as theoretical components related to the concepts. Before they are discussed, a delineation between key terms is needed. There are three distinct, yet related concepts discussed in this dissertation: social central- ity, popularity, and status. Social centrality refers to a class of measures termed in the social network analysis field. This class of measure was originally developed to quantify “stars" or important people or points in a given network (Bavelas, 1950). However, over time, these measures have advanced and expanded in their use. Their mathematical sophistication com- bined with recent proliferation of network data as well as computing power have allowed for centrality measures to make public transportation more efficient, allow corporations to assess the resource needs of their individual bank branches, and, relevant to this work, quantify influential people within a network of individuals (Scott, 2011). The centrality measure described later in this dissertation, proximity prestige, is the key variable in the analyses. And while it is powerful and easily interpreted, there has been little work that has been done relating this measure to deviance of individuals. Luckily, much work in and psychology have researched the relationship between the second key term, popularity, and deviance. And while there is no work that has statistically tested the overlap between popularity and proximity prestige, the constructs that each attempt to encapsulate do have similar characteristics. Popularity provides those who have it with many positive outcomes, but it could also be a key mechanism in the relationship between peers and crime. Early studies equated popularity to being liked by one’s peers. However, two studies in the late ’90s (LaFontana

2 & Cillessen, 1998; Parkhurst & Hopmeyer, 1998) delineated the concepts of popularity and peer acceptance (i.e. likability). These works showed evidence that being liked by one’s peers is not the same as having influence/status (or potentially being central to the social network) — the latter being the superior definition the researchers argued for. Following these groundbreaking studies, researchers have used sociometric methods and neurological equipment to uncover how this new conceptualization of popularity is developed, attained, maintained, and how it can be used to influence others. With the growth of research on this concept in both psychology and sociology, it is in- teresting that criminology has not integrated this construct into its theoretical frameworks. Traditional criminological theories — specifically control and cultural deviance theories — focused on the debate between socialization versus selection and which process better ex- plained the relationship between delinquent peers and deviance (Lilly, Cullen, & Ball, 2018). While this debate is an important one, it may have distracted the field from pursuing other lines of inquiry that attempt to tackle the specific mechanisms through which this relation- ship works. The third key term, status, relates to evolutionary psychology and describes one’s ability attain resources, influence, and mates for reproduction — mainly in hunter-gatherer societies. As descendants of ancestors who had evolutionarily advantageous genes, we can view our traits as ones that have a purpose. That purpose is to survive in a specified environment, attract a mate, procreate, and pass on our genes to offspring who will also be able to repeat the process (Buss, 2015). While this may, at first, sound like a relatively simple process, competition between rivals and challenges put forth by nature created a complex organism who creates complex social structures and expectations. And it is this environment that an adolescent must learn to navigate in order to survive and attract a mate. And the best strategy to achieve this was to compete for status. This conceptualization of status has parallels with both popularity and social centrality. Those with status will be able to influence others. Furthermore, those who seek to attain

3 high status will look to individuals who have status as models and may mimic their behavior — making them both popular as well as central to the network. The discussion regarding these three terms and their interrelation is an important one to distinguish as this dissertation is read. Popularity and status are not measured and, therefore, not tested. However, relationships between these two concepts and deviance are pivotal to establish hypotheses when assess how social centrality relates to deviant behavior. As each concept is discussed, it is important to both keep in mind that they are different constructs, but are simultaneously being used to infer hypotheses for the analyses. Dissertation Road Map

Considering this distinction, this dissertation will be organized in the following way. The first chapter following this one is a discussion on the research of popularity, what predicts its levels, and how it relates to aggression. Additionally, this chapter includes a summary of relevant neurological literature that relates to peer influence during adolescence. Chapter 3 gives a summary of the basics of evolutionary theory and a discussion over evolutionary psychology — a subfield which applies evolutionary principles to human behavior. This literature will then be applied to Moffitt’s Taxonomy (1993) in Chapter 4. Parallels will be drawn between all 3 key terms and Moffitt’s theoretical assertions. Chapter 5 lays out how deviant behaviors and demographic characteristics relate to popularity and social centrality. The goals of this study will be achieved by following a three-step analysis plan laid out in Chapter 6. The first step is to explore what factors — drawn heavily from the popularity literature — in the analysis predict centrality. Second, the study will assess how centrality differs across different groups in the sample and its relationship to deviant behavior during adolescence. Following this step, analyses will determine how one’s level of centrality in adolescence can affect individuals later in life. More specifically, depression, anxiety, aggression, criminality, and socioeconomic status will be assessed. Chapter 7 is the Methods section where all of the measures and coding are explained. The Results chapter displays tables from the analyses and brief contextual explanations of key findings. Finally, the

4 Discussion section relates the key findings to previous research and explains contributions to the knowledge base of the field. It also includes limitations of the study and closing thoughts.

5 Chapter 2

Popularity and Peer Influence

Criminological research has repeatedly found that a key risk factor for youth involving themselves in deviant activity is being around other delinquent peers (Brechwald & Prinstein, 2011; Pratt & Cullen, 2005; Warr, 2001). Work by Zimring (1998) actually suggests that adolescents primarily offend in groups while adults tend to offend alone. The two works that grounded much of contemporary criminological thought — Sutherland (1947) and Shaw & McKay (1942) — recognized this trend and their theories revolved heavily around it. In both frameworks, deviant behavior sustains itself by youth offending in groups and learning from and/or reinforcing each other. What has not been considered, however, is an adolescent’s level of popularity among his/her peers and how this relates to deviance. Those who are more popular are higher on the social pecking order. This position can allow popular youth to influence others. However, this level of popularity may increase the likelihood that they will offend. The section below will discuss popularity and its findings relevant to deviance. This phenomenon is discussed because it is a closely-related phenomenon to social centrality and will add insight to inferences made later in this work. I also include a section reviewing the neurological literature and peer influence as it relates to adolescents. What is Popularity?

The term “popularity" is a difficult word to tie down with a definition. It seems to be one of those words that is similar to pornography: you know it when you see it (Jacobellis v. Ohio). Even at its inception “popularity" was ill-defined. The Romans first named the concept — popularitas — meaning “belonging to the people." This definition is vague in two ways: (1) Instead of defining what popular is, it just says it belongs to “the people" and (2) it does not specifically define, “the people." The word survived and evolved through French, German, Spanish, English and others (Bukowski, 2011) suggesting that it is a universal concept that humans, in general, intuitively experience — at least in Western culture. While

6 the word’s lineage is interesting, its path into a construct to be researched is fascinating. While there are myriad ways that a word can be created, there are three general paths that a word finds its way into academic research. The first and most common path is when a word or expression is defined already. Crime fits this pathway. Crime is not only a word that everyone in society knows, but it is also something that is explicitly defined in codes of law1. A second common way is when a new construct is discovered and needs to be named (Bukowski, 2011). Sampson and colleagues (1997) defined a new construct and used the term collective efficacy to describe it. These two paths to research work well because they are beholden to a well-defined construct. And while debate over how to best measure these constructs does take place, it is kept to a minimum. Contrary to these routes, the third path occurs when a word is brought into the research realm without having an explicit definition (Bukowski, 2011). Popularity seems to have come into the psychological and sociological literature via this route. The problem with this pathway is obvious. Without knowing what exactly is being researched, it is difficult to operationalize from one study to another, thereby preventing any replicated, consistent findings2. It is not clear why this happened with popularity. Perhaps researchers believed it to be a concept that was already well developed and specific considering Western languages have continually adapted to accommodate for the phenomenon. Regardless of the reason, it has not been properly defined until recently. Accordingly, during the infancy of popularity research, one could state that they were studying popularity with no qualifiers. It was largely held that, to measure popularity, one just needed to find out how much an individual was liked among his or her peers. With this definition of popularity came findings suggesting that popularity was associated with emotional regulation, prosocial skills, and social adjustment (Mayeux, Houser, & Dyches,

1There are works that debate whether measuring crime as an outcome is valid since the concept is socially defined. However, for this example, the explicit defining of the construct for use as a measurable phenomenon is what is being considered. 2Other concepts in criminology have followed this path and survived — e.g. strain (Lilly et al., 2018). While the three causes of strain were given, the concept itself has yet to be explicitly defined. It is still an issue that needs to be resolved.

7 2011; Burt, 2009; Cillessen & Rose, 2005). The relationship was simple: popularity is good, being unpopular is bad. However, two seminal works published in 1998 independently discovered the same con- clusion: researchers had been studying popularity incorrectly. The first of the two works, LaFontana and Cillessen (1998), interviewed 135 fourth- and fifth-graders. Participants were interviewed individually. The process involved participants being told that three new stu- dents were going to be added to their class and the researchers wanted the child’s opinion as to how the new student was going to act in his/her new class. Researchers presented the youth with hypothetical vignettes of these new students. The order in which the target (hypothetical) youth were presented was varied, but there was one “neutral" youth, — the researchers said nothing of the youth’s popularity — one youth who was deemed “popu- lar", and one “unpopular". Researchers presented two vignettes for each target youth. One vignette had a positive outcome and the other had a negative/hostile outcome. Following each vignette, participants were asked to describe the youth’s motivations and intents for the behavior in an open-ended format. In addition to the open-ended response, participants used a 6-point Likert scale from “definitely not" to “definitely" to denote the level that they believed the outcome (positive or negative) was intended by the target youth. Put together, these measures attempted to look at the likability of different peers combined with those peers’ aggression, altruism, and intentions. The study’s original hypothesis was that the schoolchildren would like popular peers the best, unpopular peers the least, and neutral peers in between. The results in fact showed that the participants liked popular and neutral youth the same. They also viewed popular youth as more aggressive and less trustworthy than neutral peers. This trend suggested a clear delineation between the concepts of likability and popularity. LaFontana and Cillessen’s (1998) study illuminated an inconsistency in the literature. The other study conducted by Parkhurst and Hopmeyer (1998) identified the two constructs at play. Also using fourth- and fifth-graders, Parkhurst and Hopmeyer (1998) attempted to

8 bridge similar research being done in psychology and sociology. The psychological construct involved was sociometric popularity (peer acceptance), and the sociological construct was reputational popularity. The researchers gave the participants a survey in which they identi- fied their peers that they liked/disliked the most and separately identified which peers they viewed as popular/unpopular. In other words, peer acceptance was measured by counting the number of times a subject received a friend nomination (or a “like") while popularity was directly measured by asking youth to name the popular youth among their peers. What Parkhurst and Hopmeyer (1998) discovered is that the two constructs were correlated, but distinguishably different concepts. Following these two studies, researchers have explored the relationship between these two constructs. They generally consider the construct concerning likability among peers to be peer acceptance. The second construct is labeled popularity. Generally, popular youth are seen as well-known, socially center, and having influence among their peers (Adler & Adler, 1998). They are able to act aggressively when needed and know how to get what they want. For example, consider John and Chuck. These two boys are 15 years old and go to school together. John is liked by many of his classmates. He gets good grades, helps others, and is athletic, but only uses his physical prowess on the field. Chuck, on the other hand, is known by everyone in school, although he is not very well-liked. His core group of friends are considered the “in-crowd" and others mimic what Chuck does to increase their social standing. Of these two, Chuck is considered popular where John would be considered accepted by his peers. This example begs the question: what are the properties of someone who is popular? Researchers in the field conceptualize popularity as a balance between two seemingly con- tradictory concepts: differentiation and assimilation (Bukowski, 2011; Cillessen & Rose, 2005). Differentiation is the process of setting one’s self apart from others. People can do this through multiple means. Some develop a skill or talent — such as athleticism, musicianship, or artistic ability. Other ways include having money, being attractive, or publishing high-

9 quality journal articles at a high frequency. If the definition were to stop at this point, popularity would be synonymous with elitism. Both definitions ascribe to setting themselves apart from others. Where they differ, however, is that a popular individual also assimilates — or adopts the practices and values of a group. The key difference lies in that an elitist’s place is defined by some external criterion. A popular person’s status is given to him or her by a group (Bukowski, 2011). Think again of the example of Chuck from above. As stated earlier, he is not well-liked, but people emulate him because they want to be associated with him. He defines what is accepted and expected of his group. So, he is both distinct — he sets trends and follows his own path — and a central member of the group simultaneously. It is interesting why a group would elect to function in this way. One accepted explanation as to why groups behave in this manner is due to the nature of a group wanting to promote its own goals (Collins & Raven, 1969). The group creates a reward structure with status as the prize. The one who attains status does so by defining and promoting the expectations and standards of the group. Members of that group also receive status as being part of the “cool crowd" (Levine & Moreland, 1998). Most people who went to an American high school (especially a public one) can think of an individual that few liked, but everyone would not mind being associated with. A Hollywood example of a person like this can be seen in the cult classic Dazed and Confused. In the depicted town, 8th-grade boys (incoming freshmen) are chased down by junior (incoming senior) boys all summer. If caught, the 8th-graders are then paddled by their older peers with what look like handmade cricket bats (or the fraternity paddles from Animal House). While most of the older youth paddle their younger peers because of tradition or because it was done to them, there is one individual who seems to get sick pleasure from it — Fred O’Bannion. He is considered a popular high-schooler, but throughout the movie it seems that nobody really likes him, he is just put up with because of his physical prowess and position as a star football player. At one point, the main character of the movie (Randall Floyd),

10 who is the star quarterback, even says that nobody really likes being around O’Bannion, but he is a good guy to have blocking for you. While O’Bannion is just a fictional representation, there are many real-life O’Bannions. They are popular, not because they are liked, but because they have some sort of status. In the example above, O’Bannion is not liked largely because of his level of aggression towards others. He is depicted as a bully who only knows how to communicate through physical dominance. This depiction is one time where academia and Hollywood agree on something. While being overly aggressive makes you unlikable, it does not necessarily mean that you will be ostracized by your peers. The next section reviews the complicated relationship between aggression and popularity. Aggression and Popularity

While popularity in its original form was viewed as a wholly positive quality, studies over the past 20 years have suggested otherwise. Research on the construct has grown considerably since its re-conceptualization from likability into a definition of status. One particular area that has grown is the exploration of the negative characteristics of those who are popular (Mayeux et al., 2011). A key attribute that has been of central focus is aggression. This section discusses how youth use aggression to gain and keep their status. However, before aggression and its relationship with popularity is discussed, a clear con- ceptualization of how these two concepts are measured is needed. Chapter 2 noted that likability/peer acceptance can be measured by counting the number of times an individual is named as a friend among his/her peers. In contrast, popularity is most commonly measured by asking students to list the most popular peers in their school. Because the researchers of these earlier studies consider the concept of popularity to be a well-known, innate idea, having participants name popular youth with no definition is preferred. The constructs pop- ularity and acceptance are closely linked but distinguishable. Interestingly enough, the data suggest that it does not start out that way. When children are young, a likable person is a popular person. The differentiation between the two constructs seems to start at roughly

11 age 10 where the two measures correlate at .4 (LaFontana & Cillessen, 1999). It seems that once children hit this age, social dynamics begin to change. This trend should not be surprising. Many transitions begin in early adolescence. Indi- viduals in this age-group hit puberty, have hormonal changes, and begin the process of both sexual and a transition into adulthood (Susman & Rogol, 2004). This is also the point at which many youth start to experiment with deviance (Farrington, 1986; Moffitt, 1993) Most early studies of aggression and social acceptance found a negative relationship be- tween the two variables. However, the relationship between aggression and popularity in more recent studies found modest positive correlations (LaFontana & Cillessen, 1998, 1999; Parkhurst & Hopmeyer, 1998; Prinstein & Cillessen, 2003), and some of them strongly cor- related the two together (Caravita & Cillessen, 2012). The previously referenced studies were all basic cross-sectional designs analyzed with multivariate regression techniques. More recently, researchers have employed more sophisticated techniques to better explore the re- lationship between these concepts and how they change over the life-course. The first longitudinal assessment of aggression’s role in peer acceptance and popularity was conducted in 2004 by Cillessen and Mayeux. They followed a cohort from 5th-grade to 9th-grade. The researchers measured both peer acceptance and popularity but also overt aggression and relational aggression. Overt aggression refers to aggression performed in the open, such as fighting, bullying, name-calling (in front of the target peer), and the like. Contrarily, relational aggression is more subversive. It refers to acts such as gossip, rumor-starting, group shunning, and manipulating relationships. Their results were that the consequences of using aggression reduced over time. More specifically, peer acceptance’s ini- tial negative relationship with overt aggression became less pronounced as youth progressed through adolescence. Furthermore, both forms of aggression had a positive relationship with popularity, and this relationship increased as the cohort aged. The only relationship that remained negative was relational aggression and peer acceptance for girls; and that negative relationship actually got stronger over the life-course. Cillessen and Borch (2006) used the

12 same data to conduct a multi-level model. They found similar results — both types of ag- gression raised status level. However, both relational and overt aggression were negatively related to peer acceptance over the eight years that were analyzed. Further analyses of this relationship yielded other interesting findings. Using path anal- yses, researchers found a self-reinforcing relationship between popularity and aggression. Cillessen and Borch (2006) reported gains in aggressive behavior as a result of a gain in popularity while another study (Rose, Swenson, & Waller, 2004) found a reciprocal effect between relational aggression and popularity for ninth-grade girls and the same effect for both types of aggression and popularity among boys. Finally, Puckett, Aikins, and Cillessen (2008) found that relational aggression positively predicted popularity among middle school- ers. These findings combined suggest that popularity and aggression reinforce each other in a bidirectional manner. Certain forms of aggression seem to be successfully used by some youth, giving them status. That status then seems to increase their ability and/or need to use aggression at a more frequent rate. But which youth are able to use aggression successfully? Think back to high school. There were probably students there who were aggressive and unpopular while other students were also aggressive, but accepted and admired by their peers. While the research above suggests that aggression may be used by popular youth to obtain and maintain status, the indiscriminate use of aggression will not necessarily grant an individual status. It must be used strategically. Hawley (2003) argues that those who use aggression and pair that behavior with a prosocial act minimize the damage of using aggression. She labels these youth “Machiavellian actors" because they are socially adept at balancing “getting along and getting ahead" (Hawley, 2003). Evidence of this behavioral strategy can be seen in the work of Sutton and colleagues (1999). This study showed that if youth who use aggression to further an end attempt to reconcile in some way after dominating his or her peer, their peer acceptance level rises. These findings suggest that socially savvy youth know how to assert their dominance while saving face and maintaining their status. Machiavellianism and its

13 compatriot members of the Dark Triad are discussed further in the next section. Perhaps reconciliation’s effect on status has to do, partially, with the status of the victim. Andrews and colleagues (2017) tested the assumptions of social dominance theory — which holds that aggression, if used properly, can be an evolutionary adaptive way to gain status and access to resources — and found that those who used aggression on other high-status victims saw their status rise. Acting aggressively on a low-status victim had no effect on their status. Because aggression is most effective between two high-status individuals, it makes sense that reconciliation is a key behavior among these individuals. High-status youth, generally, have overlapping friend groups (Cillessen & Marks, 2011). Therefore, if one youth dominates another, the aggressor will undoubtedly upset the friends of the victim. Reconciling with the victim can be a way to smooth things over between not only the victim, but friends of the victim. The type of aggression also matters. Researchers have distinguished between proactive aggression (initiating the behavior) and reactive aggression (responding to aggression). Stoltz and colleagues (2016) conducted a longitudinal analysis that followed seventh- and eighth- graders. They used a sociometric popularity measure to explore the relationship between popularity and both types of aggression. Their results were that both popular and unpopular youth used aggression. However, stably popular youth used proactive aggression while stably unpopular youth used reactive aggression. These findings were further supported by Volk, Camilleri, Dane, and Marini (2012) who argued that bullying is an evolutionary seen across cultures and time periods. Machiavellianism. Recent work on the concept of Machiavellian Intelligence can shed some additional light on the concept of “Machiavellian actors" and their ability to manip- ulate others. Relatively recently, the fields of psychology and evolutionary sciences have developed what is called the “Dark Triad" (DT). The DT includes , narcissism, and Machiavellian behavior. While the three concepts overlap, they are distinct from one

14 another.3 Psychopathy refers to an inability to feel empathy or form bonds with others, callousness, impulsivity, and arrogance (Hare, 1999). Narcissism refers to high levels of self- centeredness, egoism, and grandiose opinions of one’s ability/self-worth (Jonason & Webster, 2010). Finally, Machiavellianism (Mach) is defined as an individual who is willing to forego moral expectations in order to exploit an individual for a reward. Furthermore, they see others as means to an end in achieving their goals. In other words, Machiavellianism is a cheater strategy where a Mach actor claims they will uphold their end of a bargain (be it explicit or implied) but will re-neg in the right circumstance in order to increase reward while decreasing effort (Muris et al., 2017). While psychopathy and narcissism most certainly have relevance in this discussion, the more salient and relevant member of the DT is Machiavellianism as it directly pertains to using others to gain success, status, or resources. Christie and Geis (1970) were the primary researchers to explain this concept in the field of psychology. They argued that there was no line that separated Mach actors from non-Mach actors. Rather, the construct existed on a spectrum across individuals. This level — while largely genetically influenced — also only shows itself, behaviorally, in a ripe environment. In general, an environment that is explicitly, or obviously, competitive and individual- focused does not bode well for tactics used by Mach actors. But environments that have an expectation of cooperation are. Bereczkei, Szabo, and Czibor (2015) used two versions of the public goods game. In this game, groups of 5 were put together. They were each given a starting dollar amount to work with. Participants were told they could give whatever amount they wanted to the public fund and whatever they wanted to their private fund over the five rounds. In the cooperative version, the public fund was redistributed equally among the 5, regardless of their deposits into the community fund. In the competitive version, participants were instructed that only one person would receive the community funds, and that would 3Some researchers argue that the three concepts are part of one construct. Attempts to standardize the measure in a way that would allow for measuring individually or as a single factor have been done rather successfully (Jonason & Webster, 2010; Muris, Merckelbach, Otgaar, & Meijer, 2017).

15 be person with the highest balance in their personal account. What the researchers found in their sample of 144 undergraduate students was that participants who scored high on a Mach test put significantly less money in to the community fund that did low-scoring Mach participants in the cooperative version. However, in the competitive game, there was no difference between the two groups. This suggests that high-level Mach individuals do well in environments where reciprocity is the expectation. They can assess the minimal level of reciprocity they need to show in order to receive a greater return from others. In an environment where reciprocity is replaced with competition, they have fewer individuals to “dupe." Other research has looked at emotional intelligence among Mach actors. Originally, it was hypothesized that high-Mach actors would score high on emotional intelligence — or the ability to understand others’ emotions and adapt one’s behavior to those emotional cues. However, high-Mach actors actually score low on this measure (Austin, Saklofske, Smith, & Tohver, 2014; Pilch, 2008). Instead what researchers have found is that high-Mach actors have a high sensitivity toward situations, rather than people. Instead of reading people, it seems that these actors read environmental cues, take information acquired in past situations, and apply to new ones (Bereczkei, 2018). Their lack of attention to the emotions of others could allow them to minimize — in their mind — the harm done to others by their actions while simultaneously freeing up cognitive resources to process more important criteria. The relationship between reading an environment and cognitive power can be seen in recent research that has established a link between Machiavellianism and Working Memory Index (WMI) (Bereczkei & Birkas, 2014). Higher levels of working memory are associated with “rational" thinking, reason, and cognitive flexibility. This benefit of working memory would allow an individual to process more environmental cues and make a more accurate assessment of the situation while also applying past experiences to new, novel situations. In addition to these valuable studies, meta-analyses have been conducted assessing the relationships between Mach (as well as psychopathy and narcissism) and personality traits.

16 Muris and colleagues’ (2017) reviewed 22 studies assessing how Machiavellianism relates to the Big 5 personality traits. The researchers found that Mach was significantly, negatively correlated with both extraversion (r = −.16) and agreeableness (r = −.25). These relation- ships were expected as Mach actors usually employ their manipulation covertly. Additionally, the profile above describes an individual who is selfish and inconsiderate (in a situation where it is advantageous). Muris and colleagues’ 2017 analysis also looked at the unofficial sixth personality trait, honesty-humility. This factor included measures on sincerity, fairness, greed avoidance, and modesty and was drawn from seven studies. Machiavellianism was significantly and nega- tively correlated with all for measures except greed avoidance, which was negative but not significant. These results suggest that, in general, high-Mach actors are more likely to act in dishonest ways in order to achieve their goals. It is not much of a stretch to see how an individual with these attributes could have an advantage at obtaining status — regardless of age. Additionally, what is not being said here is that every popular person is a Machiavellian actor. However, those who possess some of these traits or abilities would have more options in their social interactions. Similar to bi-strategic youth discussed above, having more “clubs in the bag" and knowing how/when to use them gives an individual a higher chance of achieving their goals. Evolutionary Perspective. These trends regarding how popularity functions in adoles- cent peer groups seem at odds with each other at first glance. How can aggression and manip- ulation make someone popular? Prosocial individuals are considered empathetic, agreeable, and well-liked. Antisocial individuals are aggressive, impulsive, and self-serving. How can there be overlap? The answer to these questions pivot on perspective. If we use the lens of traditional, developmental theory, the answer is that these concepts are contradictory and a resolution cannot be made. However, if we look at these findings with an evolutionary perspective, the contradiction no longer exists (Ellis et al., 2012; Hawley, 2003). The evolu- tionary concept will be briefly introduced below, but will be discussed in much greater detail

17 in Chapter 3. Behaviors such as substance abuse, underage sex, aggression, and other deviance in ado- lescence can be considered risky behavior. Risks are calculated responses to environmental stimuli. Some risks may seem absurd to some but completely legitimate and worth taking by others. These risks factor in both the reward and possible negative outcomes for the behavior. In adolescence, youth are learning to manage new situations, responsibilities, and desires. Their desires will be somewhat different from adults — e,g. peer acceptance is more highly valued (Brown, 2004) — and the risks may not be as well known due to their lack of experience (Ellis et al., 2012). So if one can consider aggression and other risky behaviors to be a tool for social influence rather than a wholly maladaptive behavior, then one can consider a perspective where both prosocial and antisocial strategies can be used in tandem to further an end (Hawley, 2003). That end, in this perspective, is access to resources. Hu- mans are not directly designed to desire evolutionary fitness. We are, generally speaking, ingrained to desire things such as safety, food, sex, status, and other possessions that led to our ancestors’ success in evolutionary history. These possessions that we covet are lim- ited resources. Over millennia, shaped our species through neurobiological mechanisms that detected and elicited responses appropriate for the environment that in- creased the chances of humanity’s survival. These mechanisms are the underlying, ultimate explanations to our thinking patterns. These patterns are also with us today (Ellis et al., 2012) and manifest themselves in a way that is relevant to today’s social structures — in other words a proximate manifestation. For example, our tendency to respond to frustration with aggression can be viewed as an evolutionarily-ingrained emotional response that had a use in the past. Today, it’s use — when used instrumentally — can also be an advantage to individuals and grant them access to coveted possessions (Volk et al., 2012). Now consider a high school student’s experience through the lens previously given. There are limited, highly desirable resources and there is competition to gain access to them. Youth desire status and acceptance above almost anything else during adolescence (Allen, Porter,

18 & McFarland, 2006; Blakemore & Mills, 2014; Brown, 2004) because it is the most likely way to gain access to friends, better dating prospects, and control over peers. Someone who is socially adept enough to know when to use which strategy has an incredible advantage over others who can or will only use one strategy. These bistrategic individuals are more likely to gain status and are also more likely to keep it once they obtain it. In addition to an explanation of motivation for this behavior, evolutionary thought can also help explain why this behavior exists at this time in the life-course. As stated before, adolescents prioritize acceptance, recognition, and status more highly than any other group. They also are beginning their sexual maturation and transition into adulthood. This mo- tivates youth — especially males — to gather resources largely in order to obtain a mate (Pinker, 2002; Wright, 1994). Status is the most reliable way to get this. Therefore, it should be no surprise that an increase in risky behavior is seen at puberty because youth become able to reproduce, have not had time to gain resources, and need to develop a novel way of obtaining said resources quickly. The timing of these two events increases the chance for procreation and survivability of offspring (Ellis et al., 2012; Kanazawa & Still, 2000).4 Considering that the evolutionary perspective for how popularity functions in a group has a great amount of explanatory power, one can assume that play a part in this relationship. This relationship is further reinforced given that qualities associated with popularity (e.g. athleticism, intelligence, attractiveness, aggressiveness) are genetically in- fluenced; and all genetic influence on behavior asserts its effect via the brain. Therefore, it should be no surprise that with the evolutionary direction of research and advances in neuro- logical observation technologies, much of the growing literature that examines peer influence is being conducted in the neurological realm. The next section discusses this literature. 4It should be noted that adolescents’ level or risky behavior is not determined solely by their drive to reproduce. Other factors such as obtaining a higher degree of freedom and autonomy as well as curiosity regarding new experiences also play a role in this. These factors (among others) are not irrelevant to the phenomenon. They are merely not tested in this study.

19 Neurological Research

Research has explored the conceptualization and measurement of popularity for decades, but the bulk of the research has focused on the environmental aspects. However, following Caspi and colleagues’ (2003) finding of a gene-environment interaction between MAOA and child maltreatment, researchers have begun focusing on research to find other interplay between genes and environment. More recently, however, the interaction research has been called into question. Because of the mechanics behind the analyses, there seems to be too little statistical power to rule out that these interactions are not just artifacts of hypothesis testing (Barnes and Beaver, n.d.; Duncan and Owens, 2011; Sham and Purcell, 2014). To fill this gap in the research, neurological studies have begun to meld psychological principles with brain scans. This trend is a product of both the evolution of science and the availability of functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) equipment. The results of these works are discussed in this section. The work regarding gene-environment interactions, while controversial, is not completely without merit. While the exact genes that have been proposed to interact with specific environmental stimuli may not be completely accurate in its conclusions, it has suggested and provided evidence that both genetic and environmental forces depend on one another. As a result of this, theoretical frameworks have developed to explain these processes (see Hankin and Abela, 2005 for a review). These theoretical frameworks are relevant to neurology because many of the genes that have been tested deal with structure or regulation of the brain. The MAOA gene tested by Caspi and colleagues (2003) was suspected and tested due to its influence on behavior regulatory systems embedded in the serotonergic system. And while the mechanisms and specifics of the frameworks vary, all of the frameworks share the same assumption in regard to peer groups — some people are more susceptible to peer influence than others (Schriber & Guyer, 2016). What may be more useful to policy makers and parents is that adolescents, more than any other group, are the most susceptible to peer influence. Starting at puberty, the brain

20 — in addition to the body — begin to undergo changes. These changes are caused by environmental factors such as sexual maturation and in an increase in peer involvement (Nelson, Leibenluft, McClure, & Pine, 2005) that work in tandem with the neurological processes of myelination and the re-balancing of excitatory and inhibitory inputs (Monahan, Guyer, Silk, Fitzwater, & Steinberg, 2015) and cause the brain to “re-orientate".5 Neural plasticity is at a high during adolescence, second only to infancy. The amount of plasticity suggests that this life-stage can be seen as a great time of opportunity for recovery of past traumas and have both positive and negative lasting effects on development (Bredy, Zhang, Grant, Diorio, & Meaney, 2004; Crone & Dahl, 2012). It also suggests that, evolutionarily speaking, there is an advantage to having a malleable, readily-influenced brain during the time when the desire to sexually reproduce is at its height. This will be discussed later in the section. In regard to the discussion of peer influence, it is well known that teenagers care more about what their friends think than they do about their parents’ opinions (Steinberg, 2008; Steinberg & Morris, 2001). But this vein of research is beginning to inform us on individ- ual variations in susceptibility to peer influence. These findings can be divided into three frameworks: peer presence, peer evaluation, and social exclusion. Peer Presence. As stated previously, research involving fMRI and EEG equipment in laboratories have been instrumental in this research. Neuroscientists are now able to monitor brain activity while putting youth into simulated situations. One popular example of these is a simulated driving task called “Stoplight." This task takes place on a computer screen displaying a straight road with 20 intersections from the driver’s perspective. All intersections have stoplights. As the driver approaches an intersection, he or she is asked to make a decision of whether to brake or go through the intersection. To encourage risk 5An example of brains altering throughout adolescence and affecting behavior can be seen in somatic marker theory (Crone, 2016). This theory asserts that as humans age, their brains — specifically the somatosensory and orbitofrontal cortices — develop and prioritize long-term gain over short-term. During this process humans also develop an understanding that rules have legitimate exceptions that may benefit an individual in the long-term. However, evidence of this maturation is not present until age 16 at the earliest, suggesting that these regions of the brain develop more slowly than others.

21 taking, experimenters generally give a monetary incentive for faster times through the course. However, that incentive is taken away if the driver crashes. One study used this simulation to test how the presence of peers would affect adolescent drivers. Chein and colleagues (2011) used an fMRI to see if the adolescent brain reacted to the simulation differently when peers were present versus when they were alone. Chein and colleagues (2011) found that the ventral striatum (VS) — an area of the brain known for reinforcement and reward perception — had a negative relationship with self-reported resistance to peer influence. This suggests that those with a more active VS are more susceptible to rewards, especially social rewards. Regions of the brain that perform similar tasks (e.g. the medial pre-frontal cortex (mPFC)) have also been found to be similarly affected for certain individuals when peers were present (Segalowitz et al., 2012). Taken together, this line of research seems to support the conclusions drawn by Rancourt and Prinstein (2010) who found that youth believe that rule-breaking and deviance will increase their popularity since presence of peers increase risk-taking and performing risky behaviors is valued by adolescents. Peer Evaluation. Other work has evaluated how adolescents are differentially influ- enced by the experience of being evaluated by peers. Researchers who have studied the adolescent striatum — the region of the brain that is responsible for decision-making, re- ward structures and, most importantly, reinforcement — report an increase in activity of the sub-organ during adolescence (Moor, van Leijenhorst, Rombouts, Crone, & der Molen, 2010). This suggests that youth are more attune to what their peers think of them and prioritize their peers’ opinions more highly than at other ages. What is interesting about this phenomenon is that it works for negative evaluations as well as positive ones. This supports the notion that, if given proper environmental cues, adolescence is a time where youth can recover from childhood traumas and/or problems (Schriber & Guyer, 2016). Not surprisingly — because it is considered to be a hub of social circuitry (Bickart, Dickerson, & Feldman Barrett, 2014) — the amygdala functions in a similar way. Adolescents’ amygdalae,

22 generally, activate more intensely when they know they are about to be evaluated by a peer than adults’ do.6 Social Exclusion. One of the key tools for social exclusion, or ostracism, studies is a program developed by Williams and colleagues called “Cyberball." While this tool started out as a face-to-face activity involving confederates, time, and space; monetary considera- tions caused the researchers to turn the activity into a web-based social experimental tool (Williams & Jarvis, 2006). The web-based version is a bit of a ruse. You log on with the idea that you are exercising your brain. Other players log on and you play catch with a group of people to stimulate your neurons. However, what participants do not know is that there are no online players. Experimenters are able to manipulate how many times a participant gets thrown the ball. What researchers have found over time is that, even for an arbitrary, online activity, the ones who get thrown the ball the most feel the best and those who never get thrown the ball feel the worst (Williams & Jarvis, 2006). Recent studies have used Cyberball in tandem with fMRI equipment (Masten et al., 2009; 2011). What they found was that the youth who were excluded from the tossing game saw more activity in the social-affective regions of the brain and less activity in the regions that control regulation. What this means is that when youth are rejected from peer groups, they feel bad emotionally and have a limited capacity to control their behaviors in response to the ostracism. In this same vein, Peake et al (2013) found that this same brain region they called the rTPJ (right temporoparietal junction) actually mediated the relationship between an adolescent’s ability to resist peer influence and their risk-taking after being rejected. Driving risk models, similar to the ones discussed earlier, have found the same results as the Cyberball activity. Falk and colleagues (2014) found that youth who have the most neural reactivity in the rTPJ regions of the brain intensely feel the effects of ostracism and exhibited the riskiest driving behavior when peers were present. These findings suggest that ostracism 6It should be mentioned that much of the research from this subsection focused on teens with overactive striatum and amygdalae, even for adolescents. Many of their cases had a history of social anxiety. Regardless, their findings also show that, as a whole, adolescents’ brains are more sensitive to peer interactions.

23 differentially affects individuals and can increase their risk-taking behavior. Further ostracism research has found that the striatum and amygdala play a role here as well. More and colleagues (2010) used slides of unfamiliar peers’ faces with differing expres- sions and showed them to participants. While wearing an fMRI, they asked participants if the person on the slide would accept them followed by feedback on acceptance or rejection. Participants were divided into four age groups: (8–10), (12–14), (16–17), and (19–25). The researchers found a positive linear relationship between age and activity of the amygdala and striatum with both acceptance and rejection feedback. All these works taken together suggest that adolescents are incredibly susceptible to social forces — and some youth are more susceptible than others. As seen in the research above, the forebrain — more specifically the amygdala and striatum — plays a significant role in peer interactions. This is incredibly important for adolescents as their brains are re-orientating throughout this stage of their lives. The synaptical pruning and myelination largely take place in the forebrain, suggesting an increased level of sensitivity to social situations while the behavioral regulation region lags behind in its maturation. This trend is further exacerbated when youth are surprised or upset by social situations (Blakemore & Mills, 2014; Burnett, Sebastian, Cohen Kadosh, & Blakemore, 2011). Health Outcomes and Popularity

The evidence that adolescents are more susceptible to their peers’ influence speaks to the power of the social network on this group. As stated earlier, adolescence is a time where youth learn to exist in a hierarchy established by themselves, not adults. Youth’s ability to form meaningful relationships during this time is paramount for their well-being — not only during adolescence, but also into adulthood. A recent study by Narr and colleagues (2017) looked at the quality of relationships and those relationships’ influence on mental health outcomes later in life. The researchers used a sample of 184 seventh- and eighth-graders from the southeastern United States who were followed for ten years. They examined youth’s relationships comparing those who had a small

24 number of close friends to other youth who had more friends who were not as attached. Narr and colleagues (2017) found that the first group — those with fewer friends but were tightly knit — showed significantly lower levels of depression, social anxiety, and higher levels of self-worth at age 25 when compared to the second group. The second group of Narr and colleagues’ (2017) study is akin to those with high status discussed earlier, while those in the first group are similar to those considered to be “likable" or “accepted." This suggests that popular youth who have multiple loose affiliations instead of meaningful friendships are more likely to experience negative health outcomes later in life. This finding is consistent with the literature regarding the effect of adolescent stress and self-image on mental health during adulthood. 7 An alternative — or additional — explanation may be that the same personality charac- teristics that increase the likelihood of someone having high status are the ones that protect them from depression and anxiety. Trzaskowski, Zavos, Haworth, Plomin, and Eley (2012) found that genetic factors explained 68% of the variance of anxiety. This suggests that there is a genetic component to anxiety and that that component may attract or repulse potential social connections. Similar results were found with depression (Ksinan and Vazsonyi, 2019; Lau and Eley, 2006; 2008). Analyses later in this work explore this question. Summary

Adolescence is a crucial time for development. It is a period of great plasticity and potential for positive change for the brain. Youth have the opportunity to overcome past developmental hurdles and make recoveries that help them thrive in adulthood. However, it is also a time of high susceptibility. Adolescence — more than any other time in an individual’s life — is a time where acceptance in their peer groups is of chief concern. In the course of exploring this process, researchers have discovered that youth who are considered popular are not necessarily friendly or well-liked. Instead of having a large group of friends, they are characterized by holding status and having influence among their peers. This research, 7For an extensive review on this relationship, see Lupien and colleagues (2009)

25 simply put, has asserted that boys who are funny, athletic, and have physical prowess are popular. Additionally, girls who are athletic and can balance being nice while keeping her group exclusive are popular. These relationships are a good starting point to begin to assess social centrality’s re- lationships with deviance. The parallels between measures leads to questions regarding if the same factors that predict the level of popularity will also predict social centrality in a similar fashion. Therefore, my first research question of this dissertation will be “What are the factors that predict social centrality?" While this question is worthwhile to answer as it may inform us on well-being outcomes later in life, the following chapter will continue by discussing a reason for humans becoming social animals and establishing social hierarchies in the first place. I will discuss evolutionary theory in general, then specifically evolutionary psychology — the field that enables us to explain why humans have specific traits and perform certain behaviors based on evolutionary thought.

26 Chapter 3

Evolutionary Psychology

Since ’s (1859) work , scientific thought regarding the biological functioning of animals has undergone a revolution. Researchers have been able to take their observations and place them into a theoretical framework that can explain the creation of biological structures/systems. Over time, evolutionary theory has been refined and applied to fields other than biology. Most recently, evolution has begun its integration into the behavioral sciences. For this dissertation, the field that is of central focus is psy- chology. It is psychology’s integration of evolution that has caused researchers to apply the theory’s foundational principles to behaviors and instincts, not just physiological structures.8 This section gives a brief overview of evolutionary theory, then shows how evolution has been integrated into the social sciences, and finally seats status into the evolutionary psychological framework. Evolutionary Theory

The beginnings of Darwin’s 1859 work laying out evolutionary theory began with his voyage on the HMS Beagle to the Galapágos Islands. Because this island chain was largely left untouched by humans until this time, Darwin was able to observe an unspoiled landscape, filled with plants and animals only influenced by nature — not humans. A seemingly minor observation from this voyage involving finch beaks gave a spark for his theory of life. Darwin noticed that every island within the chain had a different species of finch. The defining quality of the was beak length (Buss, 2008). Darwin hypothesized that all of these finches started off as one species and, due to consistent, inter-generational environmental pressures, began to specialize to their environment. Environments with an abundance of insects gave finches with small, powerful beaks an advantage. And other environments where nuts and seeds were plentiful benefited finches with longer beaks as they were better equipped to break open the shells. 8More accurately, it is because of evolution’s refinement of one physiological structure in particular — the brain — that evolution has been able to affect animal behavior over time.

27 This train of logic led Darwin to develop a theory that explained where life began and how speciation occurs. And while Darwin was correct in many of his assertions, it was not until Gregor Mendel’s (1866) work was re-discovered and applied to Darwin’s theory decades later that evolutionary theory had a mechanism for heredity — the gene. Mendel’s principles stated that genes carry the information from parent to offspring, but the two chief forces that push evolutionary forward are natural selection and sexual selection. Both of these are discussed in the current section. Natural Selection. Darwin was troubled about defining his chief mechanism for adap- tation. He attempted to develop and incorporate multiple theories but none could explain how species change over time. It was not until Darwin used principles from Thomas Malthus’s (1798) work that he developed the concept of natural selection. Malthus (1798) observed that species exist in numbers that are unsustainable. Said an- other way, species have more offspring than can be supported by the environment. However, this serves a purpose. It filters out unfavorable variations within the population while pre- serving favorable ones. This process encapsulates natural selection. And Darwin laid out 3 key components to this concept: variation, inheritance, and selection (Buss, 2008). The first component, variation, refers to how members within a species differ from one another. For example, humans range in height, eye color, musculature, athletic prowess, and sociability to name a few. This variation gives evolution options from which to select. It is the “raw material" of adaptation. Only some of the variations are passed down — or inherited. When an individual repro- duces, s/he gives their offspring their genes and traits, and their offspring give these genes to the next generation, and so on. Only genetic traits are passed down. Attributes caused by environmental events — such as severe head trauma — are not. Not all heritable variants are created equal. The final component to Darwin’s mechanism is selection. It is this component that allows for changes to occur. Individuals who are in environments where their variants are advantageous are more likely to survive and reproduce

28 when compared to an individual with fewer of these variants in the same environment. Due to this, advantageous genes are passed down from one generation to the next, while disadvantageous ones are filtered and eventually eliminated (or at least significantly reduced). A classic example of the process of natural selection in humans is the bubonic plague of the 14th Century. The disease began in Asia and arrived in Europe via the Silk Road. Once it arrived in Europe,9 it claimed the lives of a quarter of the continent’s population (Diamond, 2017). While most individuals who contracted the disease died, a number of individuals survived. Their immune system was able to fight off the disease and prevent death. Because this disease culled the population, those with the advantageous variant of immune system were more likely to survive the Black Death and pass on their immunological heritage to their offspring. Evidence of this can be seen in today’s work on the human genome. Laayouni and colleagues (2014) identified a Toll-like receptor (TLR-1) that was shaped by and came to prominence during the 14th Century. This variation was also found to be present in Rroma populations (who would have been present in Europe at the time of the plague) but not in northwest Indian populations (where the Rroma originally resided). This suggests a filtering of weaker variants and a proliferation of bubonic resistant immune systems. This strengthening of the immune system in European populations is one theorized factor that led to Europe’s colonization of the New World (Diamond, 2017). Sexual Selection. While Darwin’s natural selection is a robust mechanism that can explain a large amount of the adaptations we can observe, it cannot sufficiently explain all of them on its own. Darwin recognized this when he observed male peacocks. This land- dwelling bird has large plumage that, as Darwin noted, serves no purpose of survival. If anything, the plumage makes it more difficult for the peacock to evade predators, thereby making it a disadvantageous adaptation that should have been bred out. This fact actually caused Darwin so much frustration that he remarked “The sight of a feather in a peacock’s tail, whenever I gaze at it makes me sick!" (Cronin, 1991, p. 113).

9It should be noted that there were substantial casualties throughout Asia and parts of Africa as well. Millions of lives were lost in Russia, China, and Persia to the bubonic plague.

29 After much deliberation regarding the shortcoming of his theory of natural selection, Darwin began to come across other observations. He noticed that males and females generally differ in size, strength, and other attributes. Through his frustration and additional insights, Darwin began to create a secondary theory that worked in tandem with the process of natural selection. That new theory was sexual selection (Buss, 2008). Contrasted with natural selection — which is used to explain how traits that are ad- vantageous to survival of an individual are passed down to future generations — the new theory of sexual selection focuses on what traits provide individuals with an advantage in reproduction. This was an insightful delineation. In order to pass down one’s genes, it is necessary for an individual to survive long enough to reach sexual maturation. However, merely surviving, while necessary, is not sufficient. In addition to surviving, an organism must also attract, secure, and successfully copulate with a mate. The set of skills for these two mechanisms are related, but not identical. Darwin envisioned the process of sexual selection to be composed of two mechanisms: intrasexual competition and intersexual competition. Intrasexual competition refers to the struggle between members of the same sex to secure a mate. Examples of this can be seen all over the animal kingdom, but a clear example of how males compete with one another can be seen among Guinea baboons. This species of baboon shows great differences between the sexes. Male Guinea baboons may be up to 3 times larger than females. Their canines are also longer and denser . Guniea baboons’ social hierarchy is also a one-male unit. Males directly (through acts of physical dominance) compete with one another to establish multiple female mates and hold exclusive breeding rights to them (Dixson, 2012). The biggest and fiercest baboon wins the competition and passes down his genes to the next generation. Contrast this to a different species of baboon, the Kinda. Males and females are closer to the same size (males are generally 1/3 larger). Their social hierarchy is more communal with multiple males and females living together. While there is less physical violence among this species, males still compete with one another, however, they do so indirectly. Recent

30 work has noted that Kinda baboons have an annual spike in births. This means that females have a narrow time at which they are able to breed and that most of the females are fertile at the same time. Due to all females being available to reproduce and a social structure where multiple males are in close proximity, no male can hope to dominate and monopolize multiple females. Therefore, the way that Kinda baboons compete is indirectly, meaning that they copulate with as many females as possible and the strongest sperm succeeds in impregnation or males who are best able to assess the fertility of a female and act are the most likely to win the competition (Petersdorf, Weyher, Kamilar, Dubuc, & Higham, 2019). The other pathway through which sexual selection affects a species is through intersexual selection. This process takes place when members of one sex — generally, as a group — prefer a certain attribute in a mate from the other sex. Those with the preferred attribute will find mates. Those without it, will be less likely to find and keep a mate. Darwin’s bemoaned peacock is a perfect example of this. Female peacocks prefer a mate with large decorative plumage. Therefore, males with large, decorative feathers are more likely to attract females and have offspring. While developing this theory, Darwin (1859) observed that females are more “choosy" about who they mate with than are males — at least for mammals. He named this concept female choice. It is hypothesized that females are choosier due to the unequal investment needed for offspring between the sexes. For females, having a child costs enormous physical, emotional, and caloric effort and they are also constrained by the timeframe in which they can conceive. Furthermore, before modern medicine, it was not uncommon for women to die during childbirth. Men, contrarily, need only invest moments of their time to impregnate a woman. After this act, nothing from the male is needed. Males also have a wider window to father a child (into his 70s for humans). These reasons affect the mating habits of females and influence them to be choosier about who they mate with. Clarifications on Evolutionary Theory. There is a key point of evolutionary theory that many individuals misinterpret: that the evolutionary process is purposiveness. (Buss,

31 2008). Selection is not intentional. In other words, evolution — and its mechanistic forces — is not purposive, but rather, probabilistic. Male peacocks did not think, “Chicks (pun intended) dig guys with plumes" and then grew ornate feathers to attract a mate. What happened was female peacocks selected males with more feathers as mates, and the males with more ornate plumage had a higher probability of attracting a mate and passing on his genes. This male’s offspring would also have decorative feathers, increasing their chances of having offspring of their own. Furthermore, females did not consciously choose males with ornate feathers. The feathers subconsciously represented fitness in a mate, which made the male peacock attractive, or just found the feathers themselves to be attractive. Regardless of the reason, the plumage influenced the females to mate with the described males. Selection merely filters out the variants that are disadvantageous or non-indicative of fitness while making the advantageous and attractive ones more common in the species. It should be emphasized here that since evolution is not purposive, it is only a description of what is or was and a framework used to explain why things are the way that they are. It is not a philosophical or moralistic framework. Using evolutionary principles as a justification for the rightness or wrongness of actions is a misapplication of the theory. One cannot jump from “is" to “ought" without a guiding, intentional force or principle, and evolution does not provide either of these.10 This attribute of the theory becomes more important when the discussion jumps to evolutionary psychology, gender roles, and aggressive behavior. Independent from the conclusions that can be drawn from evolutionary theory, it is largely natural and sexual selection that drive adaptations.11 Selection processes have shaped every species on the planet into what it is today. Traditionally, these mechanisms have been applied to physiological traits and structures and have been largely used in biology. However, recent efforts in the behavioral sciences have incorporated these mechanisms into its models of 10While this is the perspective of this work because its use of evolutionary theory is solely descriptive, it is worth noting that other works, such as Harris (2011) argue that evolutionary theory can give us a moral foundation. 11Other forces such as and take place as well. While they are powerful forces, they are not the driving forces behind evolutionary adaptation like natural and sexual selection are.

32 behavioral propensities. The following section discusses these developments. Evolution and Psychology

Scientific progress has a history of merging seemingly unrelated fields together into a unified idea. For centuries, the terrestrial and extraterrestrial bodies were seen as wholly different entities and had no parallels between them. However, when Newton’s work and conceptualization of gravity asserted that anything with mass — which includes both earthly and heavenly bodies — generates and is affected by gravity, there was a scientific revolution that blended physics with astronomy. Darwin’s theory of evolution has created a similar — and just as powerful — unifying force within the physical sciences. His theory, which started in biology, has been incorporated into chemistry, medicine, virology, anthropology, information sciences, and neurology, to name a few. Unfortunately, the behavioral sciences have lagged behind this new paradigm by holding fast to assumptions long-since abandoned by evolutionarily-informed researchers. However, work completed by scholars such as George Williams (1966), (1976), and (1987, 1992), and (1994, 2002) have laid out how evolutionary theory can inform researchers — and society at large — on the root causes of why humans act and think the way that they do. While criminology has not witnessed such integration, there have been blips of evolu- tion’s addition in the early formation of criminological theory. For example, the Italian criminologist, (1884), sought to identify the “criminal man" via physiologi- cal features. The most notable component of his theory was that criminals were evolutionary throwbacks, and as such, would have physical features more closely aligned with our more “primitive" relatives (e.g. apes). While Lombroso’s theories have been largely invalidated, his work represents one of the first attempts to incorporate evolutionary ideas into the behav- ioral sciences (Durrant & Ward, 2015). One of the main causes of his ideas’ failures is likely the lack of empirical refinement of evolutionary theory. Another is his misapplication of evolutionary processes. Luckily, recent advancements in neurology, genetics, and computer

33 processing power have allowed for an explosion of discoveries and support for evolution’s correctly specified explanatory power of behavior. There are a multitude of sub-disciplines that are in the process of integrating evolutionary concepts into the behavioral sciences.12 This dissertation focuses on evolutionary psychology, which is, according to Tooby and Cosmides (2015), “the long-forestalled scientific attempt to assemble out of the disjointed, fragmentary, and mutually contradictory human disciplines a single, logically integrated research framework for the psychological, social, and behavioral sciences — a framework that not only incorporates the evolutionary sciences and information theory on a full and equal basis, but that systematically works out all the revisions in existing belief and research practice that such a synthesis requires" (p. 3). In other words, evolutionary psychology seeks to take supported, validated findings from evolutionary theory and apply those findings to the behavioral sciences. This will, in turn, create a unified, consistent framework across the behavioral sciences — similar to the one in the physical sciences. Environment of Evolutionary Adaptedness. The first step to incorporating evo- lutionary theory (which is the ultimate, distal explanation for why organisms are the way that they are) with the behavioral sciences’ explanations of current phenomenon (the prox- imate, contemporary explanation) is to first consider the environment in which adaptations developed. This environment has been coined the Environment of Evolutionary Adaptedness (EEA). The EEA is not a specific place or time. It represents a specific environment that placed pressures on a species. The species either adapted to these pressures or died out (Buss, 2015). So the EEA can be thought of as the environment that a specific adaptation developed in and is “tuned" for. For humans, the condition for many of our adaptations are hunter-gatherer societies in the Pleistocene epoch. Pleistocene Epoch. The Pleistocene is the geological epoch immediately preceding the epoch in which we are in currently (the Holocene). Geologists have estimated that the

12Review Buss (2015) for a summary of the integration of evolution into the behavioral sciences.

34 Pleistocene lasted from about 2,588,000 to 11,700 years ago and ended with the decline of the final Ice Age. The period was characterized by its rapid climactic changes and glacial activity. The glaciers during this time would advance and retreat like a miles-wide, slow- moving hand saw. The glaciers carved the landscape, forming waterways, lakes, and paths between mountains. What caused this glacial activity was rapid weather changes.13 In addition to glacial movement, this epoch also saw increased volcanic activity and changes in water levels. These weather changes and geological shifts not only affected the huge ice sheets that moved along the continents of North America, Europe, and Asia, but also affected habitats of the flora and fauna of the time. Glacial periods saw species move closer to the equator, while interglacial periods were characterized by spreading and exploring of plants and animals toward the poles (Willoughby, 2006). It is during this period of unpredictable weather patterns that humans — and many other hominids — first appeared. The accepted dawn of our species occurred approximately 200 thousand years ago (kya). However, a recent discovery in Morocco found Homo sapien fossils that were roughly 300,000 years old (Hublin et al., 2017; Richter et al., 2017). If our species dates back 300kya and the Plesitocene epoch did not end until 11,700 years ago, that means our species has spent over 95% of its existence in the unpredictable Pleistocene landscape. And it is this landscape that many of the adaptations with us today developed. Therefore, most traits or adaptations that we observe in humans today should have had a purpose that was advantageous during the Pleistocene epoch. Before continuing this discussion, it should be noted that while our species, H sapien, carries adaptations from this era, some traits and instincts of H sapien developed much earlier. For instance, humans are mammals. Therefore, many common traits that mammals share (e.g. females nursing their young and a high level of ) developed well before our species existed. Similarly, humans’ shared traits with our primate cousins 13It is uncertain what caused these weather changes. Some climatologists argue sea patterns shifted while others argue that the Earth’s orbit around the Sun was more elliptical-shaped during this period. Regardless of the cause, the Earth did experience high glacial activity (Willoughby, 2006).

35 (e.g. our opposable thumbs and and an instinctual detection system of snakes (Langeslag & van Strien, 2018)) were brought about by forces long before the Pleistocene and carried down to all primate descendants. And while the geophysical factors discussed in this section were powerful and affected our species’ evolution greatly, they are not the only environmental factors that shaped humanity. Another source, potentially more important than the factors discussed in this section, is social factors. It will be discussed in the next section. Hunter-Gatherer Society. The most effective way for humans to survive during this period were in hunter-gatherer communities. Based on archaeological sites and observations of contemporary hunter-gatherers, it is hypothesized that our ancestors lived in nomadic groups, numbering in the dozens and composed of multiple families. Gender roles were strict. Men warred, protected, hunted, and sometimes scavenged. Women foraged for fruits, berries, and nuts and were the primary caregivers for offspring (Willoughby, 2006). It was in this social structure and environment that natural and sexual selection were filtering disadvantageous traits and selecting ones that helped individuals survive and reproduce. One of the chief sources of pressure for selection was found in the social structure described above. For a group of H sapiens to survive during the Pleistocene, all members of the group needed to work together. Because our ancestors during the Pleistocene had not yet developed agricultural technologies,14 all food was derived from scavenging and hunting.15 Meat was not always a guarantee at this time (and neither was a surplus of scavenged goods). A hunter could go out for weeks without success. This unpredictability combined with the need for meat caused members of communities to develop altruism and social exchange (Buss, 2008; Wright, 1994). Members of the community would share their kills with other, less-fortunate hunters — even non-kin. This was done with the knowledge that the successful hunter today 14It is theorized that agriculture was not possible before the end of the Pleistocene as the average temper- atures were lower and the soil harder during this time. After the earth began to thaw and humans refined tools, agriculture was possible (Durrant & Ward, 2015). 15Even hunting came somewhat later in our development. Tools/weapons that would have been capable of taking down large, substantial game were not developed until the late Middle to Late Pleistocene. Before this, humans acquired meat from smaller animals or scavenging a kill from a large predator (Durrant & Ward, 2015).

36 may be unsuccessful tomorrow. In Pinker’s words, successful hunters were “storing" their excess meat in their neighbors for a rainy day (2009). Evolutionists argue that pressures such as these selected for altruistic tendencies. Individuals who were more likely to share with the group were also more likely to have those good graces reciprocated when s/he hit hard times. Furthermore, someone who shared their good fortune would be more likely to be invited on group hunts and share in the bounty of larger kills. In short, altruistic behavior in these groups was advantageous for the survival of the individual and, by proxy, their offspring (Wright, 1994).16 However, as we know, not every human is honest and/or altruistic. Chapter 2 dis- cussed the psychological construct of Machiavellian (Mach) actors. These individuals have attributes that increase the likelihood that they will attempt to take advantage of others and get away with it. While the psychological work discussed in the referenced section explains how Mach attributes manifest in the present day, neurological research put into an evolu- tionary perspective can help us understand how Mach actors had an evolutionary advantage in the EEA. Bereczkei (2018) conceptualized the idea of Machiavellianism being a Darwinian algo- rithm. This algorithm is a heuristic, or style of thinking, that evolved to allow high-Mach actors “to evaluate their social environment and make predictions about future reward in a basically risky and unpredictable situation" (p. 40). Five key mental algorithms have been gleaned to describe how Mach was advantageous in the Pleistocene. They are reward-seeking, monitoring, inhibition of cooperative impulses, task orientation, and flexibility. Taking all of these algorithms together, when put in an unpredictable situation, high- Mach actors will seek to maximize their rewards from others with the least effort. These actors’ brains are more sensitive to both anticipating and receiving rewards (Bereczkei, Deak, Papp, Perlaki, & Orsi, 2013)17 while simultaneously evaluating the risks in a given situation

16There are other contributing forces to the development of this adaptation. Some researchers (Dawkins, 2016; Hamilton, 1964) cite kin-relationships (a la inclusive fitness) and in-group loyalties as sources for altruism as well. 17It should be noted that, while informative and well done, some of these research projects in this paragraph

37 and monitoring/adapting their behavior based on those evaluations. Neurological evidence of this process is suggested in research as their thalamus is more active in these scenarios. (Czibor & Bereczkei, 2012). In addition to evaluative algorithms, those who are high-Mach have brain activity suggesting extraordinary focus. For example, recent work has shown that when engaging in a bargaining game where reciprocity is expected by participants, high-Mach actors show an increased level of activity in the dorsolateral prefrontal cortex (DLPFC) (Bereczkei et al., 2015). This site of the brain is known for engaging when actors have a conflict between personal gain and societal expectations (Sanfey, Rilling, Aronson, Nystrom, & Cohen, 2003). Essentially, those with a more active DLPFC in these situations make a more utilitarian decision than an emotional- or person-driven one. And apart from just removing emotion, Bereczkei et al. (2013) found increased activity among high-Mach actors in the middle frontal gyrus when participating in Trust Game. This suggests that Machs’ brains almost automatically filter out any information or expectation that is not directly relevant to the goal at hand. The fact that some individuals seem to ignore the factors that developed altruism in our species may, at first, seem counter to evolutionary theory. However, the final algorithm, flexibility, shows how that is not the case. Potentially the most important algorithm of high-Mach actors is flexibility. It has been long held among social psychologists that high-Mach individuals score highly on bargaining games. They are adept at these activities because they are able to shift from selfish to cooperative strategies almost seamlessly. What determines their shift is environmental con- ditions of risk and reward (see Christie and Geis (1970) for a review). Recently, neurological work has added to the support of their flexibility. Spitzer, Fischbacher, Herrnberger, Grön, and Fehr (2007) found that Mach scores were positively associated with activity in the left anterior orbiotfrontal cortex. This area of the brain is shown to evaluate stimuli. Behavioral measures were consistent with this brain activity as participants were most likely have significant flaws. For example, Bereczkei et al. (2013) had a sample size of 15 (8 low-Mach and 7 high- Mach). While these findings are not without limitations and need further exploration, they are consistent with previous literature’s assumptions.

38 to take risks — and profit the most — in the condition with no punishment while being the most likely to escape punishment in the 3rd party punishment condition. It is this ability to alter one’s behavior and be sensitive to potential — and serious — if caught being dishonest that allowed the genes of Machs to be passed down in a communal society. Having unpredictable behavior would make it difficult for others to conclude if the Mach was taken advantage of a situation or not. Taken together, it seems that a high-Mach actor is “hard-wired" to maximize reward while avoiding punishment. It is this ultimate explanation that explains the origin of the behavior we see today. This heuristic seems to be evolutionary advantageous, as one who can manipulate others and maximize resources is most likely to attract a mate and have her/his offspring survive and procreate — as long as the actor is not caught. However, if a hunter-gatherer society is full of high-Mach actors, then no one can trust anyone else. This mistrust may lead to the community falling apart and losing its advantage of survivability for members. Because of this kind of limit on high-Mach actors due to diminishing returns, scholars have argued that the process of frequency-dependent selection may be responsible for this trend(Jones & Paulhus, 2010; Wilson, Near, & Miller, 1996). Essentially the number of high-Mach actors may increase or decrease depending on environmental pressures and the social make-up. Deception may be advantageous, but so is detecting deception. Ultimately, evolution favors cognitive ability and versatility. If a community is composed of individuals wise to a high-Mach’s deceptions, s/he has lost their advantage.18 One other interesting perspective regarding Machiavellianism and evolution pertains to who we as humans determine who is a successful leader. In the book The Wisdom of Psychopaths, Dutton (2012) discusses the evolutionary benefits of being a psychopath. And as discussed in Chapter 2, psychopathy’s attributes overlap greatly with Machiavellianism. The author makes reference to various points in history where leaders remove emotional 18Scholars refer to this process as the (Ridley, 1994). It is the idea that one trait has an advantage (i.e. deception) and another trait to counter it proliferates (i.e. deception detection). This process may be partially responsible for modern-day humans’ cognitive capacities as both of these processes require high-level cognitive ability (Whiten, 1997).

39 attachment from situations to come to the best solution for their charges — and, by effect, themselves. One classic example is one of Winston Churchill who, allegedly, in World War II decided not to warn the city of Coventry of a Luftwaffe blitz that destroyed the city. Had he decided to warn the city, residents may have been able to evacuate. However, this would have tipped off German forces that the British had been able to decipher their radio communications. For the sake of protecting the intelligence — and in his estimation, to save more lives — Churchill decided to sacrifice the people of Coventry. Dutton (2012) argues that it is this type of thinking — emotionally detached thinking — that aided an individual in the Pleistocene to rise in the hierarchy and attain status. Once that individual had status and influence, s/he was able to use her/his style of thinking to better lead the group. Evidence of this style of thinking being beneficial to a leader can be seen in politicians, as discussed for Winston Churchill, and with the relatively high prevalence of psychopaths in high-ranking positions in corporations compared to the population at large (Hare, Babiak, McLaren, & McLaren, 2011). This selection process shaped our species’ psyche for millennia during the Pleistocene. It is not only responsible for our aptitude for language, the development of altruism, and the mental arms race, but every proclivity that we have — for example, our sense of right and wrong (Wright, 1994) and our preference for sugary and salty treats (Pinker, 2002). It is also responsible for our mate selection practices and those practices’ relation to status. Status’s Role.

Evolution and Status. Living in pockets of hunter-gatherer communities helped de- velop much more than altruistic tendencies in our species. One relevant development — and one of the most powerful in our species’ history — has to do with mating habits. During the Pleistocene, humans developed a polygynous mating system (Daly & Wilson, 1988).19

19Humans have shown that a wide array of environmental factors affect mating habits. Short-term focus, longer-term focus, sex-ratio, and even presence of parasites have played a role in mate selection and the social structure for mating and child-rearing. Humans are quite versatile and adaptable in this regard (Buss & Shackelford, 2008). However, it is theorized that polygyny was the order of the day during the Pleistocene epoch.

40 In other words, one male would have several female mates. The reason for this system was pragmatic. Given enough mates, males can sire dozens — if not hundreds — of offspring per year and can produce offspring late into their lives. Females, however, have a limited window of reproduction and are also limited by the number of pregnancies they can have — ≈1 per year. This essentially made females more important in reproductive terms. Furthermore, due to the physical differences and accepted gender roles of the time, men participated in much riskier tasks than did women (Willoughby, 2006). Hunting had a much higher risk of serious injury or death than did gathering. Males were also tasked with protecting their homes and families from predators or other groups of hominids. These occupational hazards caused the group to have more females than males. And due to both males’ ability to sire multiple offspring and the unpredictability and risk of having children for females during this time, polygyny was the most effective strategy to have healthy, viable offspring (Buss, 2015).20 Because of these mating patterns, males were in intra-sexual competition for mates, while females were in intra-sexual competition for the best mate. Females’ higher probability of having children and passing on their genes combined with the physical cost and amount of time it takes to birth and raise a child caused our female ancestors to be more choosy re- garding their mates — like many other mammals as Darwin (1859) observed. Women during this time (and currently) had to choose which mate would give them and their offspring the best chance of survival and the most advantages over their peers (Betzig, 1986).21 Evidence of this can be seen today. Buss and Shackelford (2008) measured women’s expectations of a prospective mate’s attributes along four different dimensions: having good genes, be- 20There is a debate as to what originally caused the sex differences in mating habits. Evolutionary psychologists argue that the differences come from gradual, genetic change caused by environmental factors of the Pleistocene epoch (Buss, 1989). Social psychologists contend that it was the division of labor that caused the sex differences in mate preference (Eagly & Wood, 1999). Attempts to reconcile these two perspectives have been attempted (Schaller, 1997), but the debate has not ended. 21These adaptations were developed in a polygynous societal structure. In current times, monogamy is the normal, preferred mode of mate coupling (although some have argued with increased divorce rates, industrialized societies have developed a sort of polygamous method). This may be an example of an evolutionary malintegration where the environmental pressures that we are designed for have changed and we require adaptation to the new pressures.

41 ing a good parent, being a good partner, and having good financial prospects. What the researchers found was that attractive women (the ones who are most desirable) have the highest standards. They expect a potential mate to score highly on all four dimensions — so that their mate’s value matches their own. It is important to note that the emphasis on resource acquisition for females via their mate was paramount during the Pleistocene because of the previously discussed gender roles. Women foraged for sustenance and were the primary caregivers to children. Men hunted, warred, and competed. During this time, it was males who were able to attain resources through their works (e.g. they were the best warrior, best hunter, best fisher). And through the attainment of resources, status among peers was developed.22 Females, during this time in history, did not have the opportunity to gain resources and status in the way that males did. Therefore, her best chance to have quality offspring and make sure that they were provided for was to mate with a successful male and control his resources so that their offspring could flourish. Women’s places in the social system and reproductive process drove the selection process for humans in two ways: (1) It made women more likely to be attracted to men with status and (2) it selected for genes that made it more likely for males to have status — through both their ability and behavior. Because we are all results of our evolutionary histories, these tendencies are still with us today. For instance, Buss and Schmitt (1993) conducted a study comparing long-term and short-term mating strategies of both men and women in the United States. What they found was that women valued job prospects, acquisition of resources, and status much higher than men did. Furthermore, women valued these attributes more highly for a potential long-term mate when compared to a short-term mate. These trends suggest that women (consciously or

22Ehtnographies of contemporary hunter-gatherer societies are our best method to peering into what human society was like during the Pleistocene. These studies show that there is a clear social hierarchy where resources are most bountiful to men with status and the resources trickle down the hierarchy. Combined with historical evidence showing similar trends across continents and time periods, these studies suggest that status was not only a result of having resources, but also led to the attainment of additional goods. (Buss, 2008). In other words, those with status would get gifts from those with lower status to get in the high status individual’s good graces.

42 subconsciously) seek out males with status. This is especially true for males with whom they are most likely to reproduce. Females’ preference for status is not only present in capitalistic nations either. Research has found that females in 37 different cultures overwhelmingly rank status as a more desirable attribute than do men, suggesting that status in mates is a key factor for women (Buss, 1989). The fact that this tendency is consistently shown across cultures suggests that it is a human universal, and that this preference for status and access to resources developed with humans’ earliest ancestors (or before) and is species-wide. It also suggests that attaining status was the most effective way for our male ancestors to gain access to a number of mates and provide resources for their offspring’s survival. Adolescents and Status. One commonality between many hunter-gatherer societies was some sort of ritual or acknowledgement that a child had reached the point where they became an adult. While researchers have no direct method of observation, it is assumed that many primitive human cultures had a formal, explicit transition from adolescence to adulthood.23 This assumption is present because many traditional societies (either still present or well-documented from the past) have these ceremonies and generally take place about the time that the youth begins puberty (Schlegel & Barry, 1980). This strategy of explicitly demarcating child from adult aided in the transition and also allowed adults to give some guidance to youth. For females, it marked that they were of age to become a mother, while males’ transition denoted that they were ready to contribute to the community — and by effect gain resources. An important note in this discussion is that this rite of passage generally signified that the newly identified adult’s work was valued by adults. Much time of a hunter-gatherer’s life was spent securing and processing food. These communities did not have the time or resources to support “dead weight." Therefore, once an individual was deemed an adult, they were allowed access to and expected to participate in adult work, which contributed 23I do not discuss different types of ceremonies here as they most likely varied by the rituals and re- quirements. It is, however, theorized that they existed for a similar purpose — to differentiate child from adult.

43 to the group’s survival. This was especially important for males. Females’ value in this type of society was largely to be mothers.24 Therefore, their value was innate. Males’ value, however, was in their ability to provide resources and protection for themselves, their family, and the community. It was by attaining these skills and resources that a male could attract a mate. What is interesting about this age period is that adolescent males had not had time to attain status and resources. It was older males who had these desired qualities. This trend explains two behavioral tendencies that we see today. The first trend is that, generally speaking, females prefer men who are older than they are. There is actually a positive correlation with the amount of resources/status a male has and the age difference a female is likely to tolerate (Buss, 1989). Put simply, females prefer men who can provide and the males who are most likely to provide are older than the female. And the more resources a man has, the later in life he can find a mate. The second tendency that this trend explains is that men, in general, attempt to attain status (especially younger males (Buss, 2015)), protect their status (Wolfgang, 1958), and they are likely to use aggression to do both (Card, Stucky, Sawalani, & Little, 2008). It appears that at the age when their status and reputations are developing, males are most motivated to develop it and be attuned to its level. And while adolescents have few resources to offer a potential mate, they do have a new found ability to physically intimidate or use force against others inside or outside of the community. Combine this with a lack of experience using aggression, and it follows that an increase in aggression should be shown. This relationship between the motivation for status and an increased likelihood of using aggression explains much from Chapter 2. It gives insight to why young males are more likely to use overtly aggressive tactics than are older males or females and also explains adolescents’ sensitivity to social cues and evaluation discussed in the neurological literature. 24During the Plesitocene, this was arguably the most important role in regard to our species’ survival. Females were the primary caregivers and as such, were expected to teach the next generation the group’s roles and expectations.

44 Evolutionary Mismatch. The above conversation describes some of the “pre-loaded software" in our behavioral repertoire. This software was designed to take input from the en- vironmental cues that were common in hunter-gatherer communities during the Pleistocene, and execute the most efficient plan for an individual’s survival and reproduction. Since this software is composed of many adaptations that developed in hunter-gatherer societies, many of our instincts and proclivities work best for the past environment. Unfortunately for our brains, we no longer live in that environment. Some adaptations that would help our survival in the Pleistocene now hinder our prospects in contemporary society. This in- congruence between program and environment has been termed an evolutionary mismatch (Hawley, 2011). A classic example of an evolutionary mismatch in our species has to do with our preference for sugary foods (Pinker, 2002). During the Pleistocene epoch, food was not easy to come by. The majority of time was spent obtaining it. Environments that had a wealth of fruits and berries were of great use to our ancestors. These foods gave them lots of calories and were easy to catch (as they did not run away). Our ancestors who developed a taste for this type of sustenance had an evolutionary advantage over those who were repulsed by it. Therefore, those ancestors who gravitated toward sugary foods had an evolutionary advantage and had more successful offspring in many environments. While this preference worked well for our hunter-gatherer fore-bearers, it causes problems today. Too much sugary food can cause serious health conditions. In developed countries, sweet foods are easy to find (and cheap). All one has to do is walk into a grocery store and find the snack or dessert section. There will lay a wealth of high-caloric, sugary foods. Our preference for these types of food combined with their now ease of access has contributed to the obesity epidemic, rise in diabetes, and societal increase in heart disease (Nesse & Williams, 2012). We essentially have “stone- age minds" and use those minds to navigate the modern world (Cosmides & Tooby, 2007). Sometimes our minds get us in trouble. Another example that is more relevant and will be discussed further in Chapter 4 deals

45 with who adolescents learn from. Considering that much of the criminological literature focuses on this specific question (Gottfredson and Hirschi (1990), Sampson, Raudenbush, and Earls (1997), Shaw and McKay (1942), Sutherland (1942) to name a few) it seems like an important puzzle to solve. Harris (1995) completed an in-depth review of the literature on group socialization theory from an evolutionary perspective. In it, she spoke on the community structure while humans were in hunter-gatherer societies. At this time, because of the low number of community members, there were few cliques or segregation of individuals. If communities are large enough, humans tend to divide themselves into groups. Those groups are generally defined by age and gender. However, in small communities, there aren’t enough individuals to form such cliques (or at least form age- or gender-specific cliques). What this leads to is a situation where community members act as a cohesive group, similar to a large family (which is partially literal as everyone in these communities was distantly related to all the others by and large). Modern-day organization is at odds with the structure described above. Most youth go to middle and high school during adolescence. They are segregated from the rest of society while there. The only adult figures are teachers and administrators, who are vastly outnumbered by adolescents. In hunter-gatherer societies, because of the lack of delineation of groups and the earlier entrance into adulthood, youth may have learned from adults more in the ancient social organizations. With the segregation of youth to schools, being surrounded by their peers, they can create deviant subcultures. And as discussed in Chapter 2, teens in present day care more about their peers’ opinions than any other group’s. The process and consequences of this specific evolutionary mismatch will be discussed in more depth in the following chapter. The strategic use of aggression seems to be part of an evolutionarily adaptive system as well. Youth in the Pleistocene epoch with the tools and/or means used aggression to obtain status and resources in order to reproduce. However, most individuals today view aggressive behavior as maladaptive and its unbridled use can decrease social standing (Hawley, 2003;

46 Volk et al., 2012). Furthermore, if aggression is used to an extreme extent, youth could be charged with a crime further diminishing their ability to gain resources and attract a mate. One of the most relevant evolutionary mismatches has to do with how youth are or- ganized in society. In hunter-gatherer societies, groups were small enough to where every village member knew every other one. Adolescents (and newly labeled adults) were brought into the fold. They were included in daily tasks and expected to carry their weight. Ado- lescents, in contemporary society, are segregated. They are placed into schools where the greatest social influence comes from their peers. Furthermore, they are told that they need to finish high school (and college) before they will be allowed to obtain adult roles and em- ployment (Kanazawa & Still, 2000). In addition to this extended delay in adulthood, youth are experiencing puberty sooner than past generations have (Gluckman & Hanson, 2006; Worthman, Panter-Brick, & Worthman, 1999). Put briefly, youth during the Pleistocene accepted adult roles around the age of fourteen. Adult roles for youth now are not obtained until their mid- to late-twenties, even though they are physically adult-like at an earlier age than their hunter-gatherer ancestors. Youth are “chronological hostages of a time warp between biological age and social age" (Moffitt, 1993, p. 687). Summary

Humans have been evolving for hundreds of thousands of years, and many of our behaviors and physiological structures evolved before then. In order to survive the harsh environment of the Pleistocene Epoch, humans developed a strict social order. Our ancestors stayed in groups large enough to protect themselves but small enough to be sustainable. In these polygynous communities, men hunted, warred, and gained resources. Simultaneously, women bore and raised children while searching for food and resources. And while our social structures are different today, many of our behavioral adaptations are still made for this environment. While Chapter 2 discussed the definition of popularity and why it is so important to adolescents, this chapter lays out why these processes exist. That reason is reproduction. Furthermore, just because past and present societies are not

47 identical, does not mean that the underlying cognitive processes and drives are any different. A 16-year-old boy in a hunter-gatherer society may achieve status by mastering hunting or fishing, build an adequate shelter, and/or defending his family. These were all valuable skills that allowed for a potential mate to feel secure and give the boy status in the community. Contrast that with today’s 16-year-old boy. These skills would provide little value to his peers/potential mates. What might provide a greater impact on his status is having a new sports car. While this is not a tool for survival (and in the hands of many teen boys it may actually decrease their likelihood to survive), it is a symbol of status. Potential mates may be attracted to this as they would have access to (stylish) transportation and would be able to attend gatherings with peers. The skills or possessions are different as they are context-specific. But the underlying drives are the same. This further expands the scope of the research question gleaned from Chapter 2. By having a more accurate picture of what factors increase a current-day adolescent’s status (or centrality), we can see how evolutionary processes are at play in modern society. As stated earlier, it would be difficult for a teenager in present day to afford a new sports car on her/his own. This is due to teenagers being sequestered to high school and having few desirable and lucrative job skills. This, again, is an evolutionary mismatch. The consequences of these mismatches are encapsulated in Moffitt’s work. In her groundbreaking article, Moffitt (1993) takes the age-crime curve — one of the few consistent principles in the field of criminology — and while proposing a mechanism to describe the curve’s causes, also explains why an overwhelming majority of youth offend. Her explanation is that they experience a maturity gap brought about by the incongruence between adolescents’ adult bodies with their lack of adult roles. The following section describes her theory and shows how it fits within the frameworks previously explored.

48 Chapter 4

Moffitt and Adolescent Deviance

An interesting quality of evolutionary psychology is that it is incredibly useful for ex- plaining the long-term antecedents of a behavior ingrained in a species, but easily overlooks the short-term process for that behavior to become ingrained. While evolution can explain what purpose a trait serves or served, it many times does not consider the individual life of the species, and how that lifespan is affected by evolutionary processes. Luckily, the sub- field of developmental/life-course criminology (DLC) can be integrated into the evolutionary framework to provide a more proximate context in which the processes are working. Calls for increased attention in this subfield by John Laub (2004) and Frank Cullen (2011), combined with the work by a multitude of researchers has provided a solid foundation for integration. One of the most influential of these researchers is Terrie Moffitt due to the development of her taxonomy. Her re-conceptualization of adolescent behavior has spurred research, debate, and helped to reconceptualize how criminologists view deviance. In this section, I will discuss her taxonomy and show how this framework, along with popularity, can aid us in explaining why youth offend at such a high rates. This influential framework began with Dr. Moffitt accepting a position in New Zealand with the now world-famous research initiative called the Dunedin Multidisciplinary Health and Development Study. This study followed a cohort of almost every single child born in the New Zealand city of Dunedin between April 1, 1972 and March 31, 1973. Investigators, over time, have followed these youth who have participated in interviews, medical examinations, genomic measurements, and a host of other assessments. While this cohort was young (under the age of 10), Moffitt identified a group of youth who displayed antisocial behavior early in life. She found that neuropsychological deficits were related to this behavior and, by effect, criminal involvement. However, when this cohort entered adolescence, her results did not hold (Piquero, 2011). Upon entering adolescence, the effect of neuropsychological deficits and criminal behavior

49 became weaker. Moffitt was concerned that she would not be able to explore this relationship anymore, seeing how there was no relationship. In her own words, Moffitt stated that “The taxonomy of life-course-persistent versus adolescence-limited antisocial behavior grew out of my struggles to understand why the data betrayed me" (Piquero, 2011, p. 402). After much pondering, Moffitt concluded that there were three qualitatively different groups accounting for the change during adolescence. These three groups are discussed below. Taxonomy

Life-Course Persistent. The first of the three typologies that Moffitt discusses is the life-course persistent (LCP) group. This group — which composes 5%-8% of youth — account for roughly half of crime committed by adolescents and is defined by continuity across the lifecourse (Moffitt, 1993). Individuals who are LCP experience anti-social tendencies at an extremely early age and this behavior stays with the individual late into adulthood. One of Moffitt’s key observations was that these anti-social tendencies manifest themselves differently depending on the life stage of the individual. For instance, toddlers will kick, bite, and scream (moreso than an average terrible two-year-old), children will hit and lie, teenagers will smoke, drink, and become violent, and adulthood sees further escalation of criminal behavior (Moffitt, 1993) Because these anti-social behaviors manifest themselves at an early age, it follows that the cause of these behaviors first presents itself around the time of birth or in utero. Moffitt’s (1993) hypothesized key cause of these behaviors is neuropsychological deficits (NPDs). This term was carefully concocted. Moffitt (1993) wanted to emphasize “...the extent to which anatomical structures and psychological processes within the nervous system influence psy- chological characteristics such as temperament, behavioral development, cognitive abilities, or all three" (p. 681). Combining neuro and psychological best encapsulated this concept. Some of the risk factors that can influence NPDs include prenatal care (e.g. poor nutri- tion, maternal smoking) and postnatal care (e.g. brain injury). Deficits such as these can affect a child’s psychological qualities that are important for development, such as verbal

50 ability, impulse control, and executive functioning. This process is similar to the one laid out by Gottfredson and Hirschi (1990) that they labeled contemporary continuity. Basically, it refers to the continuity of traits throughout the lifecourse and how the same constellation of traits can get individuals in trouble across different developmental stages. The effect of these neuropsychological deficits is far reaching. Their manifestation causes low cognitive ability, low behavior inhibition, and high irritability and these qualities put youth with deficits in difficult social situations. These traits are also genetically influenced (see Beaver, 2016; Polderman et al., 2015 Wright, Tibbetts, and Daigle, 2014) which increases the likelihood that the parents of these youth with also have NPDs. So not only are these children difficult children to manage, the adults managing them (parents) likely share similar traits. This creates a parenting environment that has the potential to exacerbate the genetic predispositions (Caspi & Moffitt, 1995). Add to this that those with NPDs are more likely to reside in an adverse neighborhood and it is easy to see the difficulty that these youth face in life. Because of all of the risk factors and behavioral tendencies discussed above, youth with NPDs generally find themselves ostracized from their peers. Children who hit, scream, and manipulate are not very likely to make friends. More likely is for these children to be labeled as the “bad kid" in class. This trend detrimentally affects youth with NPDs because they miss out on experiencing pro-social influences and cannot “practice conventional social skills" (Moffitt, 1993, p. 684). The absence of these experiences can throw off these youth’s developmental progress. Combine this lack of social support with the underlying traits that they possess and these youth are more likely to become ensnared by their behavior. These youth are more likely to drop out of school, become pregnant (impregnate) during adolescence, and/or abuse alcohol and drugs (Moffitt, 1993). Youth who go through these experiences can carry their effects well into adulthood. This is a process called cumulative continuity where past events limit individuals’ opportunities for change down the road. With no social support, a bad reputation, and underlying traits that are unsavory in mainstream

51 society, LCP youth are likely to become trapped in the cycle of drug abuse, domestic violence, joblessness, and homelessness (Caspi & Moffitt, 1995). Adolescence-Limited. While LCP youth are defined by their continuity throughout the lifecourse, adolescence-limited (AL) youth’s defining characteristic is change. This group, which the vast majority of youth are a member of, shows no abnormal level of anti-social behaviors during childhood. Contrary to LCPs, ALs begin to deviate when they start pu- berty. The classic work completed by David Farrington (1986) summarized the prevalence — the number of offenders in a given group — of youthful offenders. In his review, Far- rington found that the prevalence rate of antisocial youth went up from 5% at age 11 to 32% for 15-year-olds. By the age of 18, 93% of boys admitted to committing some form of delinquency25. The prevalence of this behavior at age 18 suggests that this behavior is a normal process during development. The pattern of increasing prevalence may also be related to the time at which individuals begin going through maturity as well — those who entered the adolescence-limited group sooner, generally, began puberty at an earlier age than late-comers to the AL group (Barnes & Beaver, 2010; Barnes, Beaver, & Boutwell, 2011; Galambos, Barker, & Tilton-Weaver, 2003). The largest challenge for Moffitt to explain the antecedents of offending for this group was due to their complexity. An explanation had to consider four key factors: “modal onset in early adolescence, recovery by young adulthood, widespread prevalence, and lack of continuity" (Moffitt, 1993, p. 686). Her elegant and simple explanation was that youth are trapped in a maturity gap.26,27 Modern society’s structure is one that puts youth in a precarious situation. Youth are physiologically and sexually mature but socially cannot assume adult roles. This juxtapo- 25The type of offense that was committed depends on the age. Generally, as age increases, property crime decreases and substance-related increase (Farrington, 1986). 26What Moffitt explains regarding the maturity gap mainly applies to Western culture. Developing coun- tries and Eastern nations generally do not segment their youth as much as Western nations do, thereby exacerbating the maturity gap (Moffitt, 1993). 27To the author’s knowledge, the only tests of Moffitt’s assertions have been in North America and Europe with the one exception occurring in Brazil. This study, conducted by Oliveira Dias, Lisboa, Koller, and DeSousa (2011), did not find any significant relationships between theoretical variables relevant there.

52 sition is a result of improved nutrition that initiates puberty and sexual maturation earlier in life compared to previous generations and also modernization of work (Toppari & Juul, 2010). Youth are required to delay entering the workforce due to demands of extended edu- cation and training. In essence, youth are left in a vacuum where they are compelled to want adult roles and responsibilities, but are not given the opportunity to obtain them (Moffitt, 1993). Furthermore, almost any work a youth can do is seen as remedial and cannot con- tribute to society in the same way that an adult’s labors can. It is at this point that we see the direct connection between Moffitt’s work and the discussion in Chapter 3. Due to the current social and financial setup in today’s society, adolescents experience an evolutionary mismatch in the form of Moffitt’s maturity gap. While Moffitt’s (1993) application of a maturity gap brought the idea widespread atten- tion, it was first introduced by Matza (1967). In his work, Matza described the concept of drift. He viewed drift as a relatively normal phenomenon observed during maturation — when youth are given more freedom. Adoelscents are tethered to conformity but drift away into criminality on occasion. Matza (1967) argued that the drifting of adolescents was due to two forces: preparation and desperation. Preparation referred to a youth observing others engaging in exciting behaviors, then the youth realizing s/he can also participate and poten- tially get away with it. Desperation refers to a sense of a lack of individuality or autonomy — similar to Moffitt’s (1993) maturity gap. Youth commit crime in order to retain their individuality. Matza (1967) also acknowledged that adolescents committed crime frequently during the early stages of the maturational process until it subsisted when the youth reaches adulthood. During the transitional phase, deviance is seen as exciting and interaction with the opposite sex is paramount (Hagan, 1991). The novel and temporary nature of deviance for most youth can also be seen in Campbell (1969). In this work, Campbell argued that while youth culture is deviant in nature, they are only “playing at" deviance as they are just trying to fit in with their peers while asserting their individuality. Other works — namely Hagan (1991,

53 1997) — delivered similar findings for these adolescent offending trends. Moffitt (1993) posits that youth begin going through puberty during middle school. At the same time, these youth are in search of a way to assert their autonomy and assume more adult-like roles28. Youth quickly figure out that societal structure will not allow them the ability to obtain this status of adulthood for quite some time. This realization coincides with their graduation into high school where older youth who have already realized this situation and have begun to assert their autonomy by deviating from the role that adult authority figures expect of them. To them, showing maturity is having sex, doing drugs, breaking curfew, and stealing desirable goods to show adults that they have every capability that an adult has (Caspi & Moffitt, 1995). It is at this point when youth begin to utilize social mimicry. As explained earlier, life-course-persistent (LCP) youth have been at the game of deviance and antisocial behavior long before adolescence. Because AL youth want more adult roles and wish to assert their autonomy, they view LCP youth as role models of sorts. These youth have been assuming adult behaviors — e.g. drinking alcohol, using drugs, having sex — for years. Therefore, the behavior that originally ostracized LCPs is now seen as appropriate and desired by both LCP and AL youth. LCPs, and their pseudo-adult roles, become magnets for ALs (Young, 2014). This shift in perspective causes LCPs to move from the periphery of the social network to a more central figure29. As youth begin to realize that they can obtain adult roles with LCP youth showing the way, more and more ALs join them until they reach critical mass in late adolescence. Once the maturity gap dissipates — when youth become legal adults and can begin to assume their coveted adult roles — ALs will begin to desist from crime and become law-abiding members of society (Moffitt, 1993). AL youth who do not desist normally, Moffitt (1993) argued, continue offending because they become ensnared by the consequences of their actions during adolescence. Examples of 28See Allen and Loeb (2015) for a discussion on autonomy in youth 29This portion of Moffitt’s theory is known as the“role magnet" hypothesis and is discussed later in this chapter.

54 these snares can be exposure to the justice system, dropping out of school, or having a child during adolescence. Abstainers. But what about youth who abstain from deviance completely? While this group is rare, they are not of insignificant size. Because this group does not offend at all, they do not have neuropsychological deficits, like LCPs. Therefore, determining how they differ from adolescence-limited (AL) youth is the key to explaining this group. And the two causes of AL offending are the maturity gap and deviant role models. Moffitt (1993) argued that the reason abstainers do not offend is because they do not experience the maturity gap. Her two proposed ways that this happen were that abstainers either start puberty later than their peers (and therefore do not have as severe of a physi- ological mismatch with their place in society) or that they assume adult roles sooner than their peers. An example of a youth assuming adult roles early could be a boy whose parents farm. Most likely, his parents dictate what he does with his time moreso than other youth as he is needed on the farm to work. Because he started working at a younger age, he has already assumed the same roles as his father and does not experience a gap.30 Another reason for the lack of deviating has to do with opportunity to offend/learn to offend. Moffitt (1993) noted that youth who live in urban areas have a greater opportunity to offend than do youth in rural communities. This trend is largely due to urban youth’s access to public transportation and public places to have unsupervised time with friends (Sampson & Groves, 1989). The amount of unsupervised time with peers gives youth a chance to learn from one another. And according to Osgood’s (1996) work, groups of youth left to their own devices will probably commit crime. One other explanation for abstainers briefly mentioned by Moffitt could be personal attributes. LCPs display continuity across the life-course. Moffitt (1993) argued that this was due to the NPDs. The theorized causes of NPDs are myriad. But recalling the evolutionary 30The most interesting part about this example is that this agrarian family structure is closer to what our hunter-gatherer ancestors experienced than to our contemporary society. Because of this, youth with a similar home life to the hypothetical example may have a less severe experience with an evolutionary mismatch.

55 discussion in the previous chapter regarding variation in a species could also give some insight. An adaptable species is one that varies.31 If there is variation, then selection has room within which to work — if everyone is the same, selection has nothing to select from. While LCPs have continuity in offending, abstainers have continuity in not offending. Perhaps these two groups are just extremes on a spectrum of variants and represent the bookends of the attribute. An additional effect could be their personal characteristics that keep them from offending that also limit their opportunity to be exposed to deviant behavior. Because there is a deviant subculture in adolescence (Osgood et al., 1996), those who commit the right crimes in the right frequency can gain status. Those who do not are ostracized. Due to their lack of offending, abstainers may be cut out of the social scene and will not get invited to get-togethers. Moffitt’s Taxonomy and Popularity

While there has been quite a bit of research testing the validity of Moffitt’s (1993) theory — see Lilly et al. (2018), Kubrin, Stucky, and Krohn (2009), and Moffitt (2006) for a review — it is interesting that the research regarding social status’s processes and their relationship with deviance is quite scant (DeLisi & Piquero, 2011). One way that this gap in the research can be explored is by applying the construct of popularity — as defined in Chapter 2 — to Moffitt’s framework. Luckily, many of her theoretical claims draw parallels with the popularity literature. The overlap between popularity and her ideas of autonomy attainment and the magnetism of LCPs are discussed below. Autonomy. Autonomy, in Moffitt’s paradigm, is a quality that is desired by youth dur- ing adolescence. It is something they wish to assert and develop in order to display their independence. Based on the discussion from the previous chapter, this drive to become inde- pendent seems to be ingrained in our species. The psychological literature has also explored the concept of autonomy and its relationship with popularity. Findings here have supported 31And humans are one of the most adaptable mammals as we have been able to survive in almost every environment on the planet.

56 the idea that association with delinquent peer groups can lead to many kinds of negative consequences — discussed more in Chapter 5 — but association with peers is a necessary part of healthy development. “As difficult as it is for teenagers to live with the challenges and dangers of peer relationships, they cannot live well without them either" (Allen & Loeb, 2015, p. 2). Youth who do not establish friendship networks during adolescence fair worse in friendships, romantic involvement, perception of self-worth, and mental health — specif- ically, they are more likely to suffer with bouts of depression (Allen & Allen, 2009; Allen, Schad, Oudekerk, & Chango, 2014; Cillessen & Rose, 2005). This vein of research views the peer-adolescent dilemma to be a result of an adolescent subculture in direct contrast to the values of mainstream, adult culture (Allen et al., 2014). Due to the support that popularity is associated with problem behaviors such as aggression, substance abuse, underage sex, poor academics, and delinquency, it follows that members of that group are largely supportive of those types of behavior. Otherwise, popular individuals would not be given status by their peers. Moffitt’s (1993) theory works with this idea. Her framework supports the notion that adolescents value deviant behaviors because it enables them to assert and display their independence and preparedness for adulthood. Where the two paradigms diverge slightly is their view of autonomy. According to Moffitt (1993), acting out is a strategy employed by youth to obtain autonomy — or at least the impression of au- tonomy. The popularity literature, contrarily, views autonomy as a necessary developmental process that can actually shield youth from delinquent influences (Allen & Loeb, 2015). While Moffitt (1993) argues that the desire for autonomy causes youth to deviate from conventional values, Allen and Loeb (2015) argue that youth who successfully navigate ado- lescence are the ones who can figure out how to form bonds with peers while simultaneously establishing their own autonomy — independent of the group’s influences. Youth who can learn how to keep relationships, even when resisting negative influences, fare the best in the long-term (Allen et al., 2014). This idea of autonomy fits into the popularity literature rather neatly. As discussed in Chapter 2, popularity is comprised of both fitting in and standing

57 out. Youth who have the confidence, social intelligence, and ability to convince others to their way of thinking gain status. Similarly, youth who are able to bridge the gap between group dynamics and their own autonomy (self-interest) actually become more popular over time (Allen et al., 2006). While Moffitt (1993) was definitely tapping into a meaningful construct, autonomy’s place in the mechanism of deviance needs further exploration. It is very important to note here that autonomy and a quest for status are not the only factors driving youth to engage in these risky behaviors. It could be argued that these are subconscious or secondary motivations behind the risky behavior — which would still be consistent with the evolutionary framework previously presented. A relatively new perspec- tive known as the dual systems model (Steinberg et al., 2008) conceptualizes adolescents’ heightened drive toward sensation-seeking and new experiences with their developing ability to self-regulate. Simply put, adolescents are wired to seek novel and exciting experiences for their own sake. They seek things that feel good because they feel good. They do this because their brain craves these experiences and they have not fully developed their self- regulation system for behaviors.32 While this model has empirical backing (Shulman et al., 2016; Steinberg et al., 2018), the underlying reason for this behavior to exist in the first place is most likely due to adolescents’ drive to gain status in order to attract a mate, as discussed in Chapter 3. Role Magnets. To review, LCP youth are first ostracized by their peers, then once ALs experience a maturity gap, LCPs are accepted back into the fold due to their experience with deviant behavior. ALs begin to mimic LCP behavior in an attempt to display their indepen- dence and capability of obtaining adult roles thereby moving LCPs from the periphery of the social network to the center. Once ALs can obtain these roles legitimately, LCPs move back to the fringe of friendship networks. This proposition in Moffitt’s theory of LCPs being rejected, then central, and rejected again is known as the role magnet trajectory (Young,

32I will be incorporating the topic of this dissertation with the dual systems model in future works. This model, while criticized by some as over-simplifying the process (Pfeifer & Allen, 2012; Casey, Galván, & Somerville, 2016) has also accurately described and explained adolescent behavior across multiple samples in the US (Shulman et al., 2016) as well as more recently, across international samples (Steinberg et al., 2018).

58 2014). To the author’s knowledge, there has only been one test of the role magnet hypothesis. Young (2014) used the National Longitudinal Study of Adolescent Health (Add Health) to complete the sole test of Moffitt’s assertion. The author first used violent behavior — operationalized as fighting — to determine how many groups there were in the sample and who fit in which group. Violence was chosen as the grouping variable for theoretical considerations. Moffitt (1993) hypothesized that LCPs would be the most likely to use violence. Young also used a the measure of in-degree centrality (a count of how many peers listed them as friends) to measure popularity — although in this work’s framework it would be considered the likability construct. Young (2014) found trends consistent with Moffitt’s assertion that these youth who use violence were initially perceived by their peers as unpopular. However, their popularity increased and their social position moved to a more central location during adolescence turning these consistently violent youth into role magnets during late-adolescence. It should be emphasized that this trend ends as youth transition into adulthood. After adolescence, rule-breaking and violent behavior are no longer seen as socially desirable and young adults begin to desist. This forces the role-magnets — who generally do not desist at this time — to move back to the periphery of the network. While the vast majority of youth offend (ALs and LCPs make up approximately ≈95% of the adolescent population), what influences them to offend can vary. Nedelec, Park, and Silver (2016) is one of only two studies33 to examine the maturity gap using theoretically relevant variables in a methodologically sound way. The researchers obtained measures of physical and social development, took the difference between the two, and — by having a sample composed exclusively of monozygotic twins — controlled for genetic confounding. What they found was that controlling for genetic influence rendered some of the effects of the maturity gap insignificant. Said another way, individual differences matter — and in this case, they may matter more than environmental ones. This supports the idea that 33The other test being Barnes and Beaver (2010)

59 evolutionarily-driven propensities may be at play in the relationship between adolescents and offending. In addition to the genetically-informed analysis, the study also found that males and females differ in what influences them to offend. For instance, having delinquent peers was associated with both light and heavy drug use for females, but only associated with light drug use for males. This suggests that environmental factors play less of a role in risk-taking for males than for females.34 As displayed by Young (2014), implanting concepts from the popularity research into Moffitt’s framework can yield additional explanation to the processes at work during ado- lescence. Adolescents’ need to fit in and their desire for adult roles could motivate them to mimic LCPs and offend. ALs choose to follow LCPs because they are youth who — at least to a certain extent — behave more like an adult than the AL does. Due to the evolutionary mismatch that youth experience, this is the closest that AL youth can come to having the adult status. However, individual differences also need to be considered. While youth, in general, may be driven to seek adult roles, there is still variation between individuals. Additionally, Young’s (2014) work suggests that social network centrality measures are related to the concept of popularity defined in Chapter 2. Even though the measure was closer to the concept of peer acceptance, it still associated to key theoretical variables in a way that confirms the popularity literature. Summary

Moffitt’s Taxonomy came at a time when multiple lifecourse theories were being developed (e.g. Gottfredson and Hirschi (1990) and Laub and Sampson (1993)). However, her theory of adolescent deviance is unique in that it incorporates both within-individual and between- individual variances. Furthermore, it provides a proximate framework that is consistent with evolutionary principles. On top of these contributions, Moffitt (1993) also laid out the mechanisms by which life events earlier in life can have far-reaching effects later in 34It should be noted that because the sample of this study only included monozygotic (MZ) twins, there may be a problem with generalizability. The researchers’ findings may not apply to other types of siblings besides MZ twins.

60 the lifecourse. While Moffitt focused on how deviant behavior has consequences later in life, it could also be that an individual’s location in the social network could affect one’s trajectory. This is especially likely considering Young’s 2014 results finding that those who commit deviant acts later in adolescence move from the periphery to the center of the social network. Having considered all of this, it has led me to develop the research question “How is social centrality in adolescence related to well-being later in life?"

61 Chapter 5

Rule-Breaking and Social Centrality Correlates with Popularity

As discussed in Chapter 2, there are many benefits for youth to become popular. They are recognizable among peers, have access to resources, have better dating prospects, are less likely to experience depression or anxiety disorders, and can determine the direction of the group and impose their ideals. However, there are also pitfalls. As briefly discussed in Chapter 2, adolescents being among their peers while unsupervised is one of the chief risk factors for commission of criminal acts (Warr, 2001; Osgood et al., 1996). This section discusses these relationships more thoroughly. Substance Abuse. The research of popularity and substance abuse has traditionally looked at the relationship through one of two lenses. The first lens is one of socialization. The idea of socialization, while not a new concept (see Kubrin et al. (2009) for a review on social learning theory), comes from the observation of homophily among peers. Homophily refers to the concordance in behavior, beliefs, and attitudes among adolescent dyads and peer groups (McPherson, Smith-Lovin, & Cook, 2001; Schwartz & Hopmeyer-Gorman, 2011). Consistent with social learning theory, many researchers explain homophily by arguing that adolescents acquire their substance use behaviors through a socialization process with close friends (Schwartz, Gorman, Nakamoto, & McKay, 2006).35 This effect is then reinforced by the desire to be accepted by peers (Verkooijen, Bloomfield, Nielsen, & de Vries, 2007). Youth may passively model this behavior or they may succumb to peer pressure. Regardless, exposure to peers with values that endorse alcohol or drug use increase the likelihood that a youth will experiment with substances (Alexander, Piazza, Mekos, & Valente, 2001; Allen, Porter, McFarland, Marsh, & McElhaney, 2005). While the process of socialization is clearly important, the second lens through which this 35There is still a debate regarding socialization vs. selection (see Lilly et al., 2018). While socialization is more relevant to this study, a discussion on the genetic origins of selection will be discussed later in this chapter.

62 relationship is viewed, exposure, is also of great concern (Schwartz & Hopmeyer-Gorman, 2011). Although there is little research directly assessing popularity as a risk factor for substance abuse via exposure, some insight can be gained by reviewing findings from crowd affiliation studies. What is meant by “crowd" in this context could be equated to clique or “crew." It is the group of one’s peers that one identifies with. Examples of groups are “jocks", “nerds", “band geeks", or “preps." Crowd membership is important because, while members of the same crowd may not affiliate with each other on a regular basis, it can inform researchers as to a youth’s peer role models (Schwartz & Hopmeyer-Gorman, 2011). Those wishing to become or remain popular will take note of their peers’ behaviors and mimic in order to “fit in." Evidence of this can be seen with both alcohol and substance abuse. Verkooijen et al. (2007) sent out a mail survey in Denmark that attempted to assess youth’s group membership and their likelihood of substance abuse — defined as alcohol, tobacco, and/or marijuana use. The researchers found that youth who identified with skater, pop, hip-hop, or hippy groups were more likely to adopt the pro-substance use norms of the group while those in more pro-social groups adopted anti-substance use norms. Further evidence, reported by Becker and Luthar (2007), found that youth who were seen as substance users were also respected and admired by their peers. Therefore, youth who aspire to be popular or wish to keep their status in the network at large are more likely to interact with groups that are pro-substance use, thereby exposing them to these norms. These studies do seem to support the discussion from Chapter 2. When a youth climbs the social hierarchy, s/he is more likely to be exposed to individuals with beliefs that support drug use. Furthermore, as youth increase in their desire to use alcohol and drugs, there will be an increased pressure on those with status to “fit in" in order to maintain that status. A major shortcoming to the research above (and other research that has attempted to investigate this relationship) is that they use the measure which captures the construct of likability, and not popularity. Recall from our discussion in Chapter 2 that popularity is

63 defined as a measure of status, influence, and network centrality — not likability (or the number of friends one has). The above studies operationalized their construct by counting the number of peers that named that individual as a friend, thereby measuring how liked that individual is. Chapter 2’s conceptualization argues for a direct measure of popularity by either sociometric or network centrality methods. To the author’s knowledge, there are only five studies that have looked at the relationship between substance use and popularity as conceptualized in Chapter 2. Three of these five studies were longitudinal designs. The first of these works was con- ducted by Killeya-Jones and colleagues (2007). Using a 1-year longitudinal design of middle- schoolers, they found that youth who participated in drinking and those who smoked were more popular than youth who did not upon the first measurement. After one year, youth who drank or smoked were also more likely to still be more popular — suggesting that sub- stance use may be an indicator of status among these youth. The following year, Mayeux, Sandstrom, and Cillessen (2008) conducted a 2-year longitudinal study that followed youth from the end of their sophomore year to graduation. Similar to the 2007 study, Mayeux and colleagues found that alcohol and tobacco use predicted popularity. A more interesting finding from this study deals with the relationship between popularity and substance use. For boys, smoking predicted an increase in popularity. This finding suggests that these boys smoked in order to increase their status. So it is not just popular youth smoking to keep their status or defining what is “cool", but there is also an instigating effect of others smoking in order to become popular (Schwartz & Hopmeyer-Gorman, 2011). The third longitudinal study is the only long-term design. The researchers, Sandstrom and Cillessen (2010), followed a group of 264 youth from middle-adolescence to early- adulthood. They found that popularity increased with self-reported levels of tobacco use, alcohol use, and substance use. Also, popularity during high school had a positive rela- tionship with risk-taking in early-adulthood. While this relationship is modest, the finding tentatively supports Moffitt’s assertion of a group that becomes socially central during high

64 school and offends throughout most of the lifecourse. The fourth study, (Gommans, Muller, Stevens, Cillessen, & Ter Bogt, 2017) used a sample of 800 adolescents with a mean age of 14 to explore popularity’s relationship with substance-use level, as well as how popular youth influenced those with a lower status. Using a multi-level model with students at the individual level and classrooms at the aggregate, the researchers found that popular youth drank alcohol more than unpopular youth. Given the previous evidence, this result should not be surprising. However, the study also found that popular youth still drank more even when surrounded by less-popular youth. This finding could suggest that popular youth have internalized their definition favorable to drinking and/or that they are trying to cement their status among those that are not in their group. Finally, Ragan (2014) used longitudinal network analyses to determine if peer beliefs about drinking alcohol affected behavior. He found that peers’ beliefs significantly predicted an individual’s own belief regarding whether drinking alcohol was an acceptable behavior. Furthermore, peers’ beliefs also predicted whether or not the individual reported drinking. Ragan contended that while this relationship did not completely explain adolescent alcohol use, he considered it to be a direct effect. He hypothesized that there was an indirect effect at play as well. Being around youth who believe it is morally fine to drink increases the opportunity for one to use alcohol. This work is consistent with research conducted earlier. While not many studies have been conducted with the conceptualization of popularity used in this dissertation, we can draw some tentative conclusions. The first one being that popular youth are at higher risk of abusing substances when the peer group values such behavior. Youth who are a part of the high-status group are more likely to have an opportunity to drink or smoke and will feel pressured to do so if the group supports such behavior. Given Moffitt’s (1993) assertion that the maturity gap influences youth to commit delinquent acts to appear more adult-like, it is no surprise that substance use is common among high-status youth. To keep their status, they must be perceived as the most mature and daring. The addition of LCPs to the center of the network allows more opportunity

65 to display one’s maturity as well (Cohen & Prinstein, 2006). In addition to just providing opportunity, LCPs’ shift from the periphery to the center of peer networks could cause these youth to adopt definitions favorable toward offending. On top of all of this, youth who are popular may be more sensitive to social cues, as they are socially savvy enough to be popular in the first place. Therefore, they may feel added pressure — just by virtue of their perceptions — to engage in substance use (Allen et al., 2014; Mayeux et al., 2008). Taken together, youth who desire status must navigate these risks if they are to obtain and keep their place. They may not only be encouraged to drink, but also influenced to drink more than the average student (Choukas-Bradley, Giletta, Neblett, & Prinstein, 2015).36 The case should also be made that the attainment of status is not the only reason that youth are attracted to illicit substances. While raising one’s status would be an external reason, there is an implicit motivation to use drugs and alcohol. That motivation is that it feels good. And while it is a good sensation for humans37 — regardless of age — as discussed in Chapters 2 and 3, adolescents are “hard-wired" to seek new experiences and sensations. While popularity is not the only factor motivating youth to experiment with drugs, it as a strong one (see Chapter 2) and the chief one of this study. Underage Sex. There are many parallels to draw between the research on substance abuse and underage sexual activity (defined in this study as having sex while being a high school student). Similar to drinking and smoking, having sex can be viewed as an adult enterprise. Those who have sexual relations are seen as more mature and deserving of status (Prinstein, Meade, & Cohen, 2003). One parallel that can be drawn between the two relationships are their potential causes. 36It should be noted that these relationships are inferential based on the literature and are not empirically backed. Furthermore, they are most likely context-specific, if true. An example of the context-specific nature of substance use will be seen later in this chapter with the discussion of race. In short, white youth are more likely to use alcohol than are black youth. However, there is evidence to suggest black youth are more likely to use marijuana. These trends may suggest preference among different groups of individuals and, therefore, difference among those with status in those groups. 37It is not only a motivator for humans, but also for many mammals including primates Weerts, Fantegrossi, and Goodwin (2007) and rats Ahmed, Walker, and Koob (2000). This trend supports the evolutionary theories discussed in Chapter 3 and the overall framework of this study.

66 As discussed in Chapter 2, popularity is a status. With that status comes access to re- sources that others without status do not have. A chief resource among adolescents is dating prospects. Evidence of this can be seen in work conducted by Carlson and Rose (2007) where they found that popular youth are more likely to have a significant other. Considering that sexual intercourse is most likely to happen in a monogamous relationship (Zimmer Gembeck & Collins, 2008) and, one can see that popular youth are more likely to have the opportunity for sex than are less-popular youth. Similar to the substance abuse literature, youth can also be socialized or pressured to have sex when in a monogamous relationship if their peers are also engaging in such activity. Adolescents, generally, adopt the view of sexual activity that their friends have. Those who socialize with peers who are likely to engage in sex are more likely to do so themselves (French & Dishion, 2003). Those who associate with a group that values abstinence are less likely to engage in sex (Silver & Bauman, 2006). These relationships support Moffitt’s taxonomy. The combination of a maturity gap where adult roles like sex are desired combined with the high susceptibility of youth suggests a reinforcement between biology and socialization. While the proposed theoretical components seem supported, there have been a number of studies that have looked at the relationship between popularity and sexual activity. Prinstein et al. (2003) were the first to explore this relationship. Using a sample with the average age of 16, they found that youth who engaged in oral sex and intercourse were significantly more popular than those that did not. This relationship only held for those with relatively few partners. If the number of partners was too high, the youth was not considered popular. This interesting finding suggests that, at least in this peer group, monogamy is valued and it gives further support to Carlson and Rose’s (2007) finding that popular youth are more likely to be in a monogamous relationship. Mayeux and colleagues’ (2008) work — which also assessed substance use’s relationship with popularity — supported Prinstein, et al. (2003). Over the last two years of high school, they found that popularity predicted increased sexual activity. While these studies do not shed light on the mechanisms, they do show support for

67 the relationship between being popular and having sex during adolescence. These findings could potentially explain why some adolescence-limited offenders do not desist soon after entering adulthood. Moffitt (1993) described the cumulative consequences that some youth face. This concept refers to the limitation of legitimate options caused by a past event in their life. These events ensnare youth into a cycle of deviance that is incredibly difficult to exit. Moffitt listed teenage pregnancy as one of the key snares that youth may encounter that will delay their desistance. Furthermore, early romantic involvement has been associated with future psychosocial difficulties — even in an online setting. (Szwedo, Mikami, & Allen, 2012). Popularity, with all of its benefits, can also be a risk factor for continued deviance if dating and sexual interactions are not done safely. Poor Academics. While there is a relatively clear link between popularity and sub- stance use/sexual activity, the relationship for academic performance is a bit more nuanced. Understanding this relationship may also have the most long-term consequences as any other discussed in this section. If youth do poorly in school, they are less likely to have good job prospects, which will lead them to more economic and social obstacles to overcome on the road to desistance. To understand this relationship, it is imperative to separate popular youth into two categories: 1) those who show high levels of aggression and antisocial be- haviors and 2) those who show normal levels of aggression and prosocial behaviors. When separated into these groups, the relationship becomes much more clear. Using cluster techniques, Rodkin and colleagues (2000) found that youth who had a negative attitude toward school, but were popular, did poorly in school. As alluded to above, this group also scored high on antisocial behaviors as well as aggression. The other group of popular children who scored low on aggression, did not show a significant drop in grades during adolescence (de Bruyn & Cillessen, 2006). These findings together may support Moffitt’s taxonomy. Popular youth who are LCP never did well in school, mostly due to their neuropsychological deficits. This, most likely, caused them to have little investment in education. Conversely, AL youth who are popular, while wanting to adopt adult roles, do

68 not let their grades slide significantly. Schwartz and colleagues’ (2006) work came to similar conclusions. The researchers fol- lowed a sample of high schoolers for four semesters. They found that as popularity rose, so did the number of unexcused absences. Grade point average also dropped as popularity rose. But this relationship held only when the individual was experiencing an increase in aggression. Neither of these relationships were significant when aggression was at a normal level. Property Crime. It is interesting that, while substance use and underage sex have both been investigated, property crime has not been explored much. Dijkstra and colleagues (2009) explain that, theoretically, youth who participate in destructive behaviors (e.g. van- dalism) should increase in popularity for those who display other prosocial and highly valued attributes. They argue that defying norms is a way of showing their autonomy and that van- dalism would be a chief way to display this. However, when Dijkstra and colleagues (2009) tested this idea they found no association between destructive behavior and popularity for neither the prosocial group nor the antisocial one. In addition to vandalism, Santor and colleagues (2000) tested whether theft — in the form of shoplifting — was associated with popularity. The researchers developed vignettes that gave examples of different types of deviance and asked the 9th to 11th graders to rank the level of popularity they had for the hypothetical youth in the vignette. In addition to the vignettes, the researchers developed a survey for participants to self-report their levels of deviance. What the study found was that theft was positively associated with popularity for both males and females — but males did admit to committing theft significantly more than females suggesting it may be more normal for males to shoplift/steal. Other studies have found similar results linking theft and acceptance within friendship dyads (Selfhout, Branje, & Meeus, 2008) — suggesting that theft may or may not be related to popularity, but it most likely is related to pairs of friends. Despite these inconclusive findings, researchers argue that looking at property crimes —

69 such as theft, vandalism, and shoplifting — would be beneficial to the field because of their predicted prevalence. Scholars predict that these types of crimes would be more common in early adolescence than would substance use and underage sexual activity (Mayeux et al., 2008). It is interesting that they have not had more attention. This study seeks to add to that literature. Confounding Variables

While popularity has many benefits, it is also associated with deviant behavior. Youth who are aggressive and popular are at the highest risk for detrimental consequences, but all popular youth have a higher probability of engaging in underage sex and using alcohol, to- bacco, and/or illicit drugs. These relationships relate to the theoretical frameworks discussed in Chapter 4. Studies suggest that there is a socialization process where popular youth are indoctrinated into an adolescent subculture where deviant acts are encouraged. This sub- culture may be an effect of our society’s structure which causes a maturity gap for teens. While there has been an uptick in the research being done on this topic, more research is still required to determine the magnitude and mechanisms of these relationships. This section looks at other concepts that may condition or confound the relationships discussed above. Socioeconomic Status. The link between crime and low socioeconomic status (SES) is one of the founding relationships of modern criminology. The four major theorists of criminological thought from the early 20th century focused on this relationship when laying out their theories (Shaw & McKay, 1942; Sutherland, 1947; Merton, 1938). As discussed in previously, popularity is also associated with certain forms of offending — especially when youth are in groups. Therefore, it should be no surprise that SES and popularity are also linked. According to Moffitt (1993), LCPs’ antisocial behavior stems from neuropsychological deficits (NPDs). These NPDs are a result of both environmental and genetic influences38 (Moffitt, 2006). NPDs cause youth to perform poorly in school, act overly aggressive, and

38The genetic component of this will be discussed later in this chapter.

70 in turn are ostracized from their peers. In addition to these obstacles, LCP youth’s parents are likely to have the same deficits, resulting in the family likely living in a low-income neighborhood. Because they live in these areas, they are at risk of falling prey to the factors of differential association Sutherland (1947). Their schools — if they consist of other students from a disadvantaged neighborhood — are more likely to be composed of students with the same deficits. With a higher proportion of students with NPDs, it follows that youth will have a greater chance of being exposed to deviant peer groups. These deviant peer groups may gain status through adolescence and set the standards that deviance is acceptable or expected, thereby reinforcing delinquent behavior. Other works such as Anderson (2000), Pattillo (1998), and Wilson (1987) suggest that popularity — in the sense that most Americans think of it and how it is defined in this project — may be replaced with another concept known as respect in severely low-income areas. Because of social and economic forces that took place in the 1970s and 1980s, African Americans were caught in high-crime, low-income pockets in American cities. Once there, they were unable to leave due to lack of employment opportunities (Wilson, 1987). Because these areas were entrenched in poverty, the only way for youth to gain status in these areas was by earning respect. This status was earned by being the “biggest and baddest on the block", having multiple sexual partners (for males), and asserting dominance over all of their peers. According to Anderson (2000), only a small group of youth in these areas whole- heartedly bought into these ideals while the majority of youth adopted these behaviors as protection. And the appearance of gangs forming in these areas may have reinforced or legitimized the behaviors (Pattillo, 1998). While these works are either qualitative or historical, there has been one quantitative work that has substantiated these claims. Schwartz, Hopmeyer, Luo, Ross, and Fischer (2017) used a sample of 7th- and 8th-graders in a middle school that served an economically-deprived area of Los Angeles, CA. Over 80% of the sample was Latino, 9% Asian, and the rest were a mixture of races and ethnicities. What the study found was that the gang members — or as the students referred to them,

71 “cholos" and “taggers" — generally did poorly in school, acted aggressively, but also had high-status and were visible to their peers. This suggests that in economically-deprived areas, gang members are seen as possessing the respect of his peers, which runs parallel to the forms of popularity seen in less disadvantaged areas. The influence that these youth have on others, and the effect they have on the school, could prevent lower-status, more pro-social youth to have difficulties in the classroom. The high-status youth may cause disruptions and apply social pressures, thereby impeding other youth from achieving academically (Schwartz & Hopmeyer-Gorman, 2011). Race. Another important factor that may affect these hypothesized relationships is race. While race is intertwined with SES in the United States (Damaske, Bratter, & Frech, 2017), it is important to differentiate between the two as much as possible. This section discusses the relationship between race and deviance via popularity. The only study to the author’s knowledge to look at the relationship between race and popularity (Choukas-Bradley et al., 2015) argued that popularity should be researched within a cultural context. To explore this perspective, they used a sample of 364 low-income high school freshmen and surveyed them every semester until the Fall of their senior year. A little over half of the sample was Caucasian, a quarter was Black, and roughly 20% were Latino. The researchers’ model that did not control for race found a positive linear effect between popularity and alcohol use. However, when the groups were broken into their separate racial groups, they found that this relationship did not hold up for African American youth. Alcohol use and popularity’s relationship was strongest for Caucasian youth and moderate, but significant, for Latino youth. There was no significant relationship for the African American group. This study demonstrated that popularity needs to be considered within a cultural context. While this study did not analyze other substances commonly used by youth (e.g. tobacco, marijuana), it may not be a stretch to assume that alcohol may be replaced by another substance as a status symbol within African American peer groups.

72 Age. The age at which popularity and peers’ importance begins to emerge and the age- crime curve have been discussed at length already. But one aspect of age that has not been assessed is age of onset. Moffitt (1993) argues that LCPs and ALs are almost indistinguish- able during adolescence. They both display antisocial behaviors and delinquency. However, the key difference in the etiology of these behaviors between groups is that LCPs suffer from neuropsychological deficits that exist very early in life, at birth, or before birth. Due to the possibility that the causal factors of their antisocial behavior can be traced to early in the life course, LCPs start offending at an earlier age than ALs. Allen et al. (2014) looked at a group of individuals from age 13 to 23 who display what they call “pseudomature behavior" — drinking, smoking, sex, and delinquency — at an early age. The study’s cross-sectional results showed that the youth who displayed pseudomature behavior at an early age desired popularity more than their peers. This strategy worked for a time. Those who displayed pseudomature behavior had gains in popularity across adolescence. However, the study’s longitudinal findings concluded that these youth experience a higher likelihood of mood dis- orders, substance abuse problems, problems in close relationships, and an increased chance of criminal involvement later in life. While this study does not verify the mechanisms that Moffitt (1993) put forward, it does give support to the idea that there are multiple groups of youth and that their behavior in adolescence can have cumulative consequences later in life. Gender. While the above concepts are important to consider, the variable that may upset the model most, if not controlled for, is gender. Much research has been conducted on how the sexes differ in their pursuit of status. Early work was largely qualitative. It consisted of interviewing high schoolers and looking for trends across interviews. However, recent has also began to shed light on these relationships. These two veins are discussed next. Qualitative Evidence. One interesting nuance to the three qualitative works that have looked at this relationship is that two of them only research one gender, females (Adler,

73 Kless, & Adler, 1992; Merten, 1997). Their findings suggest that there is a stark difference between what makes a girl popular, as opposed to a boy. For females, popular girls are mostly described as “attractive, affluent, and involved in prestigious school activities (particularly cheerleading)" (Rose, Glick, & Smith, 2011, p. 105) and acting snobby (Adler et al., 1992). An attractive girl, according to Adler and colleagues (1992), is a female who can physically attract popular boys. They also were incredibly attuned to their appearance, especially keeping up with the latest fashion trends. It should be no surprise that physical attractiveness is a key factor for female popularity. From an evolutionary perspective, males are attuned to a female’s appearance to assess fertility. Considering that adolescence is the point in life where an individual becomes sexually mature, a popular girl who could attract a popular boy would be one who was viewed as fertile and mature (Dawkins, 1976; Pinker, 2002). As far as popular girls keeping up with current fashion trends, this would take a bit of affluence of her family. Make-up and clothing are expensive. A girl’s family would have to be financially stable in order to afford such leisure items. This affluence and appearance would then allow an adolescent girl to focus on participating in desirable extra-curricular activities. She would have the physical appearance as well as the support of her family to take on the time commitment. As a cheerleader, she would gain visibility and, by effect, status. The last finding regarding females is that they alternate fluidly between acting friendly to their peers and acting snobby or elitist. This may speak to the duality of popularity discussed in Chapter 2. Most individuals want to be liked by their peers. In addition to this desire, youth want status. Likability is a more abundant resource than is status. In order to be liked, one mainly has to treat others with respect. However, to gain status, one must be a bit more shrewd. Popularity, for girls, involves balancing any desire to be liked to remaining a part of the exclusive group (Merten, 1997). While the qualitative work has paid more attention to females, it has not completely ignored males. Attractiveness is the most important quality for females, but it seems that athletic ability is the more important factor for determining a male’s popularity (Adler et

74 al., 1992; Eder, 1985). While this may show a contrast between males and females, further consideration shows significant parallels. Athletic prowess, in an American high school, can be considered the male parallel to females being a member of the cheerleading squad. It allows for visibility to the rest of their peers, thereby giving him/her status. Add that making a sports team is competitive — making the team and becoming a starter are both earned through try-outs and practice — and the popular youth is considered as the most physically capable male among his peers. This would grant him popularity and influence among his peers. Although physical attractiveness is not as big of a factor for boys, self-presentation was still important. Similar to females, popular boys are expected to keep up with the latest fashion brands and styles. This requires popular boys to come from affluent homes as well. One final difference between the genders is the attitude that is used for popular youth. The (Adler et al., 1992; Merten, 1997) found that popular girls were generally seen as snobby when interacting with those outside of their clique. Boys, on the other hand, generally were seen as having a good sense of humor or being the “class clown." This deviation may be a product of the type of aggression the genders tend to use. Chapter 2 discussed that females are more apt to use relational aggression while boys are more likely to use overt aggression. Therefore, social exclusivity (snobbishness) would be a tactic more used by females while males could be more charismatic while using physical threats as needed. Quantitative Evidence. Recent quantitative evidence has supported many of the find- ings reported by the early, qualitative work. For example, in regard to self-presentation, Vaillancourt and Hymel (2006) found that self-presentation was a key attribute of both popular girls and boys with no significant difference between the two groups. While self- presentation’s relationship is consistent across methodologies, physical attractiveness is a bit different. Qualitative works emphasized physical attractiveness for females more than for males. Duncan and Owens (2011) found support that attractiveness was the number one predictor of popularity for females. They surveyed two demographically different high

75 schools and found that regardless of demographics, attractiveness to boys was the major loading factor that predicted popularity. However, the quantitative work does not report an imbalance between the two groups in regard to attractiveness (Boyatzis, Baloff, & Durieux, 1998; Vaillancourt & Hymel, 2006) (Boyatzis, et al., 1998; Vaillancourt & Hymel, 2006). Boyatzis and colleagues (1998) gave a group of ninth graders vignettes involving attrac- tive and unattractive hypothetical individuals. The participants were asked to rank these hypotheticals’ attractiveness. Regardless of gender, popularity was significantly, positively related to attractiveness suggesting that attractiveness is equally important for males as it is for females. One caveat to this line of inquiry involves what makes an individual attractive. According to the qualitative work, physical attractiveness is less important for males than females. What is important for males is athletic prowess. It is possible that athletic ability could augment a male’s attractiveness. And because the quantitative work’s methodology is cross- sectional, it is unclear as to whether female peers’ rating of attractiveness preceded the display of the popular male’s athletic prowess, or if the attractiveness is the result of the athletic ability. The idea that attractiveness is the result of athletic prowess supports the evolutionary perspective put forth in Chapter 2. In order to attract a mate, a male must demonstrate that he can provide resources for his offspring to better increase their chance of survival. Status is one of the highest probability ways to obtain resources. Therefore, if athletic ability gives status, from an evolutionary perspective, it should also increase attractiveness. Furthermore, there is not a clear line of whether attractiveness causes more success or more success causes more attractiveness for males. Hamermesh (2011) explained the benefits of being attractive. One of which is that attractive people earn more money, generally. However, as discussed in Chapter 3, males who obtain resources are more attractive to women. Therefore, the temporal ordering could be reversed or, at least, the relationship could be self-reinforcing. The neurobiological research shows findings that support gender differences in popularity

76 as well. Guyer and colleagues (2009) used a chatroom to evaluate how adolescents’ brains react when there is an anticipation of evaluation from peers. What they found was that the regions of the brain dealing with social-affective processes were activated. However, the girls in the sample showed significantly more activity in these areas than did boys. This trend strengthened as the girls aged as well suggesting that females are more attuned and susceptible to evaluation by their peers. Genetic Influence. The evolutionary perspective discussed throughout this disserta- tion along with the relationship between brain formation/functioning and peer influence suggests that genetic factors play a significant role in the relationship between popularity and deviance. Furthermore, the threads of genetic influence are interwoven throughout the theoretical perspectives discussed above. This section discusses how genes matter throughout these different components. To begin, research regarding the concept of antisocial behavior — which is what the majority of youth display during adolescence — has been linked to genes. A number of meta analyses (Ferguson, 2010; Mason & Frick, 1994; Miles & Caery, 1997; Plomin, 1990; Polderman et al., 2015; Rhee & Waldman, 2002) have shown that genetics explain about half of the variance in antisocial behavior in the population. Any discussion of this behavior then, to be properly specified, must include genetic analyses. Direct support for the idea that genes, rule-breaking, and popularity are interconnected can be found by Alexandra Burt (2009). She used a sample of adolescent boys and found that a specific of the 5HT2A serotonin receptor gene’s effect on popularity was par- tially mediated by rule-breaking. This model demonstrated an evocative gene-environment correlation (rGE) and showed that genetic factors via behavior can affect an individual’s contemporary or cumulative consequences. The idea of cumulative consequences was a significant part of Moffitt’s (1993) theorized mechanism for the perpetuation of behavior. She discussed the role of genes in her theory by reviewing the research of the time. What this research found was that serious,

77 long-term, persistent antisocial behavior — indicative of those who are LCP — is more genetically affected than is a normal level — similar to the AL trajectory (Moffitt, 2006). More recent work has used the Add Health data to explore how genes affect trajectories. Using a heritability estimate, Barnes and colleagues (2011) found that genes explained up to 70% of the variance for being identified as a LCP offender. The effect was smaller for ALs at 35%. These works support the idea of contemporary and cumulative consequences, Moffitt’s theory of neuropsychological deficits influencing the LCP trajectory, and also shows that these deficits are, at least in part, genetically influenced. Moffitt’s theory is not the only framework that incorporates genetic influences. Earlier in the chapter, the idea of socialization and its effect on drug use was briefly mentioned. The idea of a debate between those who support a socialization effect versus a selection effect into deviant peer groups was also mentioned. While the socialization perspective has received the most testing overall, the alternative view — selection effect — has received an increased amount of testing and support in recent years (Beaver et al., 2009; 2010; Christakis and Fowler, 2014; Barnes, Boutwell, Beaver, Gibson, and Wright, 2014). In essence, the selection effect perspective argues that instead of youth being influenced by peers and learning to commit crime, youth who have criminal tendencies band together to form delinquent peer groups. This trend is an example of an active gene-environment correlation. One’s genetic propensities are driving the peer groups in which a youth select into. This group is the most likely to allow the individual’s genes to express themselves. Therefore, if a youth has an inclination toward music, he or she will join a band. If he or she inclines toward sports, he or she will try out for basketball. If the individual has predispositions for antisocial behavior, he or she will join the delinquent crowd. One recent study (TenEyck & Barnes, 2015) tested this active gene-environment correlation. Early models of the analysis showed evidence that a socialization process was present. However, once genetic factors were added to the model, that effect was rendered insignificant suggesting genetic influences can be used to adjust for selection mechanisms.

78 Genetic influence is not only present in the theoretical concepts, but also in the construct of popularity. As discussed previously, there are multiple components to what makes someone popular. One of the most salient of these components is obviously genetically influenced: attractiveness. Our genes determine our physical phenotypes. Examples are hair color, eye color, height, complexion. In addition to these traits, our genes also determine our bone structure — which determines our facial features and partially explain our metabolism and musculature — which can partially determine athleticism and body appearance. Jones and colleagues (2001) have put forth that our faces — specifically their symmetry and features — are an indicator of our genetic health. Over millennia, our ancestors detected the correlation between facial features and genetic health, thereby allowing us, their descendants, to judge an individual’s evolutionary fitness at almost a glance. If we are able to determine an individual’s fitness by physical appearance, then it follows that those who are more fit have status as their genes are highly coveted by their peers. Further support for this idea of attractiveness and fitness was given by Nedelec and Beaver (2014). The researchers used the Add Health dataset to examine the association between physical health indicators and physical attractiveness. What they found was that those who were considered to be more attractive had a low probability of having a chronic physical disease or neuropsychological disorder. These findings suggest that humans, as a species, have evolved to value certain physical features because they are a proxy for health and reproductive viability. Those who have popular genes are more likely to be popular. Another key factor to popularity is affluence. While affluence may not be a direct result of our genes, it is influenced through other processes. For instance, intelligence (as measured by IQ) is largely genetically determined (Gottfredson, 1994). Intelligence is also one of the key predictors of monetary success in life (Herrnstein & Murray, 2010). Therefore, individuals who are born with the genetic predisposition for high intelligence are more likely to be affluent than those who are not. Parents will pass this predisposition on to their children along with the monetary advantage that they earned by using their intelligence.

79 The neurological research discussed in Chapter 2 also has a genetic base. All genetic influence on behavior will show its effect via neurological processes (Pinker, 1999; 2002). Our genes affect how our brain is formed, how it is connected, and what level of neurotransmitters we have to regulate our brains. It may also help explain the gender differences discussed in the previous section. While differences between male and female brains are slight, they are significant for behavior (Wright et al., 2014). Guyer and colleagues’ (2009) work suggests that the gender differences exist in the relationship between popularity, peer influence, and deviance. With all of this evidence demonstrating that genetics play a role in an individual’s pop- ularity and level of rule-breaking, it is important to note that these genetic factors do not wholly determine an individual’s popularity or rate of rule-breaking. This is an important theoretical distinction that is often overlooked or misunderstood. This misunderstanding can be dated back to the three theorists referenced earlier (Shaw & McKay, 1942; Suther- land, 1947; Merton, 1938). Their work is considered to be partially developed as a pushback against the Neo-Lombrosian theories of the time which overemphasized the deterministic aspect of evolutionary work (Lilly et al., 2018; Warr, 2001). However, given current find- ings and methodologies, it is safe to conclude that genetics and environment are intertwined and work together to influence behavior, making an individual who they are (Beaver, 2016; Wright et al., 2014) Summary

As can be seen in this chapter, there is a wealth of work exploring the relationship between popularity and deviance. Generally, there seem to be two profiles that popular youth fall into. One is the aggressive, physically developed, “bad boy" who cares little for school, authority, or rules. The other is less aggressive, also physically developed, who follows society’s rules with the exception of deviant behaviors related to partying (e.g. drug, alcohol, and tobacco use). While this trend is seen across multiple studies, there is also evidence that it varies based on locale and can be context-specific. And while these studies give great

80 insight into the relationship, there has yet to be one that considered many of these factors in one analysis. Based on the literature provided in this chapter, this dissertation will asses the overall relationship between deviance and social centrality when all of these factors are considered in one analysis.

81 Chapter 6

The Current Study Research Questions

Chapter 2 defined popularity while Chapter 3 discussed the evolutionary drive for youth to attain status among peers. Following these, Chapter 4 explored Moffitt’s (1993) theory within this evolutionary perspective. However, Moffitt’s theory and this definition of popu- larity or social centrality have never been combined and tested to see if this conceptualization is predictive of deviant outcomes. The literature suggests a bidrectional relationship between popularity and rule-breaking/deviance. In other words, deviance increases popularity and popular youth are pressured and have higher opportunity to deviate. Similarly, youth who deviate later in adolescence are more likely to move from the periphery of the social network, to the center. Another layer of this research vein is the evidence of evolutionary forces at work. Based on the neurological literature, it seems that sensitivity to social cues during adolescence is species-wide, suggesting that it had some evolutionary benefit to our earliest ancestors; and could have some benefit to humans today. Based on the findings and trends discussed in the preceding chapters, this dissertation will answer three research questions:

1. What are the factors that predict social centrality? 2. What is the relationship between social centrality and deviance? 3. How is social centrality in adolescence related to well-being outcomes later in life?

Analysis Plan

To accomplish the task of assessing the research questions, this work will lay out its analysis in three steps. Step #1. The first step of the analysis is to consider the overall sample and assess what factors significantly predict social centrality. To accomplish this, I will analyze the control variables and their effect on participants’ proximity prestige (a measure of centrality)

82 scores. I will then compare these results to an analysis including all participants and all variables measured during adolescence. Many of these relationships have been tested in the popularity literature, but not using a social centrality measure. Conducting this test provides an opportunity to assess the applicability of findings on popularity to proximity prestige. The purpose of this step is to establish a context around what factors influence a youth’s proximity prestige score in the dataset so that I can more fully explore the nature of centrality, popularity, and deviance. Step #2. The next step in the Analysis Plan is to explore the relationship of proximity prestige and deviance and see how it varies across groups within the sample. I will explore how the centrality measure is distributed across socioeconomic status (SES), age, race, and biological sex. Based on previous research (Duncan & Owens, 2011; Guyer, McClure-Tone, Shiffrin, Pine, & Nelson, 2009; Rose et al., 2011; Vaillancourt & Hymel, 2006), there should be roughly the same amount of socially central males and females. I hypothesize that there will be differences across gender regarding what makes an individual popular, but there should not be a significantly greater amount of popular males than females — or vice versa. The evolutionary ideas discussed earlier regarding attraction of mates and dating practices form the basis for this argument. Popular youth have access to better dating prospects. Because popularity is highly prized among adolescents (Allen & Loeb, 2015; Brown, 2004), popular youth will generally date youth with a similar status in order to maintain their own position. Because of this tendency to pair off, an imbalance between genders would severely handicap the sex with more high-status individuals and cause more competition between them. Therefore, it follows that there will be roughly the same amount of high- status females as there are males. Ultimately, however, this is an empirical question so I will first analyze popularity across gender. Contrarily, if one considers Young’s 2014 work, centrality should change across age. If youth begin to offend in adolescence due to a maturity gap, then younger youth will look up to their older, more learned peers for guidance of modeling in order to assert their autonomy.

83 Furthermore, lifecourse persistent youth should move from the periphery of the social network into a more central location. As deviant behaviors become increasingly valued, the youth who have experience with deviant behaviors will become more connected and influential. Finally, we should also expect to see a difference in proximity prestige across SES levels. The ability to obtain socially desirable goods (e.g. clothing, cars, electronics) is a hallmark of popularity. And in order to obtain these, one must have money. The most effective way for a teen to have money is to come from an affluent family. Therefore, SES should be directly related to centrality. Having established an idea of how centrality differs across groups, I will then proceed to determine which variables predict proximity prestige. Variables identified from the discussion in Chapters 2 and 5 will be used to assess how they relate to high-status youth. The predictors include measures developed from deviant (substance use, underage sex, property crime, poor school performance, and violence/aggression) as well as socially desirable attributes (e.g. attractiveness, physical development, athletic team membership). Step #3. Following the estimation of relationships during adolescence, I will then con- duct a longitudinal analysis where I use proximity prestige (measured during adolescence) as a predictor for deviance, financial success, and mental/physical health outcomes in adult- hood. It is at this step that I will begin to assess how centrality can affect an individual’s trajectory and suggest how it could influence one’s life well-after adolescence. To accomplish this, I will run multiple regressions with health outcomes for depression, anxiety, criminality, and substance abuse, as well as socioeconomic status as the outcome. Predictor variables will be the control variables from previous analyses and proximity pres- tige.

84 Chapter 7

Methods Data Source

This dissertation uses the National Longitudinal Study of Adolescent to Adult Health (Add Health) dataset (Harris, 2009). This initiative began by selecting 26,666 schools across the United States. These schools were stratified by region, urbanization, size, type, and racial composition. Researchers then whittled down the sampling frame to 132 nationally representative schools. Beginning in the 1994-1995 school year, every student in these 132 schools were asked to fill out an in-school, self-report questionnaire that asked about the student’s, family, peers, and self. There were 90,118 students that completed this survey (Harris, 2009). Following this stage, a portion of the in-school participants — and the participants’ caregivers — were asked to complete an additional follow-up survey in the home. This stage of data collection (Wave I in-home survey) yielded interview information on 20,745 youth and 17,700 primary caregivers. The goal of this stage of data collection was to gather more information on the participants’ home-life and activities. Wave I also presented the opportunity to get the caregivers’ perspectives on the youth. Participants of the in-home surveys were asked to complete another interview one year later (Wave II) and 14,738 students agreed to do so. Because the participants were only a year older at the time of the second interview, it was assumed that they had not changed much (many were still in the same school and had similar attributes from Wave I). Therefore, the interviews remained largely the same and measured similar concepts — e.g. home-life, social interactions, regular activities. The reason for the drop in participants from Wave I to Wave II is due to the researchers’ exclusion of participants who were not in school at the time of the Wave II interview. Those who graduated high school in the time between Wave I and II interviews were not asked to participate in the second interview. Wave III interviews took place approximately 5 years after the Wave II interviews with

85 15,197 respondents agreeing to participate. Because the participants were young adults at this point, the survey was changed to reflect age-appropriate questions that pertained to their life stage. For example, interviewers asked participants questions that explored their professional life, military service, and marital status. Six years after Wave III interviews, researchers conducted a final interview with partici- pants. Interviewees were now 24–32 years of age at the time of the survey. The goal of Wave IV was to explore how events and attributes in adolescence can affect individuals’ trajecto- ries over the life course. This wave also included biomedical and psychological assessments to assess how habits from youth can persist into young adulthood. This dataset was chosen for three reasons. First, the data are nationally representative. Because the dissertation is attempting to explore adolescent behavioral mechanisms that pertain to Western society, a sample that is representative of a large portion of the target population is needed. Second, the dataset included network measures. Youth were asked to identify up to 5 of their best friends of each sex. By having this information, researchers can construct every participant’s social network by linking who the adolescent nominates and who nominates them. Lastly, in addition to the availability of social network data, the in-school portion of the Add Health study measures the quality of participants’ family life, peers’ beliefs, recreational activities, health status, and - most importantly - deviant behavior. To conduct the analyses, I used three different sections of the Add Health dataset: Net- work Variables, Self-Report Survey, and In-Home Survey. In order to gain access to the more robust, restricted data, I was added as a user to the account of the principal investigator — who is the chair of this dissertation. Social Centrality

To this point, this dissertation has reviewed the relevant literature regarding what pop- ularity is, how it relates to status in an evolutionary perspective, and how these concepts relate to deviance. These reviews were given in order to provide context for relationships

86 relating to social centrality, deviance, and well-being outcomes in adulthood. It is here that the concept of social centrality is explicitly defined. Additionally, this section serves as an in- troduction to basic social networking terminology and describes the mathematical properties of this dissertation’s social centrality measure, proximity prestige. Social Network Terminology. To understand social network analysis measures I must first describe some of the terminology. This research begins with two key concepts — nodes and edges. A node is a data point in the network. Nodes can be anything that exists within a network. For this work, the network for a given respondent is all of the student’s peers in his/her school. Each student in the school is a node. An edge is a connection between two nodes. To create this network, the Add Health study asked each respondent to name their five best male and five best female friends. By combining all of the responses from all of the participants, researchers were able to map the network for each school and individual. The presence of an edge between two nodes denotes a friend nomination from one node to another. To avoid confusion when calculating different network estimates, the social network literature has designated the term ego to represent the individual of interest when calculating his or her network (Hanneman & Riddle, 2005; Wasserman & Faust, 1994). The network is composed of the other participants who are called alters. For this work, the ego is the respondent and alters are the ego’s peers in his/her school. To better understand this concept, consider Figure 7.1. Each circle in this diagram is a node and represents a person in a network. The arrows represent edges — or friend nominations from one individual to another.39 One can see a direct connection between nodes C and B. This denotes that actor C nominated B as his friend, however, C did not reciprocate this friendship. The size of the nodes has meaning as well. Node A is the largest. This denotes that she received more friend nominations than anyone else. Actually, every single person in the network nominated her. It is also important to note that measurements that estimate individual attributes switch 39Take note that the diagram uses arrows. This is because the network is directional. The measure for popularity uses the in-degree network or the nominations received by egos from alters.

87 Figure 7.1: Simple Network Diagram

who is an ego or an alter based on which node is being measured. For example, when Node A’s friend nominations are calculated, Node A is the ego and all other nodes are considered alters. Also, when Node B’s nominations are calculated, B is the ego while all other nodes are alters. This rule extends to all directional, network-based measures, including the one that this work uses. Network analyses use friend nominations, which have been established as a different construct from this dissertation’s definition of popularity. Luckily, social network analyses have developed to go beyond mere raw counts of friend nominations. Schools of measures can now be calculated to estimate an individual’s influence within a network. By utilizing the work done in social networking, researchers are now able to calculate different types of summary estimates based on the location of an individual in a network and how many connections s/he has (Hanneman & Riddle, 2005; Wasserman & Faust, 1994). One of the chief classes of measures that has been developed is centrality. Someone who is the center of a network will be visible, recognized, and also have status and influence (Andrews, Hanish, and Santos, 2017; Zhang et al., 2014; see Chapter 2). Which is to say that someone

88 who is socially center is similar to being popular, while someone at the periphery of the network is more similar to being unpopular by comparison. The specific centrality measure that is used for this project is called proximity prestige (Lin, 1976). It is a measure that uses the in-degree network (number of friend nominations received by an ego from alters in the network). Proximity prestige is composed of two concepts — influence domain and geodesic distance. Both are discussed below. Influence Domain. Influence domain is an estimate that represents how many alters can directly and indirectly reach the ego. Returning to Figure 7.1, an example of a direct connection would be from Node C to A. There is a direct arrow (edge) from one node to another. An indirect connection would be from C to D. Node C did not directly nominate Node D, however, C did nominate Node B and Node B nominated D. Put more simply, influence domain represents the total number of connections — both direct and indirect — that an ego has in their network. This is an important distinction from raw counts of friend nominations. Even if an ego is nominated by only a few friends, the ego’s influence domain will increase if those friends are well-connected. The major flaw of using raw counts of friend nominations as a proxy for popularity is highlighted here. It does not account for the influence an individual can have by being visible (Cillessen & Marks, 2011). If someone is visible, they do not affect just their friends. They affect those that they do not associate with because of their status and position. Incorporating indirect connections allows for a more robust description of a social network. And those with status should be able to reach (or be reached) through indirect means of association. Another reason influence domain is an important concept is because it gives information on an ego’s network. The Add Health dataset is comprised of multiple, closed networks. Participants were selected for the study via their school being selected. The schools were located across the country. Therefore, it is likely that participants’ social networks differ in size and quality as a function of the school’s geographic location. As will be shown later in

89 this section, influence domain can be used to standardize a centrality measure so that the score is comparable across social networks (Wasserman & Faust, 1994).

X Influence Domain(Ii) = βji (7.1) j

Equation 7.1 captures the sum of all the alters who are able to reach the ego — directly or indirectly. βij, in this equation, is a binary outcome of whether the alters (j ) can reach the ego (i) where 0 = unreachable and 1 = reachable. Conceptually, influence domain designates an ego and determines if an alter can reach them. If the alter can, it adds one to the total. If not, it adds zero. It does this for all alters in the network. Thus, larger values for influence domain mean that the ego is connected to more alters in her/his network. Geodesic Distance. Influence domain, on its own, is only an estimate of the number of people that know, and can be influenced by, the ego. It does not discern between direct and indirect connections. However, if geodesic distance is calculated, or how proximate the alters in the influence domain are to the ego, a better sense of the network and the ego’s place in it can be ascertained.

X d(nj, ni) Geodesic Distance = (7.2) I j i

Geodesic distance is a measure of the average steps that it takes for every alter in the influence domain to reach the ego (Wasserman & Faust, 1994). The numerator of Equation

7.2 represents the sum of all the steps from the ego (ni) to every alter (nj) that can reach the ego. The value is then divided by the ego’s influence domain, giving the average distance it takes for an ego to reach an alter. Consider Figure 7.1 again. There is a direct connection between Nodes E and A. Therefore, the geodesic distance (GD) from E→A is 1 step. The GD for Node E to reach Node B is 2 steps (E→A→B). And the GD for Node E to Node D is 3 steps (E→A→B→D). There is no score given for Node E→C because there is no path present. The distances for all of the connected nodes are summed — 1 + 2 + 3 — and

90 divided by the number of alters in Node E’s influence domain — 3. This calculation gives

1+2+3 6 the average distance for a node to reach E: 3 = 3 = 2. If there are multiple paths for an alter to reach the ego, the shortest path is chosen and all other paths are ignored. Proximity Prestige. These two measures above, on their own, are a bit crude. The influence domain is just a more sophisticated raw count of friend connections that includes both direct and indirect connections. And the geodesic distance does not consider alters who are in the ego’s network but cannot reach the ego (for example, an isolated clique that exists in the school but the members of the clique only associate with their group members). This measure also is difficult to compare across networks of differing size — a quality existent in the Add Health data. However, influence domain and geodesic distance can be used in tandem to create the centrality measure, proximity prestige. Lin (1976) suggested that researchers can calculate the ratio of the proportion of alters who can reach the ego (based on network size) by the average distance of alters to the ego (Wasserman & Faust, 1994). In other words, this measure takes the proportion of alters that an ego can reach in her/his network, then divides that proportion by the average distance an ego is from all alters. This measure would give a good estimate of the ego’s influence within the network.

Ii (g−1) Proximity Prestige(PPi) = (7.3) P d(nj ,ni) Ii j

Equation 7.3 shows the final equation for proximity prestige. The measure takes the ego’s influence domain (Ii) and standardizes it by dividing the estimate by the total number of individuals in the network (g − 1).40 The numerator allows the estimate to be compared across various sizes of networks. The denominator is the measure for geodesic distance. Note that since it is in the denominator, the further away an alter’s connection is from the ego, the less it is weighted. In other words, direct connections (1 step) are weighted more heavily

40One can think of this measure as taking all the peers that a youth influences directly and indirectly and dividing that estimate by the total number of youth in his or her school.

91 than a close, indirect connection (e.g. 3 steps) which will be weighted more heavily than a distant, indirect connection (e.g. 6 steps). If an ego has a large influence domain in relation to his/her entire network and has many close connections, the proximity prestige score will be large. Mathematically, the range of values for proximity prestige is from 0 to 1. A score of “0" denotes that the youth has no connections to any peers. A score of “1" signifies a direct connection between the ego and every single alter in the network. Scores in between have combinations of direct and indirect connections as well as a lack of connections. Higher scores denote that the ego is central to the social network while lower scores show that they are on the periphery. To give a more illustrative example of how proximity prestige functions, consider Figure 7.2 below. Inside each node is the number corresponding to a hypothetical participant. The values in the parentheses represent the proximity prestige (prestige) of the corresponding node. As can be seen, Nodes 2, 4, 5, 6, and 10 all have values of zero due to having no nominations. The prestige values increase from 9 to 8 to 7 due to the compounding indirect connections as the nodes progress toward the center of the network. Even though Nodes 1 and 3 both have three direct connections, Node 1 has a higher prestige value due to the indirect connections received from Nodes 10, 9, 8, 4, 5, and 6. While Node 3 is well connected, Nodes 4, 5, and 6 give him no access to indirect connections — suggesting that Node 3 has less influence, status, and visibility when compared to Node 1 (who is connected to every node in the network). Also note that while Node 1 is connected to every node in the network, not all connections are direct. Nodes are as many as 4 steps away. Therefore, Node 1’s proximity prestige value is .050. This measure, while composed of friendship nominations, is a valid strategy for capturing the concept of popularity because of the nature of adolescence. Because of the influence that peers have on one another, and adolescents’ desire to fit-in, youth, generally, want to be associated with popular peers. Therefore, youth may be more likely to name a popular peer as their friend to increase their standing among others - even if that youth is not

92 (Zhao et al., 2015, p. 179)

Figure 7.2: Proximity Prestige in a 10-Node Network

necessarily likable or friendly. Furthermore, the key critique brought on this measure by Cillessen and Marks (2011) is that centrality measures are not as stable over time as a sociometric popularity measure. However, because this work only considers popularity during adolescence — who are largely homogeneous in their valuing of popularity and initiating in “adult-like" behavior — this critique is muted. In addition to the nature of adolescence, by considering direct and indirect connections in the network, the quality of the friends is essentially weighted. If an ego is directly connected to a number of isolated alters, that ego will have a low prestige score. However, if one of those alters who is connected to the ego is nominated by a number of other alters in the network, the ego’s prestige will increase due to the indirect ties that the popular alter provides. This quality of the measure allows for a person who is associated with well-connected people to be identified and assessed (Zhang et al., 2014). If popular, yet unlikable, youth are connected to anyone in the network, it is most likely other youth of status. Therefore, their proximity prestige will rise by their association with influential peers. Proximity prestige also captures the idea that popular youth do not have to directly interact with others in order to influence them. High-status youth have reputations. Other youth will talk about them due to their reputation. Combine this with popular youth’s

93 visibility, and one can see how direct interactions are not needed in order for popular youth to influence their peers. Due to these reasons, it is hypothesized that many of the relationships discussed previously involving popularity will apply to proximity prestige as well. To further reinforce this viewpoint, Rodkin and colleagues (2000; 2006) identified two sets of popular children. The first group “model" boys were popular, academically successful, physically competent, high on affiliative behaviors, and low on teacher-rated aggression. The “tough" boys were popular, low on affiliative behaviors, aggressive, and physically competent. Both groups were found to be socially center using a social network measure (Rodkin, Farmer, Pearl, & Van Acker, 2000, 2006) and perceived by peers as “cool" (Rodkin et al., 2006). Correlates with Popularity

The literature previously summarized lays out much of what is known to be correlated — if not causally linked — with popularity. Studies have shown a significant relationship between popularity and socioeconomic status (SES), involvement in sports for males, cheer- leading for females, and attractiveness (Rose et al., 2011; see Chapter 5 for a review). These measures, all of which were measured at Wave 1, are discussed below. To measure SES, I used a variable from the Parent survey that asked parents to denote how much money (before taxes) the household brought in during 1994 (the year of the interview). Respondents were reminded that they were to add all sources of income including, but not limited to, employment, disability, welfare, and dividends from every member of the household. Income was recorded as the raw dollar amount from $0 to $999,000. Involvement in sports and cheerleading were measured by having students note which extracurricular activities they participated in from a list of possibilities. Respondents who answered “yes" to cheerleading/dance team were coded as a 1 and non-participants were a 0. Separate from cheerleading/dance team, an additive scale was created for all other sports teams. Included in this scale are baseball/softball, basketball, field hockey, football, ice hockey, soccer, swimming, tennis, track, volleyball, wrestling, and other sport. All scores were coded as 1 = participant and 0 = non-participant creating an index where the higher

94 score a participant had, the more sports s/he participated in. Physical attractiveness was measured by asking the interviewer to rate the respondent’s level of attractiveness in relation to his or her peers of the same sex. The interviewer used a 5-point Likert scale from 0 being very unattractive to 4 being very attractive and rated the participants’ attractiveness at the time of the interview. Wave 1 Predictor Variables

Because the literature describing the relationship between popularity and different types of rule-breaking is mixed, I divide rule-breaking into six different categories: Substance/alcohol use, underage sex, poor academic achievement, property crime, and violence/aggression. Each item in this section came from Wave 1 of the Self-Report Survey portion of the Add Health study and is discussed below. Substance and Alcohol Use. Substance/alcohol use was measured using three vari- ables from the Add Health dataset. The survey asked respondents how often they had used alcohol, tobacco, or marijuana over a specified time span (tobacco use was limited to number of cigarettes smoked in the past 30 days; alcohol use was limited to drinks in the past year; and marijuana was how many times did the youth smoke in his/her lifetime). Each response was coded as the raw number of times youth used the respective substance. Underage Sex. Underage Sex was measured by drawing on responses to a single item measured at Wave 1. The conceptualization of this measure can also be thought of as having sex while in high school — as the sample dropped all respondents who were 19 years old or older and all respondents were in high school at the time Wave 1 data were collected.41 This measure asked participants if they had ever engaged in sexual intercourse in their life. Answers were coded into a dichotomous outcome (0 = No, 1 = Yes). Respondents who reported that they were married were not asked this question during the interview. 41The oldest group in the sample, 18-year-olds, do legally count as adults in most states. Therefore, if they have sex, it would, legally, not be underage. They are included in this sample for two reasons. One, if 18-year-olds are removed, many seniors will be removed from the sample. And two, according to Moffitt’s conceptualization of the maturity gap, an 18-year-old would still be feeling the gap’s effects. Furthermore, if in high school, many, if not all, of the newly declared adult’s peers — who make up their social network — would be in high school and/or underage. Therefore, the theoretical implications still apply.

95 Therefore, married respondents were added to the group reporting that they had had sex. Poor Academic Achievement. Poor Academic Achievement was measured by using three variables. The first variable asked respondents “How many times have you skipped/did you skip school for a full day without an excuse?" Responses to this measure were recorded as a raw count of the number days. The other two measures asked if students had trou- ble “getting along with teachers" and “getting your homework done?" Both variables were measured on a 5-point Likert scale from 0 = never to 4 = everyday. The second and third measures were initially combined as an index. However, while these two constructs are re- lated, they represent different facets of what influences popularity. Therefore, they were put in the models separately. In addition to these behavior measures of academic achievement, I added in a fourth variable for grades the participants earned. The Add Health survey asked respondents to report their grade for the most recent grading period for the following subjects: English, Math, History and Science. Responses were coded on a 4-point scale from D/F = 0 to A = 3. Scores for each class were then added to create an additive index of grade performance in school. These scores were then divided by the number of respondents’ classes to create a pseudo-grade point average. Property Crime. Property Crime’s measurement consists of an index combining seven variables of various property offenses. The survey asked respondents how many times in the last 12 months did s/he “paint or graffiti signs," “deliberately damage property," “take something from a store without paying for it," “drive a car without its owner’s permission," “steal something worth more than $50," “steal something worth less than $50," and “go into a house or building to steal something." All responses were measured by use of a 4-point Likert scale ranging from 0 = never to 3 = 5 or more times. Responses from the seven variables were added together to create an index for the construct “property crime." Violence/Aggression. The final deviance category is violence/aggression. This con- struct was measured by creating an additive index from four variables. These variables

96 consisted of asking the respondent how many times in the past 12 months did s/he “get into a serious fight," “hurt someone badly enough to need bandages or need care from a doctor or nurse," “use or threaten to use a weapon to get something from someone," and “take part in a fight where a group of your friends was against another group." The violence/aggression variables used the same 4-point Likert scale as the property crime measures: 0 = never to 3 = 5 or more times. The variables responses were combined by adding into an index. Wave 1 Controls

Age. Age was measured in two ways: biological age and grade in school. Respondents were asked to denote what grade they were in while taking the survey. To calculate the biological age, I had to convert the respondents’ birth dates from month and year to months. I performed the same conversion for the month/year of the interview. Finally, I subtracted the birth date from the time of the interview to compute the respondents’ age in months. I then divided this figure by 12 and rounded to the nearest integer to have the biological age in years. In addition to this measure, I used the respondent’s identified grade. While biological age is a valid measure for development, grade level is a valid measure of the context associated with age. If an individual is in 8th grade, he or she will associate with 8th-graders. Because this work is focusing on peer influences, it is important to consider the context of one’s peers and grade allows me to accomplish this. Older participants were restricted in order to capture respondents who represent ages that are typically in middle or high school and to better relate to theoretical concepts laid out by Moffitt (1993). The youngest respondent was 11 years old and I removed all respondents whose age was 19 or older. This caps the maximum age for the sample at 18. Race. The Add Health Survey asked students to note what race they identified as. Options were White, Black, Asian/Pacific Islander, American Indian/Native American, and Other. Respondents could also select Hispanic or not as their ethnicity. Variables for race were recoded into a dummy variable where 0 = Non-White and 1 = White.

97 Biological Sex. Biological sex, was taken from the survey asking respondents to identify their biological sex. Responses were coded as 0 = Female and 1 = Male. Machiavellianism. Because having a nuanced, sophisticated understanding of social relations and an ability to manipulate others increases an individual’s likelihood at having status, it is prudent to control for characteristics that high-Machiavellian actors have. While the Add Health project did not collect data specifically on this personality cluster, there are questions that relate to the construct. Interviewers asked students how true the following statements were of the respondent: “I can do a good job of ‘stretching the truth’ when I’m talking to people" and “I can usually get people to believe me, even when what I’m saying isn’t quite true." These questions were measured on a 5-point Likert scale from 0 = not true to 4 = very true. The two variables were then summed and the sum was divided by 2. This created an average across the variables making the results more parsimonious. Machiavellianism is generally assessed by the use of the MACH-IV (Christie & Geis, 1970). The MACH-IV is a 20-item assessment that covers 3 categories: manipulative tac- tics, a cynical view of human nature, and disregard for conventional morality. While it would be preferred to use this checklist to determine Machiavellianism levels in the sample, these measures were not available in the data. The two measures above tap into the third category.42 And while this is not a complete and thorough assessment of Machiavellianism, it can suggest relationships that can be explored in future studies. It should be noted that these measures were taken at Wave 3. It is not a substantive issue to include these variables with others from Wave 1. Personality traits are incredibly stable over the life-course (Beaver, 2016; Gottfredson & Hirschi, 1990; Wright et al., 2014). Therefore, it is reasonable to assume that the level of this measure is unlikely to vary within individuals from adolescence to adulthood. Additionally, much like other aspects of person- 42One question from the third category is “Sometimes one should take action even when one knows that it is not morally right." This question is used to capture one’s willingness to knowingly deceive others, as do the two measures used in this dissertation.

98 ality, the rank-order between-person variation on the measure is also unlikely to fluctuate substantially over this time frame (Anusic & Schimmack, 2016; Blonigen, Carlson, Hicks, Krueger, & Iacono, 2008; Struijs et al., 2020; Wright et al., 2014). Wave 3 Outcome Variables

To see how popularity affects individuals later in the life-course, I will include outcome variables from Waves 3 and 4. Respondents were between the ages of 18 and 26 at Wave 3 (with 15,170 respondents) and between 24 and 32 years of age at Wave 4 (with 14,032 respondents). Because the maturity gap no longer exists into adulthood, deviance should no longer be common nor lauded by a majority of the group. Instead of focusing principally on deviant behavior, I examine popularity’s influence on different types of health outcomes in adulthood. Depression. Two variables were used to measure depression. The first measure, which is dichotomous, asked “Has a doctor, nurse or other health care provider ever told you that you have or had: depression?" Responses were coded as 0 = No and 1 = Yes. The other measure for depression asked respondents “How often was the following true during the past seven days? You felt depressed." Responses were coded into a 4-point Likert with 0 = never or rarely and 3 = most of the time or all of the time. Both variables were included as one requires a clinical diagnosis while the other is self- diagnosis. While the clinical diagnosis is most likely more accurate, the second measure will include those who have feelings of depression but either will not or cannot visit a mental health professional. Aggression. While aggression may not be as highly praised in adulthood as it is in adolescence, it can still be a tool to maintain status. Furthermore, adolescents who found aggression to be a useful tool may be more likely to use it into adulthood. There are two measures of aggression used from Wave 3 — one for overt aggression and one for relational aggression. The overt aggression measure asked respondents “How often is the following statement

99 true of you? I am aggressive." Participants marked the best option from a 7-point Likert scale that was coded 0 = never or almost never true to 6 = always or almost always true. To measure relational aggression (which was, unfortunately, not available in Wave 1) respon- dents were asked “Do you agree or disagree that you have often said something bad about a friend behind his or her back?" Responses were coded 0 = strongly disagree to 4 = strongly agree. Alcohol Use. Alcohol use, while legal for adults, is looked down on by adults when not done in moderation. For instance, drinking while at work is not valued because it can lower one’s job performance and increase the likelihood of having one’s job terminated. To measure alcohol abuse, respondents answered the question “Over the past 12 months, how many times were you drunk at school or work?" Response options were coded 0 = Never, 1 = Once, 2 = Twice, 3 = 3 or 4 times, and 4 = 5 or more times. Socioeconomic Status. Socioeconomic Status (SES) was measured via four variables: educational attainment, having a savings account, receiving food stamps, and taking welfare. Educational attainment was measured by respondents noting the highest grade level that they had successfully completed. Answers were coded from 0 = 6th Grade up to 16 = 5 or more years of graduate school. Both the receipt of food stamps and welfare were dichotomous variables. Respondents were asked to answer “During any part of 2000/2001 did you receive income from [food stamps]" or “[AFDC (Aid to Families with Dependent Children), public assistance, welfare, or a state TANF program]." Answers were coded 0 = No and 1 = Yes. Finally, respondents answered “Do you have a savings account?" Responses were coded the same as welfare receipt. Wave 4 Outcome Variables

Depression. Measures for depression at Wave 4 are identical to the measures at Wave 3. Anxiety. While there were no measures for aggression at Wave 4, there were measures for anxiety. For this concept, three measures were used. The first, similar to the dichotomous

100 measure for depression, had respondents answer whether “...a doctor, nurse or other health care provider ever told you that you have or had: anxiety or panic disorder?" Responses were coded 0 = No and 1 = Yes. The second and third questions asked respondents how they felt, generally. Participants answered “How much do you agree with each statement about you as you generally are now, not as you wish to be in the future? I am relaxed most of the time]" and the same question asking if “[I get stressed out easily]." Responses were put on a 5-point Likert scale with the first question having 0 = strongly agree to 4 = strongly disagree, while the second question was reverse coded to keep interpretation uniform across measures. Alcohol Use. The same measure for alcohol use at Wave 3 was not available at Wave 4. In its place, a question asking respondents “Have you ever given up or cut down on important activities that would interfere with drinking like getting together with friends or relatives, going to work or school, participating in sports, or anything else?" was used. Responses to this question were coded 0 = No and 1 = Yes. Socioeconomic Status. The measure for educational attainment asked “What is the highest level of education that you have achieved to date?" The coding for this measure is 0 = 8th grade or less up to 12 = completed post baccalaureate professional education. In addition to this measure, respondents were asked “Thinking about your income and the income of everyone who lives in your household and contributes to the household budget, what was the total household income before taxes and deductions in 2006/2007/2008? Include all sources of income, including non-legal sources." Responses were coded 0 = less than $5,000 up to 11 = $150,000 or more.

101 Chapter 8

Results Descriptive Statistics

To begin this section, I will discuss the shape of the social centrality measure — proximity prestige (prestige). Table 8.1 displays the summary statistics for this measure.

Table 8.1: Proximity Prestige Summary Statistics

N Mean SD Min Max Skewness Kurtosis Proximity Prestige 5854 0.156 0.069 0.000 0.774 0.716 7.672

Looking at the table, it can be observed that the distribution of the key variable is both skewed and leptokurtic. The mean is .156 and standard deviation .069. With the possible values of the variable ranging from 0 to 1, it seems that most individuals in the sample have a relatively low amount of proximity prestige. However, there is a small portion of the sample with much higher values of influence (the highest value being .774). This trend in the data is expected. Popularity is seen as a coveted commodity. Those who have it want to keep it and for it to stay exclusive. The fact that few are seen as popular gives the status more power. Centrality should act in a similar fashion. Even though it is skewed, when looking at the histogram of the variable (Figure 8.1), the distribution is close to bell-shaped. There are very few teens with high levels of prestige. But if the high-level youth are excluded, the shape is largely normal — excluding also the large number of youth who have a proximity prestige score of 0.43 Table 8.2 displays the summary statistics for all other variables in the analysis from Wave 1. The chief purpose of displaying this table is to allow reference when interpreting 43The large amount of zeros scored on proximity prestige may be measurement error. Some youth may have reported having no friends due to being tired of or apathetic toward the survey. Other youth may have genuinely not had any connections. This topic will be further discussed in the Limitations section.

102 Proximity Prestige 800 600 400 Frequency 200 0 0 .2 .4 .6 .8

Figure 8.1: Proximity Prestige Histogram

figures later in the analysis. Briefly, however, it can be seen that the normality assumption is met for a majority of the variables. Three exceptions — annual household income, alcohol use, and marijuana use — were expected. Household income in the population is skewed. Also, alcohol use should be skewed as not all youth can readily obtain alcohol. Therefore, only those who are motivated to procure and learn how to obtain it (e.g. those seeking to increase or maintain their status) are likely to use alcohol. This is also true, to an extent, for marijuana use. However because it is an illegal drug for all ages (and was illegal in all 50 states at the time of data collection), it may not have the same appeal as alcohol. Because alcohol is permissible to individuals at the age of 21, it may be seen as a type of rite of passage. Therefore, it is an acceptable, adult activity. Marijuana use is illegal regardless of the age, preventing it from becoming a symbol of adulthood.44

44It would be interesting to have a sample of youth during current day. Because of the national movement spearheaded by states to legalize marijuana, this delineation between alcohol and marijuana may lessen.

103 Table 8.2: Descriptive Statistics for Analysis Steps #1 & #2

N Mean SD Min Max Skewness Kurtosis Control Variables Age Wave 1 6796 15.806 1.557 11.000 18.000 -0.228 2.007 Grade Level 6796 9.423 1.555 7.000 12.000 -0.039 1.947 GPA 6796 0.935 0.151 0.000 1.000 -2.994 14.230 White = 1 6796 0.685 0.465 0.000 1.000 -0.795 1.632 Male = 1 6796 0.455 0.498 0.000 1.000 0.179 1.032 Machiavellianism 6796 1.676 1.230 0.000 4.000 0.230 2.006 Parent Educational Achievement 6796 5.724 2.292 0.000 9.000 -0.349 2.056 Household Income 6796 48.023 51.931 0.000 999.000 8.902 129.826 Correlates with Popularity 104 Sports 6796 1.139 1.460 0.000 12.000 2.396 13.417 Cheer/Dance = 1 6796 0.103 0.304 0.000 1.000 2.617 7.851 Physical Attractiveness 6796 2.589 0.863 0.000 4.000 -0.164 3.145 Physical Development 6796 3.392 0.804 1.000 5.000 0.090 3.307 Deviance Alcohol Use 6796 2.316 5.516 0.000 90.000 6.629 71.809 Tobacco Use 6796 3.507 8.702 0.000 99.000 2.563 9.198 Marijuana Use 6796 8.635 50.020 0.000 900.000 11.258 157.387 Had Sex = 1 6796 0.332 0.471 0.000 1.000 0.712 1.507 Significant Other = 1 6796 0.378 0.485 0.000 1.000 0.504 1.254 Skipped School 6796 1.354 5.381 0.000 99.000 9.364 124.767 Trouble with Teachers 6796 0.833 0.917 0.000 4.000 1.358 4.993 Trouble with Homework 6796 1.150 1.037 0.000 4.000 0.873 3.281 Property Crime 6796 1.360 2.535 0.000 21.000 2.842 13.013 Aggression 6796 0.895 1.599 0.000 12.000 2.694 12.209 The only other variable that is non-normal is “skipping school." This ratio variable ranged in responses from 0 to 99. It is not surprising that this variable is skewed. Most teens do not skip school — approximately 13% do. And the ones that do skip have multiple risk factors for deviance — e.g. low parental education, low investment in school, and a high amount of unsupervised time spent with peers (Henry, 2007). It is also likely that some participants could not recall the exact number of days and selected the maximum, or were getting bored with the survey and picked the largest number of days to speed through the measure. Analysis Step #1

The first step in this analysis is to determine what factors predict centrality in the sample at large. Column 1 of Table 8.3 shows the regression of proximity prestige on the control variables and correlates with popularity when considering the entire sample. The control variables of column 1 are all in the predicted directions, save one. The measure for grade level actually shows that underclassmen and middle schoolers, generally, have more proximity prestige than do older youth. This relationship is most likely a result of the data than an indicator that younger youth are more socially central than older youth. The Grade Level variable does not compare the proximity prestige of youth across an entire school — e.g. lumping all students in one network and determining centrality. Rather, a better way of conceptualizing this measure is to think of it as the average amount of prestige that individuals in a grade in their school have. During data collection, students were given a list of individuals in their grade to choose from as friends. So what this measure suggests is that prestige is more exclusive and harder to obtain the older that youth in the sample get. Before moving to Column 2 it is worthwhile to discuss the statistical meaning of these coefficients. The measure of proximity prestige is metric and ranges from 0 to 1. If we consider one of the most salient predictors in Column 1 — Cheer/Dance membership — we can see that being a member of this group shows an average increase in one’s proximity prestige level by .016. Considering that the mean prestige score across the sample is .156, one can observe that being a cheerleader or on the dance team raises a member’s status by,

105 Table 8.3: Regression of Proximity Prestige on Controls and Predictors — Full Sample

Controls All Variables Control Variables Age Wave 1 0.002 [0.001] 0.002 [0.001] Grade Level -0.005∗∗∗ [0.001] -0.005∗∗∗ [0.001] GPA 0.032∗∗∗ [0.006] 0.031∗∗∗ [0.006] White = 1 0.034∗∗∗ [0.002] 0.032∗∗∗ [0.002] Male = 1 -0.004 [0.002] -0.002 [0.002] Machiavellianism -0.002∗ [0.001] -0.001 [0.001] Parent Educational Achievement 0.001∗∗ [<0.001] 0.001∗∗ [<0.001] Household Income >-0.001 [<0.001] >-0.001 [<0.001] Correlates with Popularity Sports 0.007∗∗∗ [0.001] 0.007∗∗∗ [0.001] Cheer/Dance = 1 0.016∗∗∗ [0.003] 0.015∗∗∗ [0.003] Physical Attractiveness 0.006∗∗∗ [0.001] 0.006∗∗∗ [0.001] Physical Development 0.001 [0.001] <0.001 [0.001] Deviance Alcohol Use 0.001∗∗∗ [<0.001] Tobacco Use <0.001∗∗∗ [<0.001] Marijuana Use >-0.001 [<0.001] Had Sex = 1 0.004 [0.002] Significant Other = 1 0.007∗∗∗ [0.002] Skipped School -0.001∗∗∗ [<0.001] Trouble with Teachers 0.004∗∗∗ [0.001] Trouble with Homework -0.003∗∗∗ [0.001] Property Crime -0.001∗∗ [<0.001] Aggression -0.001∗ [0.001] Constant 0.081∗∗∗ [0.014] 0.095∗∗∗ [0.014] Adjusted R2 0.12 0.14 N 5854 5854 Standard errors in brackets ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

on average, 10% — although, as will be seen later when gender is discussed, that may change based on the group being observed. Column 2 of Table 8.3 shows the regression of proximity prestige on all control and predictor variables at Wave 1. As predicted, alcohol and tobacco use were positively related to proximity prestige — although the effect size is minuscule. This reinforces the idea of Moffitt’s maturity gap. Both smoking and drinking alcohol are legal once the youth reach a

106 certain age, thereby defining it, legally, as an adult activity. If they are wanting to assume adult roles, use of adult-labeled recreational items is an effective (and fun) place to start.45 The predictor with the strongest effect is having a significant other. This relationship of social central youth not only having access to dating prospects but also taking advantage of this access is consistent with evolutionary theory. Being central gives you more access to high-status, highly-valued mates. Also, the absence of having a significant other could hurt your proximity prestige score. One surprise from this initial analysis is that one of the most powerful predictors in the entire model is GPA (.031). While youth’s grades generally drop when they move from elementary to junior high school (Eccles & Roeser, 2009; Finger & Silverman, 1966), unintelligent youth are not seen as popular (Cillessen & Rose, 2005). Those who can maintain success in school while obtaining social success are the most influential/connected youth. Analysis Step #2

Centrality and Age. Dividing the sample into groups can inform researchers on which categories are driving relationships. The first delineation I make in the analysis is between younger and older adolescents. Table 8.4 shows the regression of proximity prestige on all the predictor and control variables. Column 1 composes of youth 14 years and younger, while Column 2 reports the figures for the group 15 and older. This age was used as the cutoff for two reasons. One is that it is the middle age point in the data. The range of participants’ age is 11–18. If the sample is divided as it is here, the younger group has a range of four years, as does the older group. In addition to just this practical divide from above, puberty in humans lasts from, on average, age 11–17 (Kail & Cavanaugh, 2010). The divide for the ages is roughly at the peak of pubertal changes. A key trend found in this table has to do with substance use. As predicted, alcohol use is significantly and positively associated with proximity prestige in both age groups. More 45This is in addition to the fact that adolescent brains are more attuned to sensation seeking (Pinker, 2002). While youth may be driven to use these substances by a maturity gap or delinquent subculture, they likely continue using these due to the feelings the drugs give the youth.

107 Table 8.4: Regression of Proximity Prestige on Predictors — by Age

14 and Younger 15 and Older Deviance Alcohol Use 0.002∗∗∗ [0.001] 0.001∗∗∗ [<0.001] Tobacco Use <0.001 [<0.001] <0.001∗∗∗ [<0.001] Marijuana Use >-0.001 [<0.001] >-0.001 [<0.001] Had Sex = 1 -0.005 [0.008] 0.004∗ [0.002] Significant Other = 1 -0.004 [0.005] 0.009∗∗∗ [0.002] Skipped School >-0.001 [0.001] -0.001∗∗∗ [<0.001] Trouble with Teachers 0.003 [0.002] 0.005∗∗∗ [0.001] Trouble with Homework -0.006∗∗ [0.002] -0.002∗∗ [0.001] Property Crime -0.002 [0.001] -0.001∗ [<0.001] Aggression 0.002 [0.002] -0.003∗∗∗ [0.001] Control Variables Age Wave 1 0.010∗ [0.005] 0.003 [0.002] Grade Level -0.001 [0.005] -0.006∗∗∗ [0.001] GPA 0.039 [0.029] 0.030∗∗∗ [0.006] White = 1 0.030∗∗∗ [0.005] 0.032∗∗∗ [0.002] Male = 1 -0.004 [0.005] -0.002 [0.002] Machiavellianism -0.001 [0.002] -0.001 [0.001] Parent Educational Achievement 0.001 [0.001] 0.001∗∗ [<0.001] Household Income >-0.001 [<0.001] <0.001 [<0.001] Correlates with Popularity Sports 0.006∗∗∗ [0.001] 0.008∗∗∗ [0.001] Cheer/Dance = 1 0.024∗∗∗ [0.007] 0.012∗∗∗ [0.003] Physical Attractiveness 0.009∗∗∗ [0.003] 0.005∗∗∗ [0.001] Physical Development -0.001 [0.003] 0.001 [0.001] Constant -0.048 [0.064] 0.071∗∗∗ [0.019] Adjusted R2 0.09 0.15 N 1367 4487 Standard errors in brackets ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

precisely, each use of alcohol increases one’s prestige score by .002 and .001 for younger and older youth respectively. Younger youth get a bigger boost by using alcohol most likely because it is more common among older youth to drink alcohol (as evidenced by previous literature presented in Chapter 5 and the smaller amount of variation in the regression presented in Table 8.4). The significance of alcohol may be due to alcohol’s consensus as an “adult beverage" and use by adults in social situations. Because adults, generally, drink

108 alcohol as a way to converse and mingle with others, relax, and/or party, it is no surprise that both age groups would view it positively. If they are trying to show their “adultness," alcohol is a good place to start. Alcohol use’s relationship with prestige gives support for Moffitt’s maturity gap hypothesis as well. This trend does not hold for tobacco use, as it is only significantly related to pres- tige among older youth. In addition, tobacco actually decreases proximity prestige among younger youth (although insignificantly). Because of alcohol’s relationship to partying and much more common use among adults as compared to tobacco use, youth most likely do not esteem those who use tobacco as highly as those who drink. Similar to tobacco use, having a significant other and having sex were meaningful variables only to older adolescents. This is most likely a result of older youth being further along in puberty. The relationship be- tween having sex, having a significant other, and proximity prestige will be discussed in the following chapter. The negative, significant relationship seen with property crime and proximity prestige also falls in line with previous work discussed (Santor, Messervey, & Kusumakar, 2000; Selfhout et al., 2008). Both of these works found that property crime — in the form of vandalism and shoplifting — had no effect on popularity. The lack of effect was most likely due to a lack of value for property crime in the network at large. Selfhout and colleagues (2008) found that youth usually committed these offenses in dyads (pairs of youth). While prestige may go up between the members of the dyads, it is not enough to raise one in the network at large. Aggression’s negative relationship with prestige among older youth is consistent with previous literature. The relationship between aggression and popularity is complicated. Youth who can use both overt and relational aggression have an advantage over those who do not know how to use these tools. The data, unfortunately, did not have measures for relational aggression. And previous work shows that those who use only overt aggression generally see their popularity drop (Hawley, 2003).

109 Finally, physical attractiveness is consistent with the theoretical frameworks used. Chap- ter 3 discussed how evolution can inform researchers on how to view status. The drive to mate is the most powerful driving force of evolution. Our brains have developed specific mechanisms to use physical features as a proxy to identify those who likely have healthy genes and, by effect, offspring (Scheib, Gangestad, & Thornhill, 1999). Those who are attractive most likely have the genes that make that individual most likely to survive.46 It is interesting that physical development was not significant and even decreased pop- ularity among younger youth. Moffitt’s (1993) work hypothesized that those who are more physically developed look more like adults — which alone may increase their status since adulthood is coveted. They may also feel like they are more similar to an adult than an adolescent. It seems the relationship between popularity and physical development is more nuanced than first hypothesized. Centrality and Gender.

Males. After determining how the different predictors are related to proximity prestige across the entire sample and the two age groups, I examined gender differences. Table 8.5 reports the regression coefficients of the males of the sample. Columns 2 and 3 divide this sample by age in the same manner as Table 8.4 (14 and younger versus 15 and older). The analysis including only males found similar results to the previous analysis in regard to tobacco and alcohol use. While alcohol use is associated with proximity prestige at both age levels, tobacco is only significantly related among older youth — but that effect (along with a greater sub-sample size) is strong enough to drive the relationship in Column 1. Younger adolescents may not have as easy access to tobacco as older youth — since older youth are probably more likely to have friends who are 18 or older (and some of the youth in this sample are 18 years old. They can legally buy and use cigarettes). Alcohol use is significant across both age groups. This trend echoes the regressions of the entire sample 46For example, women prefer men with square jaws. This is a sign of testosterone in the body which is also associated with musculature and physical prowess. Conversely, men generally prefer women with slimmer faces, as that is an indicator for high levels of estrogen and, by proxy, fertility (Buss, 2015).

110 Table 8.5: Regression of Proximity Prestige on Predictors — Males by Age

All Males Young Males Old Males Deviance Alcohol Use 0.001∗∗∗ [<0.001] 0.003 [0.001] 0.001∗∗∗ [<0.001] Tobacco Use <0.001∗∗ [<0.001] 0.001 [0.001] <0.001∗ [<0.001] Marijuana Use >-0.001 [<0.001] >-0.001 [<0.001] >-0.001 [<0.001] Had Sex = 1 0.006 [0.003] -0.018 [0.012] 0.009∗∗ [0.003] Significant Other = 1 0.009∗∗ [0.003] -0.008 [0.008] 0.012∗∗∗ [0.003] Skipped School -0.001∗∗ [<0.001] >-0.001 [0.001] -0.001∗∗ [<0.001] Trouble with Teachers 0.004∗ [0.002] 0.005 [0.004] 0.004∗ [0.002] Trouble with Homework -0.003∗ [0.001] -0.005 [0.004] -0.002 [0.001] Property Crime -0.001 [0.001] -0.002 [0.002] -0.001 [0.001] Aggression -0.001 [0.001] 0.005 [0.002] -0.003∗∗∗ [0.001] Control Variables Age Wave 1 0.002 [0.002] <0.001 [0.008] 0.005∗ [0.002] Grade Level -0.007∗∗ [0.002] 0.001 [0.007] -0.008∗∗∗ [0.002] GPA 0.028∗∗ [0.009] 0.025 [0.046] 0.027∗∗ [0.009] White = 1 0.031∗∗∗ [0.003] 0.022∗∗ [0.008] 0.032∗∗∗ [0.003] Machiavellianism >-0.001 [0.001] -0.001 [0.003] >-0.001 [0.001] Parent Educational Achievement 0.002∗∗ [0.001] 0.003 [0.002] 0.002∗∗ [0.001] Household Income >-0.001 [<0.001] >-0.001 [<0.001] >-0.001 [<0.001] Correlates with Popularity Sports 0.007∗∗∗ [0.001] 0.006∗∗∗ [0.002] 0.008∗∗∗ [0.001] Cheer/Dance = 1 -0.035∗∗ [0.012] -0.056∗ [0.025] -0.022 [0.013] Physical Attractiveness 0.009∗∗∗ [0.002] 0.016∗∗∗ [0.004] 0.006∗∗∗ [0.002] Physical Development 0.001 [0.002] -0.006 [0.004] 0.004 [0.002] Constant 0.079∗∗∗ [0.022] 0.075 [0.103] 0.054 [0.028] Adjusted R2 0.13 0.08 0.15 N 2617 547 2070 Standard errors in brackets ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

and may be indicative of male-specific behavior values. This will be discussed more in the following chapter. Consistent with the results from Table 8.3, race is a powerful predictor as Whites are more likely to have a high proximity prestige score than are other races. However, the most salient predictor in the model with only males is membership in the Cheer/Dance Team. Male members of this group see significant drops in their prestige score. This may be a result to the deviation from expected gender norms and will be discussed in the Chapter 9.

111 Having trouble with teachers significantly increased males’ prestige score. This relation- ship could be interpreted as support for Moffitt’s idea of youth needing to assert/display their autonomy. Causing problems in the classroom shows, in a public place, that a youth does not care what authority figures think of him. It also can be perceived as him attempting to be a class clown in order to impress his classmates. Regardless, it suggests evidence of a deviant subculture where butting heads with authority figures is seen as “cool." One last significant relationship should be discussed. Counter to much of the previous research, aggression and proximity prestige are only significantly related for older males. Furthermore, that relationship is negative. The reason for this relationship is most likely due to only overt aggression being included in the measure. This type of aggression, when not used in tandem with other strategies, normally lowers one’s status. The data, unfortunately, did not contain a measurement of covert aggression. Therefore, its relationship, or the interaction between the two types of aggression, cannot be determined in this study. Females. There are many similarities between male and female groups and how their attributes/behaviors are related to proximity prestige. Table 8.6 shows that tobacco use, having a significant other, being in trouble with teachers, aggression, sports, and GPA all match the direction and significance of males. Additionally, race continues to be the most powerful variable for determining one’s proximity prestige. However, there are two differences between the sexes. First, younger female adolescents who engage in property crime are likely to have their proximity prestige score significantly decrease. This trend is true for females in general, but it is the younger group that is driving this relationship. The other main difference between males and females has to do with cheer/dance team. Not surprisingly, membership in these groups increases females’ prestige score. Next to Race and GPA, membership of this group is the most salient predictor — and it has twice the impact for younger females as it does for older ones. The impact of membership in this group should not be understated. Again, the average level of proximity prestige for youth in this sample is .154 and, for young females, being a cheerleader or dancer

112 Table 8.6: Regression of Proximity Prestige on Predictors — Females by Age

All Females Young Females Old Females Deviance Alcohol Use 0.001∗∗ [<0.001] 0.002∗∗ [0.001] <0.001 [<0.001] Tobacco Use <0.001∗∗ [<0.001] >-0.001 [0.001] 0.001∗∗∗ [<0.001] Marijuana Use >-0.001 [<0.001] >-0.001 [<0.001] >-0.001 [<0.001] Had Sex = 1 0.001 [0.003] 0.008 [0.010] 0.001 [0.003] Significant Other = 1 0.004 [0.002] >-0.001 [0.006] 0.006∗ [0.003] Skipped School -0.001∗∗ [<0.001] -0.001 [0.002] -0.001∗∗ [<0.001] Trouble with Teachers 0.004∗∗ [0.001] 0.001 [0.003] 0.005∗∗∗ [0.002] Trouble with Homework -0.003∗∗ [0.001] -0.007∗ [0.003] -0.002 [0.001] Property Crime -0.002∗∗ [0.001] -0.001 [0.001] -0.001∗ [0.001] Aggression -0.002 [0.001] -0.001 [0.002] -0.003∗ [0.001] Control Variables Age Wave 1 <0.001 [0.002] 0.017∗∗ [0.006] 0.001 [0.002] Grade Level -0.003 [0.002] -0.003 [0.006] -0.002 [0.002] GPA 0.033∗∗∗ [0.008] 0.040 [0.036] 0.033∗∗∗ [0.008] White = 1 0.034∗∗∗ [0.002] 0.035∗∗∗ [0.006] 0.034∗∗∗ [0.003] Machiavellianism -0.002 [0.001] <0.001 [0.002] -0.002∗ [0.001] Parent Educational Achievement 0.001 [0.001] >-0.001 [0.001] 0.001 [0.001] Household Income <0.001 [<0.001] >-0.001 [<0.001] <0.001 [<0.001] Correlates with Popularity Sports 0.007∗∗∗ [0.001] 0.007∗∗∗ [0.002] 0.007∗∗∗ [0.001] Cheer/Dance = 1 0.019∗∗∗ [0.003] 0.032∗∗∗ [0.007] 0.015∗∗∗ [0.003] Physical Attractiveness 0.004∗∗ [0.001] 0.004 [0.003] 0.004∗ [0.001] Physical Development >-0.001 [0.001] 0.002 [0.003] -0.001 [0.002] Constant 0.109∗∗∗ [0.019] -0.133 [0.081] 0.091∗∗∗ [0.025] Adjusted R2 0.15 0.12 0.15 N 3237 820 2417 Standard errors in brackets ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

increases her score by .032 on average — that’s roughly 20%. These females are visible by their peers and teachers in school. This could give them influence and forge connections between others, giving them a wider and stronger network from which to draw. One major surprise in this analysis is that physical attractiveness is significant for only older females (although the coefficients are identical between groups). The reason for this discrepancy is probably similar to the reason that having a significant other was significant only for older youth — maturation. Puberty is only in the beginning stages for younger

113 youth while it is finishing its process among older youth. Attractiveness may become more important after this process has finished across most of the peer network. Analysis Step #3

While some deviant behavior is linked with proximity prestige among adolescents, groups of adults most likely display a different pattern. Where youth experience a maturity gap due to the incongruence of their perceived maturity and society’s imposed limitations, adults have no limitations. Therefore, a chief motivator for deviance is removed with age and many stop commission of crimes.47 Because of this change in the perception of offending, the following analyses do not consider deviant variables as a predictor for social centrality. Instead, proximity prestige, along with the controls measured at Wave 1, are considered the predictors for outcomes later in the life-course. This section is divided into five subsections — one for each of the outcome variables. Descriptive Statistics. The outcome variables in this step of the analysis can be seen in Table 8.7. They include all of the measures and are divided into the different analyses that were calculated. The data all seem to be within expected ranges and indicative of the population at large. For instance, admitting to being drunk at work is skewed. That is because it is abnormal for an individual to be under the influence while working. Therefore, those who report that behavior will be considered outliers. It is also worthwhile to note the sample size drop observed for two variables — Feel Aggressive and Cut Activities to Drink. These drops are due to the data collectors asking these questions to a sub-sample. These drops in sample size will only affect the analyses where these variables are the outcomes so this change in sample size can be easily accounted for.

47There are a multitude of other explanations for the decline in crime during early adulthood. However, the evolutionary mismatch known as the maturity gap is the only one directly related to this dissertation and is, therefore, the only one discussed.

114 Table 8.7: Descriptive Statistics for Analysis Step #3 Outcome Variables

N Mean SD Min Max Skewness Kurtosis Depression Depressed this Week W3 6795 0.336 0.639 0.000 3.000 2.096 7.411 Depression = 1 W3 6789 0.108 0.311 0.000 1.000 2.524 7.371 Depressed this Week W4 6008 0.358 0.645 0.000 3.000 1.994 7.074 Depression = 1 W4 6007 0.156 0.363 0.000 1.000 1.898 4.602 SES W3 Educational Attainment W3 6793 7.416 1.922 0.000 16.000 0.419 3.483 Savings Account = 1 6780 0.653 0.476 0.000 1.000 -0.645 1.416 Food Stamps = 1 6777 0.040 0.196 0.000 1.000 4.705 23.141 Public Assistance = 1 6778 0.026 0.160 0.000 1.000 5.907 35.893 SES W4 Educational Attainment W4 6007 5.027 2.137 0.000 12.000 0.317 3.983 Annual Household Income 5688 7.268 2.504 0.000 11.000 -0.972 3.626 Aggression Feel Aggressive 1859 3.117 1.724 0.000 6.000 -0.030 2.116 Gossip 6794 1.540 1.204 0.000 4.000 0.363 1.940 Alcohol Abuse Drunk at Work 5146 0.124 0.521 0.000 4.000 5.045 30.621 Cut Activities to Drink 2591 0.112 0.315 0.000 1.000 2.462 7.061 Anxiety Relaxed = 0 6001 1.370 0.912 0.000 4.000 0.782 3.220 Stressed = 4 6001 1.757 1.027 0.000 4.000 0.398 2.224 Anxiety = 1 6007 0.116 0.320 0.000 1.000 2.400 6.762

Depression.

Wave 3. The first of the outcome variables is Depression. The Wave 3 variables are presented in the first two columns of Table 8.8. It should be noted that the interpretation of the two different measures should be done separately. “Depressed this Week" is on a 4- point Likert scale and measures self-rated feelings of depression where “Depression = 1" is a dichotomous variable measuring clinical assessment. Therefore, the coefficient of “Depressed this Week" should be smaller as there are more possible responses than the dichotomous clinical measure. Considering proximity prestige, we can see that this variable is a protective factor against depression at Wave 3. Conceptually what is being done here is a comparison between an

115 Table 8.8: Regression of Depression on Proximity Prestige

Depressed this Week Depression = 1 Depressed this Week Depression = 1 Proximity Prestige -0.250∗ [0.128] -0.676 [0.685] -0.233 [0.136] -0.131 [0.607] Control Variables Age Wave 1 0.039∗∗ [0.014] 0.067 [0.076] 0.029 [0.015] 0.069 [0.069] Grade Level -0.047∗∗∗ [0.014] -0.071 [0.077] -0.038∗ [0.015] -0.110 [0.070] GPA 0.054 [0.059] -0.522 [0.301] -0.148∗ [0.065] -0.226 [0.290] White = 1 -0.048∗∗ [0.019] 0.891∗∗∗ [0.114] -0.028 [0.020] 1.003∗∗∗ [0.105] Male = 1 -0.140∗∗∗ [0.019] -1.111∗∗∗ [0.106] -0.142∗∗∗ [0.020] -1.068∗∗∗ [0.096] Machiavellianism 0.051∗∗∗ [0.007] 0.122∗∗∗ [0.037] 0.038∗∗∗ [0.008] 0.094∗∗ [0.034] Parent Educational Achievement -0.016∗∗∗ [0.004] 0.066∗∗ [0.021] -0.016∗∗∗ [0.004] 0.005 [0.018] Household Income <0.001 [<0.001] <0.001 [0.001] >-0.001 [<0.001] <0.000 [0.001] Correlates with Popularity Sports -0.006 [0.006] -0.005 [0.033] -0.006 [0.007] -0.061 [0.031] ∗ 116 Cheer/Dance = 1 0.012 [0.028] -0.096 [0.138] -0.061 [0.030] -0.079 [0.124] Physical Attractiveness -0.009 [0.010] -0.124∗ [0.052] -0.026∗ [0.011] -0.128∗∗ [0.047] Physical Development -0.002 [0.011] 0.025 [0.057] -0.009 [0.012] 0.028 [0.051] Constant 0.282∗ [0.139] -2.557∗∗∗ [0.740] 0.672∗∗∗ [0.151] -1.765∗∗ [0.679] Adjusted/Pseudo R2 0.02 0.05 0.02 0.06 N 5854 5848 5173 5172 Standard errors in brackets ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001 individual with a proximity score of 0 versus 1 — or the least central youth compared to the most central. So what is seen is an average of a quarter of a unit drop in the likelihood that an individual will report being depressed for the most central youth compared to the least central. This is a large, substantive, and significant drop in the likelihood of feeling depressed. Furthermore, the probability of being clinically diagnosed with depression is substantively reduced as well, although not significantly. These findings provide support for previous work that argues that individuals who are able to be central in a network show an ability to connect with others and are more likely to have support within that network to guard against depression symptoms (Narr, Allen, Tan, & Loeb, 2017). For the first time in any analysis, Machiavellianism has a significant relationship with an outcome. It appears those that believe they can stretch the truth and get away with fibs have a significantly higher likelihood of feeling and being diagnosed with depression. This could be due to co-morbidity between the two constructs, a lack of being recognized for their perceived self-worth (as many Machiavellian actors have narcissistic tendencies), or myriad of other causes for this relationship. One interesting trend in the analysis lies with both race (White = 1) and parental educa- tional achievement (PEA). The regression shows that non-Whites and those whose parents have lower educational achievement are more likely to have experienced depressive symptoms during the week leading up to the assessment. However, Whites and youth whose parents have high educational attainment were significantly more likely to have been diagnosed with depression. This could be caused by multiple factors but the most likely culprit is that those with the resources to do so, seek counseling. Those without the resources, cannot or do not. The explanation explains how depressive symptoms stay constant across Waves 3 and 4, but clinical diagnosis decreases over time. Finally, it is no surprise that females are more likely to experience depressive symptoms or diagnosis. Women, on average, are more likely to suffer from this mental illness in the population at large (Staff, 2015).

117 Wave 4. The relationships discussed for Wave 3 maintain their statistical direction and many maintain their significance at Wave 4. However, the overall trend is that the effects are diminished (with the exception of gender and PEA). One interesting difference, however, shows that being involved with cheer/dance and being physically attractive reduce depressive symptoms while attractiveness also reduces the likelihood of diagnosis. Both cheer/dance membership and physical attractiveness are also related to centrality. It may be that these attributes allow for a stronger or wider social support network, thereby protecting individuals from depression. Socioeconomic Status.

Wave 3. Table 8.9 shows proximity prestige significantly affected two of the SES mea- sures. Both of these relationships suggest that being central during adolescence increases one’s chance of being affluent later in life. Those with higher levels of proximity prestige were more likely to achieve a higher level of education. More specifically, the most central youth will have, on average, a score that is 1.517 higher than the least popular youth. What this translates to is a year and a half more of schooling. And while this increase may not matter much at the bottom of the measure (e.g. moving from 7th to 8th grade attainment) it could have significant gains at the higher end of the measure (e.g. it could be the difference between a college degree or not, a master’s degree or not, or a doctorate or not). These differences could yield significant increases in income over one’s lifetime. Not to mention, a one unit increase in between an lowers one’s likelihood of being on public assistance by an average of 11%. In addition to proximity prestige, other variables such as GPA, being female, parental ed- ucational achievement (PEA), and household income (HHI) at Wave 1 all predict an increase in educational attainment. It is not surprising that GPA predicts success in education. The most powerful predictor of future behavior is past behavior (Wright et al., 2014). Therefore, it follows that good academic performance in high school would accurately predict the same performance in college.

118 Table 8.9: Regression of SES at Wave 3 on Proximity Prestige

Educational Attainment W3 Savings Account = 1 Food Stamps = 1 Public Assistance = 1 Proximity Prestige 1.517∗∗∗ [0.330] -0.049 [0.095] -0.018 [0.039] -0.110∗∗∗ [0.033] Control Variables Age Wave 1 -0.362∗∗∗ [0.036] -0.044∗∗∗ [0.010] 0.020∗∗∗ [0.004] 0.018∗∗∗ [0.004] Grade Level 0.828∗∗∗ [0.036] 0.064∗∗∗ [0.010] -0.020∗∗∗ [0.004] -0.018∗∗∗ [0.004] GPA 1.377∗∗∗ [0.154] 0.075 [0.044] -0.013 [0.018] -0.022 [0.015] White = 1 -0.044 [0.048] 0.018 [0.014] -0.034∗∗∗ [0.006] -0.008 [0.005] Male = 1 -0.208∗∗∗ [0.048] >-0.001 [0.014] -0.054∗∗∗ [0.006] -0.044∗∗∗ [0.005] Machiavellianism 0.015 [0.018] -0.009 [0.005] 0.002 [0.002] 0.002 [0.002] Parent Educational Achievement 0.182∗∗∗ [0.010] 0.016∗∗∗ [0.003] -0.005∗∗∗ [0.001] -0.003∗∗ [0.001] Household Income 0.004∗∗∗ [<0.001] 0.001∗∗∗ [<0.001] >-0.001∗∗∗ [<0.001] >-0.001∗∗ [<0.001] Correlates with Popularity Sports 0.088∗∗∗ [0.016] 0.013∗∗ [0.005] -0.002 [0.002] -0.001 [0.002] ∗ 119 Cheer/Dance = 1 0.053 [0.072] 0.007 [0.021] 0.017 [0.009] 0.002 [0.007] Physical Attractiveness 0.107∗∗∗ [0.026] 0.028∗∗∗ [0.008] -0.008∗∗ [0.003] -0.003 [0.003] Physical Development 0.072∗ [0.028] 0.013 [0.008] -0.004 [0.003] -0.002 [0.003] Constant 2.065∗∗∗ [0.360] 0.439∗∗∗ [0.103] 0.042 [0.042] 0.007 [0.036] Adjusted R2 0.27 0.03 0.04 .03 N 5851 5841 5841 5842 Standard errors in brackets ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001 Another unsurprising relationship is that females out-perform males in education. The fruits of this trend can be seen as, currently, women make up a higher percentage of college students and graduates when compared to men. Furthermore, individuals whose parents have achieved academically can benefit from their parents’ experiences. The same can be said for HHI. In addition to just educational attainment, PEA and HHI seem to consistently increase one’s likelihood of succeeding academically and financially as they are significant across all measures at Wave 3. Another interesting finding has to do with physical attractiveness. It seems that those who are attractive succeed academically, are more likely to have savings, and are less likely to be on food stamps. This suggests that attractive people may make more money by virtue of their attractiveness. Since attractiveness was rated at Wave 1 and these outcomes are from Wave 3, it is unlikely that their achievements have made the participants more attractive. Therefore, it is most likely something to do with their attractiveness that increased the odds that they would become educated and make wise financial decisions. Wave 4. Interestingly, at Wave 4, the relationship between proximity prestige and edu- cational attainment has been rendered insignificant. This may have something to do with the way the variable was measured. The data collectors changed the measurement from years of school to more specific definitions of achievement (e.g. some college, completion of college degree, some graduated work toward a master’s, completion of master’s degree). However, in Table 8.10, one can see that popularity is significantly related to annual household income. One unit change in proximity prestige amounts to an increase to the next tier of income as defined by the measure. Depending on the starting tier, this could translate to as little as a $5,0000 per year increase to a $50,000+ increase. The exact dollar amount is not specific as the measure is ordinal. However, the fact stands that those who are central have an increased chance to make more money later in life. This increase could be due to social skills honed and improved throughout their lives, attributes that increase their popularity and income, or a combination of the two. This will be discussed further in the next chapter.

120 Table 8.10: Regression of SES at Wave 4 on Proximity Prestige

Educational Attainment W4 Annual Household Income Proximity Prestige 0.578 [0.408] 1.200∗ [0.522] Control Variables Age Wave 1 -0.493∗∗∗ [0.045] -0.386∗∗∗ [0.058] Grade Level 0.653∗∗∗ [0.046] 0.616∗∗∗ [0.059] GPA 1.431∗∗∗ [0.194] 0.746∗∗ [0.248] White = 1 -0.036 [0.060] 0.532∗∗∗ [0.077] Male = 1 -0.486∗∗∗ [0.060] 0.370∗∗∗ [0.076] Machiavellianism 0.023 [0.023] -0.014 [0.029] Parent Educational Achievement 0.241∗∗∗ [0.012] 0.074∗∗∗ [0.016] Household Income 0.005∗∗∗ [0.001] 0.003∗∗∗ [0.001] Correlates with Popularity Sports 0.116∗∗∗ [0.020] 0.087∗∗∗ [0.025] Cheer/Dance = 1 0.027 [0.089] -0.047 [0.113] Physical Attractiveness 0.076∗ [0.032] 0.218∗∗∗ [0.041] Physical Development 0.145∗∗∗ [0.035] 0.129∗∗ [0.044] Constant 3.001∗∗∗ [0.452] 4.512∗∗∗ [0.578] Adjusted R2 0.19 0.08 N 5172 4918 Standard errors in brackets ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

Similar to Wave 3, those who played sports in adolescence were more likely to attain a higher level of education and also to have a higher annual income than those who did not play sports. Additionally, youth who were considered attractive and physically developed during adolescence had higher incomes at Wave 4. Also keeping with Wave 3, youth whose parents had higher educational achievement and higher household income fared better on both outcomes. One interesting finding has to do with gender. Again, females had more academic success than did males. However, males incomes are significantly higher. This could be due to the specific areas of education that women chose versus the men who did achieve academically (e.g. education for women versus engineering for men). The data also does not give infor- mation on the types of jobs that individuals obtain (e.g. women choosing safer careers while men choose riskier ones, thereby increasing their pay). These data do not clarify on this

121 disparity but it is an interesting trend in the results. Aggression. Aggression measures were only present at Wave 3. Table 8.11 shows that aggression at Wave 3 is not significantly related to proximity prestige. This is no surprise as the two were unrelated in the models from Step #1. Also not surprisingly, high GPA is associated with lower levels of both types of aggression where males are generally more overtly aggressive and females are more relationally aggressive. One interesting relationship can be seen with athletic involvement (Sports). Those who were athletes during high school are more overtly aggressive in early adulthood. This could be due to socialization, selection, or a multitude of other causes.

Table 8.11: Regression of Aggression on Proximity Prestige

Feel Aggressive Gossip Proximity Prestige 0.880 [0.679] -0.004 [0.241] Control Variables Age Wave 1 0.028 [0.074] 0.028 [0.026] Grade Level -0.004 [0.074] -0.034 [0.027] GPA -0.055 [0.280] 0.102 [0.112] White = 1 -0.240∗ [0.096] -0.091∗∗ [0.035] Male = 1 0.173 [0.097] -0.183∗∗∗ [0.035] Machiavellianism 0.297∗∗∗ [0.037] 0.100∗∗∗ [0.013] Parent Educational Achievement 0.012 [0.020] -0.007 [0.007] Household Income >-0.001 [0.001] <0.001 [<0.001] Correlates with Popularity Sports 0.081∗ [0.033] -0.013 [0.011] Cheer/Dance = 1 0.018 [0.129] 0.094 [0.053] Physical Attractiveness 0.015 [0.051] -0.020 [0.019] Physical Development 0.014 [0.057] -0.030 [0.021] Constant 2.043∗∗ [0.709] 1.491∗∗∗ [0.262] Adjusted R2 0.05 0.01 N 1627 5853 Standard errors in brackets ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

Another key finding deals with Machiavellianism. High levels of this attribute are sig- nificantly associated with increased feelings of aggression and a tendency to talk behind others’ backs. It should be no surprise that individuals who report to bend the truth and

122 have skill in manipulation would also engage in gossip. But what is also interesting is that they also report feeling aggressive. It should be noted that the interviewers did not define “aggressive" to the participants. This concept was left to the participant to define and judge if they were high in that trait. So those who see themselves as manipulative may also see that manipulation as a form of aggression.

Table 8.12: Regression of Alcohol Abuse on Proximity Prestige

Drunk at Work Cut Activities to Drink Proximity Prestige -0.100 [0.117] 0.071 [0.971] Control Variables Age Wave 1 -0.002 [0.013] 0.176 [0.115] Grade Level -0.016 [0.013] -0.254∗ [0.117] GPA 0.009 [0.055] 0.460 [0.540] White = 1 -0.003 [0.018] -0.106 [0.169] Male = 1 0.126∗∗∗ [0.017] 0.163 [0.148] Machiavellianism 0.037∗∗∗ [0.007] 0.147∗ [0.057] Parent Educational Achievement 0.008∗ [0.004] -0.064∗ [0.032] Household Income <0.001 [<0.001] -0.002 [0.002] Correlates with Popularity Sports 0.009 [0.006] 0.037 [0.044] Cheer/Dance = 1 0.022 [0.026] -0.539 [0.288] Physical Attractiveness 0.001 [0.009] 0.055 [0.085] Physical Development -0.001 [0.010] -0.075 [0.089] Constant 0.133 [0.130] -2.574∗ [1.154] Adjusted/Pseudo R2 0.03 0.02 N 4480 2256 Standard errors in brackets ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

Alcohol Abuse. Alcohol abuse is another outcome that was assessed. Table 8.12 has two measures, one measured at Wave 3 (Drunk at Work) and the other at Wave 4 (Cut Activities to Drink). Proximity Prestige is negatively (although insignificantly) related to alcohol abuse — even though these two were significantly, positively related during adolescence. This change may be the result of the removal of the maturity gap and the expectations placed on adults versus adolescents. Furthermore, since adults are legally allowed to drink alcohol, it may lose some of its desirability (since it is no longer “forbidden fruit") and a responsible

123 use of alcohol is preferred. Males were more likely to show signs of alcohol abuse than were females at Wave 3. Furthermore, Machiavellian actors seem to be more likely to abuse alcohol at work as well as skip out on activities in order to drink. While Machiavellianism had no significant rela- tionships in the early stages of this analysis, a pattern is starting to develop that those who see themselves as manipulative do not fair well later in life. Anxiety. Table 8.13 shows the regression for the three measures of anxiety. Anxiety was measured only at Wave 4. According to this analysis, being central in high school significantly increased the likelihood of having relaxed feelings later in life. More accurately, one unit increase in proximity prestige decreases feelings of anxiety by almost a half a unit. This is interesting considering the other two measures of anxiety are in the opposite directions (although they are both insignificant). Two other trends come to light with this analysis. The first is that, generally, white women feel high levels of anxiety. The other is that participating in sports decreases your stress later in life. More specifically, for every sports team that an individual is on in high school, their likelihood to be diagnosed with an anxiety disorder decreases by .071 units and decreases .021 units for reporting feelings of anxiety. The explanation for this relationship could be that athletes developed healthy habits (i.e. exercising regularly and eating healthily) and learn how to handle stressful situations (competition is a stressor) early in life. And these healthy habits protect them from stress. It could also be that, since they participated in sports, they have a social safety net to where they are able to go to others for help if needed.

124 Table 8.13: Regression of Anxiety on Proximity Prestige

Relaxed = 0 Stressed = 4 Anxiety = 1 Proximity Prestige -0.434∗ [0.191] 0.126 [0.212] 0.455 [0.667] Control Variables Age Wave 1 0.037 [0.021] 0.030 [0.024] 0.060 [0.077] Grade Level -0.016 [0.021] -0.040 [0.024] -0.134 [0.078] GPA -0.063 [0.091] -0.080 [0.101] -0.382 [0.324] White = 1 0.169∗∗∗ [0.028] 0.220∗∗∗ [0.031] 1.014∗∗∗ [0.121] Male = 1 -0.354∗∗∗ [0.028] -0.520∗∗∗ [0.031] -1.057∗∗∗ [0.110] Machiavellianism 0.013 [0.011] 0.034∗∗ [0.012] 0.100∗∗ [0.038] Parent Educational Achievement -0.006 [0.006] -0.019∗∗ [0.006] -0.040 [0.020] Household Income 0.001∗∗ [<0.001] <0.001 [<0.001] >-0.001 [0.001] Correlates with Popularity Sports -0.004 [0.009] -0.021∗ [0.010] -0.071∗ [0.036] Cheer/Dance = 1 0.028 [0.042] -0.060 [0.046] 0.097 [0.133] Physical Attractiveness -0.001 [0.015] -0.026 [0.017] -0.008 [0.053] Physical Development 0.005 [0.016] -0.023 [0.018] 0.019 [0.058] Constant 1.074∗∗∗ [0.211] 2.005∗∗∗ [0.235] -1.760∗ [0.761] Adjusted/Pseudo R2 0.04 0.07 0.05 N 5168 5168 5172 Standard errors in brackets ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

125 Chapter 9

Discussion

This chapter of the dissertation will take the results from the analyses and place them into the theoretical frameworks discussed in the previous chapters. The organization will be based around the three research questions enumerated in Chapter 6. Following these discussions, I will address limitations and directions for future research. Research Question #1 What are the factors that predict social centrality?

Overview. Traditional indicators of popularity — specifically, GPA, sports membership, cheer/dance membership, and attractiveness — consistently predict social centrality. While the relationship is nuanced, it is clear that the popularity and proximity prestige are similar concepts. Grade Point Average. The most salient predictor of popularity at Step #1 was GPA.48 The relationship between GPA and proximity prestige is quite indicative of the discussion in Chapter 2. Socially central youth must walk a fine line. They are trend-setters, but are also beholden to the preferences of their peers (almost like politicians). While they cannot seem to give too much concern regarding their studies (lest they be considered a “nerd" and lose connections), they cannot completely disregard their grades as failing all their courses would decrease their position as well. What is most likely occurring is that central youth, in general, are at least at average intelligence if not above — evidenced by their ability to consider complex social situations and navigate them successfully. Because of this, they do not need to study incessantly in order to get their necessary marks. Combine this with the likelihood that central youth will be attractive and involved in sports and/or cheer/dance (discussed more in the next section), and it may be that central youth are able to attain reasonably high

48Race was actually the largest coefficient. However, some scholars argue that race, as a predictor variable, can cause confounding as race encompasses a multitude of factors (see VanderWeele and Robinson (2014) for a discussion). Because of this perspective and the chief focus of this study, race was used strictly as a control variable and will not be discussed as a predictor in this section.

126 grades with minimal effort while also excelling at their extra-curricular activities. Sports, Cheer/Dance, and Attractiveness. The two most salient predictors of pop- ularity for girls discussed in Chapter 2 were physical attractiveness and being a member of the cheerleading team. Additionally, the two most important predictors for males in this research were excelling at athletics and attractiveness. These relationships are largely supported for proximity prestige as well in this analysis. However, one interesting surprise has to do with the lack of a gender difference observed. The discussion in Chapter 2 would suggest that participating in sports would increase both male and females popularity. It would also predict that it would increase males’ popularity more than females’, as the expectations for roles and the factors that affect attractiveness differ between the sexes. Evolutionary theory would also support this supposition. When comparing the two groups’ social centrality however, what is seen is a significant increase in proximity prestige per sport played (.007 on average) for both groups. In other words, sports teams membership matters just as much for girls as it does for boys in relation to increasing their proximity prestige. This trend could be a result of visibility. If one is on a court or field in front of one’s peers competing for the school, more students will see the athletes. This could increase their influence among peers just because people know who they are. It should also be noted that while the analysis can mathematically be interpreted as an average of a .007 increase in popularity per sport played, this is probably partially inaccurate. Not all sports are created equal. What is meant by this is that, depending on the region, size, and demographic makeup of the school, some sports are likely more popular than others. For example, a high school in a rural Kentucky town will probably value basketball over lacrosse. Likewise, a high school in Wisconsin is more likely to value ice hockey than one in Arizona. The point to this discussion is to illustrate that the specific sports team one participates in can have a large or small effect on one’s proximity prestige and that a linear view of this relationship is most likely not indicative of reality.

127 Research Question #2 What is the relationship between social centrality and deviance?

Overview. Overall, the analyses were consistent with previous literature from popularity and Moffitt 1993. The most significant finding was support for the maturity gap. The factors that raise younger adolescents’ proximity prestige differ from the factors that older youth’s. Discussion regarding underage sex and differences between the sexes are also included. Maturity Gap. The key takeaway from this step of the analysis is that younger youth value prosocial attributes while older youth value a mixture of prosocial and antisocial be- haviors. To more succinctly display this trend, Table 9.1 shows the relationship between popularity and the significant predictors of the analysis by age group.

Table 9.1: Popularity on Significant Predictors — by Age

Predictor Younger Adolescents Older Adolescents Prosocial GPA + Sports + + Cheer/Dance + + Attractiveness + + Antisocial Alcohol Use + + Tobacco Use + Had Sex + Significant Other + Skipped School – Trouble with Teachers + Property Crime – Aggression – + = significant positive coefficient – = negative significant coefficient

Table 9.1 is divided in two ways. The horizontal line separates the prosocial variables (top) from antisocial ones (bottom). Column 1 includes the younger half of the sample (14 years old and younger) while Column 2 includes the older half (15 years old and older). Every

128 prosocial variable — with the exception of GPA for younger youth49 — positively predicts proximity prestige for both groups. Meaning participating in school activities and “following the rules" increases one’s centrality across the entirety of adolescence. However, below the line, alcohol use is the only factor that increases proximity prestige for the younger group. All antisocial behaviors (excluding skipping school, property crime, and aggression) have a positive or null relationship with prestige. This is a very interesting finding and fits nicely into both the evolutionary framework and Moffitt’s taxonomy. Younger youth do not feel the pressure of the maturity gap, due to being further away from adulthood and being less physically developed. Therefore, their social structure will be more similar to that of a child’s — when popularity and likability are synonymous — than an adult’s — when there is a clear difference between the constructs. But as youth approach adulthood, the pressure to assume or mimic adult roles becomes stronger.50 This desire to attain adult roles is evolutionarily driven due to adolescents having a more prominent role in hunter-gatherer communities as compared to modern-day society. In other words, the findings supports the maturity gap that Moffitt (1993) espoused, which is a type of evolutionary mismatch.51 And those youth who can adopt more adult roles are more likely to have a central position in the social network. Underage Sex. The results from Step #2 found partial support for previous research. Two studies (Carlson & Rose, 2007; Zimmer Gembeck & Collins, 2008) found that popularity significantly predicted sexual activity. This study found a significant relationship between proximity prestige and underage sex for only older males. The more important finding of this study’s analyses is the significant relationships between proximity prestige and having a 49The coefficient for younger youth may be insignificant but it is actually larger for younger youth (.039) than for older (.030). The lack of significance is due to a higher amount of variation between the relationships (.029 versus .006, respectively). This trend is to be expected as it is common to see a drop in grades among middle schoolers, then a recovery in high school (Eccles & Roeser, 2009) 50This could also be a byproduct of the brain’s maturational process and somatic marker theory mentioned in Chapter 2. 51It should be noted that, while Table 9.1 lays out the difference between coefficients across age groups, a coefficient difference test was run as well. None of the coefficients across age were significantly different, save having a significant other. While this does reduce the significance of the trend illustrated in Table 9.1, it is still an interesting finding that provides some support for Moffitt’s (1993) assertions.

129 significant other — specifically later in adolescence and for both sexes. The findings between significant others and centrality is supported by the previously referenced works. What is most likely at play is that having sex only boosts one’s position in certain circumstances — namely having monogamous sex with a high-status significant other — and having a significant other moderates the effect of having sex in adolescence. Promiscuity is seen as detrimental for both sexes; although moreso for females (Zimmer Gembeck & Collins, 2008). And being in a relationship removes the social detriment of promiscuity while still allowing youth to be sexually active and also allows youth to act more like adults (i.e. simulating a monogamous marriage). Sex Differences.

Males. Alcohol use was found to positively predict popularity among males for both age groups. This trend is interesting because, overall, alcohol is the only antisocial factor that increased popularity across both age groups of males. What may make alcohol unique is both its legality and widespread use among adults. Alcohol is commonly consumed among adults. When used responsibly, it provides adults with a chance to socialize with their peers and have recreational time. It can also be purchased, legally, at places such as bars, sporting venues, and concerts. All of which are appealing to many youth. If there was a substance that youth seeking to assume adult roles would choose, it would be the one that one can purchase when they are a certain age and is associated with “good times." Proximity prestige’s relationship with school activity affiliation (i.e. playing sports ver- sus being a cheerleader/dancer) is consistent with evolutionary theory as it is discussed in Chapter 3. Strict gender roles for each sex ruled the day. Males were expected to be mascu- line and excel at physical activities. That is how males attained status. This explains why involvement in sports is significantly, positively related to proximity prestige. Males who excel at their expected role are connected with many of their peers. However, the absolute value of the effect size is 4 to 7 times larger for affiliation with Cheer/Dance as it is for Sports team membership. This suggests that the price for violation of the norms is far greater than

130 excelling at the prescribed roles. The penalty of deviation also conforms with evolutionary theory. If the hunter-gatherer communities required a tight-knit community where everyone contributed and “carried their weight," those who deviated from the general norm would be compromising the group’s survival. This compromise would necessitate stern action with either social pressures or punishment (Buss, 2015). Females. One of the strongest predictors for popularity among females in previous lit- erature is physical attractiveness. However, in these analyses, the relationship was weaker than expected when using proximity prestige as the outcome. When looked at for females in general, physical attractiveness was significant, but its coefficient was smaller than many other factors — e.g. GPA, sports, and cheer/dance. Furthermore, analyses that split females into the two age groups reported statistical significance only for older females. The most likely reason for this trend has to do with human development. As stated early in the Results section, 14 years was selected as the cut-off for dividing the sample into younger and older youth. The reasons for this were both practical and based around the average age of pubertal development. Youth in the younger group are just starting puberty. Because of this, they both look and think more like children than adults. Conversely, the older youth think and look more similarly to adults as they are exiting the pubertal process. Because of this, younger youth are likely less interested in the opposite sex than are older youth. This is especially true for boys as they enter puberty later than girls, on average. In other words, girls’ attractiveness does not matter as much to as many boys until later in adolescence. Therefore, attractiveness may not relate to proximity prestige for girls until boys make this shift (because the social network comprises of both boys and girls). It could also be that social a female’s level of attractiveness is not as relevant to her position in the social network as it is for her popularity. Research Question #3 How is social centrality in adolescence related to well-being outcomes later in life?

131 Overview. Proximity prestige was directly predictive of measures of depression, so- cioeconomic status, and anxiety later in life. Furthermore, prestige’s key correlates were predictive of many of the outcomes at adulthood. Depression. A significant finding comes from this section. A move from having a prox- imity prestige of 0 to 1 can reduce an individual’s feelings of depression by a quarter of a unit in adulthood. That could change an individual from feeling depressed “most of the time" to “some of the time" which is a major change in someone’s life. And while this is the only significant relationship in the analysis, the same relationship at Wave 4 has a coefficient very close (-.250 versus -.233, respectively). The measures regarding centrality and clinical diagnosis of depression also provide support for proximity prestige as they are in the same direction. The cause of this relationship could be that those who are socially central in a network have a good support structure that aids them in combating depression. Those who suffer from depression generally feel isolated. If an individual has influence and is important to the network, members of that network may reach out to the individual and aid them in coping with their illness. In addition to this explanation, it could also be that the traits that increase an individual’s proximity prestige can also serve as protective factors. For instance, being a member of a sports team increases one’s social centrality. Additionally, physical activity has been shown to reduce signs of depression. So there is a chance that some factors that cause an individual to be central to the network are simultaneously protecting them from this mental illness. If both of these are true, then those who are socially central would have multiple forces protecting them from depression later in life. And this example could be evidence for cumulative consequences discussed by Moffitt (1993). Things that happen early in life can compound and have far-reaching effects later in the lifecourse. The relationship between depression and the predictors is not straightforward. It is interesting that non-Whites are more likely to report feeling depressed and have their parents have lower educational achievement. However, white teens’ parents have higher educational

132 achievement (see Chapter 5) and are more likely to be clinically diagnosed with depression. These trends together may be evidence of access to mental health resources. Finding and, more importantly, paying for a mental health professional’s service is expensive. Those with lower educational attainment will, on average, have a lower income. The White children in this sample may have better access to mental health professionals. This would explain both their higher likelihood of being clinically diagnosed and also their lower likelihood of feeling symptoms of depression. Conversely, those with less access would be more likely to report symptoms and less likely to report receiving professional help. Socioeconomic Status. The SES outcomes that were significantly related to proxim- ity prestige were Educational Attainment, Public Assistance, and Annual HHI. Even while controlling for many of the predictors for popularity (i.e. GPA, Race, and Sports), proximity prestige emerged as the second strongest predictor of educational attainment (1.036) and the strongest predictor for Annual HHI (1.083). These findings suggests that being socially cen- tral does have a positive benefit on earnings later in life. It could be that other factors that increase proximity prestige that are not controlled for in this study caused the relationship — e.g. confidence, persuasiveness, social intelligence. Regardless, the proximity prestige one has in high school does affect later earnings and can also be seen as a protective factor against poverty. The relationship between SES and proximity prestige can be viewed through the lens of evolutionary psychology. Previous research (and this analysis) have shown that good academic performance and affluence significantly predict social centrality. It seems that two of the best predictors of educational attainment and affluence are also proximity prestige. This self-reinforcing relationship could be a result of two forces: heredity and socialization. Those with genes that make them successful (and in turn increase their centrality) pass those genes on to their children. Furthermore, because of their success, the parents are able to provide their children with advantages. Additionally, because of the social position of the parents, members of the community will also likely know the children of these parents and

133 associate the child with the high-status parent. While the old adage “success breeds success" was originally referencing an individual’s success, based on this logic, it could be that success breeds inter-generational success as well. Limitations & Directions for Future Research

The study does have a number of limitations. They are divided into methodological and conceptual/theoretical groups below. Methodological Concerns. One major limitation of the study is the time-frame in which the data were collected. Initial data collection was conducted in the 1995–1996 school year — almost 25 years ago. Since that time, an entire generation of youth have gone through high school and entered into adulthood. Additionally, a multitude of technological innovations and social movements have shifted the social space of the United States since the initial data collection. The internet has forever changed the way that people — especially teens — communicate. Teens no longer just communicate at school with their peers. Now, they can be in constant communication with one another. Sometimes with good consequences (such as youth potentially creating tighter knit prosocial peer groups) and other times with negative results (for instance the emergence of cyber-bullying, cyber-stalking, and sexting). In other words, the values and experiences of teenage Generation Xers — who make up this dataset — varies greatly from the experience and values of Millennials, as often times happens. At first, this realization may reduce the significance of the findings found in this study. And while some of the predictors for proximity prestige or long-term consequences of central- ity may change slightly, the underlying evolutionary principles should stay constant. In other words, the manifestation of how youth interact with their environment and social structure may change, but the root mechanisms that influence youth to act like teenagers remains the same. Another potential limitation involves the measurement of proximity prestige. One of this dissertation’s chief contributions to the criminological literature is the introduction of

134 an unexplored conceptualization of popularity (influence on peers) and drawing parallels between that and proximity prestige — a measure of social centrality. The results from Step #1 do suggest that there is overlap between the two constructs as proximity prestige’s relationships with the correlates of popularity were similar to the relationships seen in the popularity literature. However, no test could be done to statistically correlate proximity prestige to the sociometric measure of popularity as described by Cillessen and Mayeux (2004). While this can be viewed as a limitation of this dissertation, it is also an opportunity for future exploration. I plan to test the issue of these measures in future studies and explore the differences and similarities of the social networking and sociometric strategies. Additionally, the measure of proximity prestige is not without its issues. As can be seen in 8.1, there are over 200 individuals with a score of 0 for proximity prestige. While this group may just be loners or groups of teenagers who did not take the survey seriously, this study did not verify that assertion. The reason for this is a logistical issue regarding access to the data toward the end of the completion of this dissertation. Once this access issue is resolved, characteristics of these individuals will be explored. The fourth methodological limitation to the study deals with the criteria for causation. This work attempts to get as close as possible to meeting all three criteria of causation (see VanderWeele and Robinson (2014) for an in-depth discussion) with the data at hand but, as most studies in the social sciences, it falls short of the standard. While the analyses accu- rately highlight statistical relationships between variables, any relationship in Steps #1 and #2 are susceptible to reverse causal ordering (Gottfredson & Hirschi, 1990) as all variables were measured at the same time. While Step #3 is less susceptible to this critique, as predic- tors were measured before the outcome variables, there is still a substantial probability that spurious variables could be driving these relationships. Some of these potential confounders were discussed above, however, not all can be controlled. Due to this, interpretations of the results should emphasize that a causal relationship was not determined. However, the mod- els did provide support for many of the theoretical assumptions discussed in the beginning

135 of this dissertation. The statistical relationships combined with the theoretical frameworks do aid in putting the relationships into a perspective and suggesting causal links. Related to the criteria for causation, I was not able to control for genetic influence in this analysis. The original intent of this work was to include a fixed-effect model using the restricted twin-data in the Add Health dataset. Unfortunately, due to logistical issues with the data that could not be overcome, the fixed-effect model was not performed and, therefore, will not be reported in this dissertation. As with the issue of proximity prestige above, once the issue dealing with access to the restricted data is resolved, models including estimation of genetic influence will be conducted. My hope is that they will serve as future publications to extend on the work done here by more fully specifying the models. Conceptual/Theoretical Concerns. There was significant discussion over the differ- ences between the sexes and what factors influenced each group’s level of proximity prestige. While these factors were considered in the analysis, what was not included was an analysis that assessed if an individual was socially central among only males or females. Put another way, all the analyses assessed proximity prestige from all youth. There was no assessment of being central in networks composed of females versus networks composed of males. Analyses that consider the sexes’ differences would be valuable as they could potentially better test some of the theoretical concepts. For example, a male in this analysis could have high proximity prestige driven by his acceptance and influence within male circles while having very little to no connections with females. This hypothetical male who is central among males but not females may have status and access to resources, but not the correct types of status and resources to attract a mate. Therefore, many of the evolutionary benefits described earlier would not apply to such an individual. Future works will seek to adjust the social networking measures and analyses to account for this difference and answer some of these theoretically relevant questions. Moffitt’s (1993) taxonomy focuses on two groups of youth — adolescence-limited and life-course persistent. The former is theorized to conform during childhood, deviate during

136 adolescence, and return to conforming in adulthood. The latter offends consistently from childhood to late adulthood when they finally desist. A delineation between the two groups was not made in this work. The aim of this dissertation was to explore the relationship between proximity prestige and deviance. However, because there was no analysis to separate the two categories of individuals, the model may be mis-specified. It could be that many of the relationships between proximity prestige and long-term outcomes could only be present for one group but not the other. Furthermore, LCPs could be driving much of the relationship between deviance and centrality later in adolescence, as was seen in Young (2014), while the relationships laid out in Step #3 could be driven largely by ALs. Future works will separate the groups and test to see which of these relationships hold for which group. Policy Implications

Despite the limitations of the study, the results suggest some policy proposals that may aid in bettering the well-being outcomes of youth — both in adolescence and adulthood. The first deals with assessing risk. Based on the findings, low levels of social centrality is linked to lower financial and educational achievement in early adulthood. Assessing teenagers may be a good proactive strategy to identify youth who are at-risk of failing to achieve financial independence. Targeted interventions may help to ease this risk and aid adolescents later in life. Additionally, high levels of proximity prestige are also associated with risk- taking behavior in adolescence. Similar to the strategy above, administrators, parents, and guardians might be able to use this measure, in tandem with others, to identify youth who are more likely to engage in using alcohol, getting into trouble with teachers, and other risky behavior. Tailored or targeted interventions could also be used with this sub-population. Administrators could also use the metric, proximity prestige, to identify influential youth in their school. Those with high scores are connected to more of their peers and can, presumably, affect change effectively. By instituting strategies where school officials identify these youth and work with them, they may be able to shift perspectives of students at large to be more inclusive to their peers — thereby mitigating some of the negative effects of being

137 on the periphery of the network. If a strategy such as this could be implemented effectively, it may be a valid strategy to combat bullying and other negative phenomena common in groups of adolescents. Conclusion

Despite the limitations discussed in the previous section, this work contributes to the literature of both Moffitt’s maturity gap and juvenile deviance in general. The most signif- icant finding regarding Moffitt’s 1993 work shows that as youth approach adulthood, they begin to value specific antisocial behaviors and reward youth who perform these behaviors with status. This trend coincides with the age-crime curve (Farrington, 1986), a trend found almost universally in Western culture. It could also be thought of as an evolutionary mis- match — that youth’s modern-day social structure fundamentally differs from the one which many adaptations evolved for. And that the praise of deviant behavior is an unintended and unforeseen consequence of delayed adolescence and the sequestering of youth in schools populated by their peers. This research also brings a new conceptualization of popularity and a novel measurement related to that concept into the field of criminology. Founding research of the field (e.g. Merton (1938), Shaw (1938), and Sutherland (1942)) focused greatly on juvenile deviance and the mechanisms that youth pass their deviant ways onto the next cohort. Despite this tradition of deviance being the focal point of many theories, the field never considered one’s position in the social network in the way that it has been presented in this text; with a few youth within the peer group obtaining status and using that status to obtain resources. In addition to just status being used as a tool, this work also brought the reconceptualization of popularity developed in psychology and sociology into criminology. That popularity is not the number of friends you have. Rather, it is the amount of influence one has within a network. These changes in perspective may better form the basis on which criminologists view peer pressure and transmission of deviant values from older youth to younger ones. A third contribution comes in the form of taking social centrality and observing how

138 one’s social position can affect individuals after they exit adolescence. One of the most salient results was socially central youth had higher education levels and earned more money later in life. While this research does contribute to the literature, it does not definitively answer what causes youth to offend more frequently as they approach adulthood. While youth may make the decision to engage in deviant behavior to improve their social position or, at the very least, fit in with their peers, it should not be stated that it is the only reason. Neurological processes are at play to increase risk-taking in youth. Furthermore, experimenting with drugs and alcohol is intrinsically rewarding. However, the mechanisms that drive youth to behave the way they do are ones that have been taking shape for millennia (or eons actually). It will probably be millennia before researchers are able to definitively determine why youth’s behavior changes so radically during adolescence. Regardless, the change in youth is a reality. Parents for the foreseeable future will need to accept the fact that their children will largely care more about their friends than the people who raised them.

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