The Association Between Sexual Behavior and Antisocial Behavior

Abstract

There has been a great deal of interest in examining the evolutionary underpinnings to human behaviors, including antisocial behaviors. Very little of this research, however, has seeped into mainstream criminology. The present study seeks to take a cautious step in addressing that gap by examining the role certain evolutionary constructs play in the association between sexual behaviors and antisocial conduct. Analysis of data drawn from the National Longitudinal Study of Adolescent Health revealed that for males and females sexual involvement predicted delinquent involvement both concurrently and longitudinally. Furthermore, items derived from evolutionary psychology, including physical attractiveness, physical maturity, and perceptions about life certainty, predicted sexual involvement for males and females. Our study represents one example of the various ways in which evolutionary explanations can be integrated into criminological theory and research.

Keywords

Add Health evolutionary psychology sexual behavior delinquency biosocial criminology

Explanations of human behavior have historically been bifurcated into those that focus on environmental factors and those that focus on biological or genetic factors. This well-known “nature versus nurture” debate has resulted in a contentious and sometimes quite heated dispute among researchers, theorists, and the public alike. For the most part, disciplines have been informed by both sides of the debate and much contemporary research is realizing that the nature versus nurture debate is dead and that human behavior is shaped by both sets of factors (Udry, 1995). Criminological research, however, appears to be the exception to this general rule as social-environmental explanations continue to dominate the field (Beaver, 2008; Walsh & Ellis, 2004). To be fair, during the past 10 years or so, more and more biological research has penetrated criminology, but it should be noted that it still remains very much on the fringes of the discipline (Beaver, 2009; Cooper, Walsh, and Ellis, 2010; Walsh, 2006). This is especially true for biological explanations that are grounded in an evolutionary framework.

Outside of criminology, however, there has been a significant amount of both empirical and theoretical work tying evolutionary processes to a wide constellation of behaviors (Barkow, 2006; Buss, 2005; Walsh, 2006; Wright, 1994). Known collectively as evolutionary psychology, this framework holds that the behavioral repertoire of the human species is a result of the biological process of selection over eons of evolutionary time (Wright, 1994). Modern evolutionary psychologists have laid the groundwork for the paradigm’s theoretical advances and have established a strong foothold in the social sciences. Just as important is that empirical tests of evolutionary psychology’s central tenets are being undertaken and the results provide some support in favor of the perspective (Buss, 2005; Ellis & Walsh, 1997). For example, researchers making use of this paradigm have analyzed antisocial behaviors such as sexual coercion and rape (Thornhill & Palmer, 2000), homicide (Buss, 2005; Daly & Wilson, 1988), spouse- and partner-directed violence (Kaighobadi, Shackelford, & Goetz, 2009), female violence (Campbell, Muncer, & Biber, 1998), child abuse and neglect (Daly & Wilson, 1988; Ellis & Walsh, 1997), and theft (Duntley & Shackelford, 2008), among others. In addition, researchers have proposed general theories of criminal behavior making use of an evolutionary psychology paradigm (Ellis, 1987; Kanazawa & Still, 2000; Rowe, 1996). This collection of work provides support for the potential for an evolutionary psychological perspective to offer new insights into the underpinnings of antisocial behavior. The present study uses this body of literature as a springboard to examine the link between sexual behavior and criminal conduct from an evolutionary psychological perspective.

Evolutionary Psychology

Evolutionary psychology is a field of study which seeks to illuminate the underlying adaptive functions of specific behaviors and traits. Evolutionary psychologists make use of the Darwinian concepts of natural selection and sexual selection (Cosmides, Tooby, & Barkow, 1992; Mealey, 2000). Natural selection refers to the process of the differential selection of genetic variants which help increase the probability of survival for an organism. Sexual selection, in contrast, refers to the differential selection of genetic variants which increase the probability of being selected as a mate (Buss, 1999; Mealey, 2000). Evolution makes use of both natural selection and sexual selection to produce adaptations, which are specific traits/behaviors that serve to increase the probability of survival, reproduction, or both (Buss, 1999).

Evolutionary psychologists argue that evolution has worked to produce specific psychological adaptations, or mental modules, which aided in the survival and reproduction of our ancestral lineage (Cosmides et al., 1992; Tooby & Cosmides, 2005). Of importance is the recognition that the evolved mental modules are not deterministic but rather are flexible strategies employed in a probabilistic fashion in response to a constellation of environmental factors (Wilson & Daly, 1997). In other words, the psychological adaptations related to survival and reproduction are responsive to specific environmental stimuli that directs behaviors in a manner which resulted, on average, in increased survival and reproduction in ancestral environments (Cosmides et al., 1992).

Evolutionary Psychology and Sexual Behavior

Sexual reproduction is of crucial importance in evolutionary analyses and forms a significant focal point for evolutionary psychology. The mating strategies employed by species are largely the result of the process of sexual selection (Buss, 1999; Mealey, 2000; Schmitt, 2005). Within sexual selection there are two overarching concepts: intrasexual competition and intersexual competition. Each of these mechanisms refers to collections of strategies for increasing reproductive success (Buss, 1999). Specifically, intrasexual competition captures those strategies used to compete with other members of the same sex for sexual access to members of the opposite sex. Whereas, intersexual competition refers to strategies related to the differential or discriminative mate choice by one sex of the opposite sex (Mealey, 2000; Trivers, 1972).

Research on intrasexual competition highlights how differences in mating strategies between males and females can result in differences in typical behavioral outcomes (Weisfeld & Woodward, 2004). For example, males tend to compete with other males by striving for status, wealth, and power within their group by employing strategies which often include aggression and violence (Daly & Wilson, 1988; Kanazawa & Still, 2000; Mazur, 1985; Mealey, 2000; Rowe, 1996; Schmitt, 2005). Conversely, females tend to compete with other females by making use of verbal denigration via gossip, often focused on a rival’s sexual reputation and physical appearance (Buss, 1999; Mealey, 2000; Wright, 1994).

The sex-specific strategies are not differentiated randomly; rather, the strategies are based on the discriminatory preferences for mates of the opposite sex. In other words, the behaviors exhibited in males and females during intrasexual and intersexual competition are a result of evolved preferences for a specific type of mate. These evolved preferences developed over periods of sexual selection in response to the specific selective pressures faced by our ancestors (Buss, 2005; Schmitt, 2005). For example, human females prefer a strong, healthy male who displays characteristics indicating that he can provide resources and protection for her and her offspring (Buss, 1999; Mealey, 2000). Over evolutionary time those males who displayed such traits were more likely to gain sexual access to females and were therefore more likely to pass on the same physiological, psychological, and behavioral traits (Buss, 1999; Mealey, 2000; Schmitt, 2005). As a result, male intrasexual competition is manifested as more behaviorally aggressive (i.e., violent) than female intrasexual competition. Recognition of these sex-differentiated processes provides a theoretical focal point by which to guide the analysis of behavioral outcomes in both males and females. Such guidance can be provided in exploring the associations observed between sexual conduct and antisocial behavior.

Sexual Behaviors and Delinquency

A wealth of research has revealed a robust association between sexual involvement and antisocial conduct (Beaver, Wright, & Walsh, 2008; Capaldi, Crosby, & Stoolmiller, 1996; Ellis & Walsh, 2000; Gottfredson & Hirschi, 1990). In general, antisocial males consistently report having greater numbers of sexual partners and earlier onset of sexual activity when compared with males who are not antisocial (Beaver et al., 2008; Ellis & Walsh, 2000). The association has been shown to hold for females as well. For example, Lederman and colleagues (2004) illustrate that delinquent females also tend to have higher than average number of sexual partners and engage in sex at an earlier age than nondelinquent females (see also Bell, O’Neal, Feng, & Schoenrock, 1999). Reviews of the literature in this area have revealed similar findings. Ellis and Walsh (2000) reviewed 51 studies examining the link between criminal involvement and number of sexual partners. Of these studies, 50 reported statistically significant and positive results, whereby increased criminal involvement was associated with increased number of sexual partners. This review also revealed a statistically significant association between age of onset and criminal behavior, highlighting the tendency for criminals to begin engaging in sexual behavior relatively early in their life (Ellis & Walsh, 2000). The literature therefore illustrates a well-documented connection between the sexual behavior of both males and females and their respective likelihood of engaging in antisocial behavior.

Criminological explanations for the observed covariance of sexual behaviors and criminal conduct tend to focus on environmental factors, such as the influence of peer networks (Armour & Haynie, 2007) or individual-level traits, such as levels of self-control (Gottfredson & Hirschi, 1990). However, recent research suggests that rather than being explanatory variables, such factors may simply be correlates of delinquency (Beaver, 2008, 2009; Beaver, Wright, & DeLisi, 2007; Cleveland, Wiebe, & Rowe, 2005). In other words, the relationship between the proposed explanatory variables (e.g., peer networks and self-control) and sexual behavior and criminal involvement is likely to be a spurious relationship with one or more antecedent factors influencing the observed covariance (Beaver, 2009). Therefore, as correlates of both sexual behaviors and criminal conduct the influence of peers or low self-control are inadequate as explanatory variables. As such, the entire collection of correlates requires an explanatory agent to elucidate the cause of the associations. Evolutionary psychology may help provide such explanatory guidance.

Sexual Behaviors, Delinquency, and Evolutionary Psychology

An example of how evolutionary psychology can help explain the link between sexual behaviors and delinquency is provided by Rowe’s (1996, 2002) “adaptive-strategy theory.” Employing aspects of sexual selection, adaptive-strategy theory highlights the various behaviors associated with different strategies of reproductive effort (see also Mealey, 1995). Reproductive efforts can be divided into effort devoted primarily to the acquisition and sexual-monopolization of mates (mating effort) and effort devoted to the rearing and protection of offspring (parenting effort). These contrasting strategies are manifested by traits which maximize the effort of either strategy. For example, traits which maximize a mating effort strategy include a strong sexual drive, a reduced ability to form strong emotional bonds, a lack of conscience, and aggressive and violent tendencies (Rowe, 1996, 2002; Rowe, Vazsonyi, & Figueredo, 1997). As a result, adaptive-strategy theory holds that the same traits which lead to a high-mating-effort strategy also lead to criminal behaviors (Rowe, 1996, 2002). Consequently, those individuals who possess the traits associated with a high-mating-effort strategy (i.e., sexual promiscuity) are also more likely to engage in antisocial conduct (Rowe, 1996, 2002).

An important aspect of adaptive-strategy theory is the recognition of the role that certain environmental characteristics play in the expression of adaptive traits. As Rowe et al. (1997) note, “[f]or an evolved adaptive strategy to work, the organism must adaptively modulate its behavior by responding systematically to ‘specific’ environmental cues that signal critical contingencies of survival and reproduction” (p. 106). Therefore, it is expected that traits which predispose a person to a high-mating-effort strategy are more likely to be expressed in an environment conducive to such a strategy. Such an environment is characterized by high degrees of unpredictability and low levels of benefit for delayed reproduction (Hill, Ross, & Low, 1997; Rowe et al., 1997). Consequently, adaptive-strategy theory holds that in certain ancestral environments it was reproductively advantageous to possess traits which led to a high-mating-effort strategy and the accompanying antisocial behaviors (Rowe, 1996, 2002).

Current Study

The present study is guided by three main hypotheses. First, given the robust association between sexual behavior and antisocial behavior observed in the literature (Ellis & Walsh, 2000), we expect a positive association between sexual activity and delinquency. Second, given the consistent illustration in the evolutionary psychology literature of variance in mating strategies between men and women (Mealey, 2000; Rowe, 1996, 2002), we expect a difference between males and females in how items derived from an evolutionary psychology perspective are associated with antisocial behaviors. In other words, if the assumption of variance in mating strategies is correct and if the variables derived from an evolutionary psychology perspective are related to antisocial outcomes, it is expected that the manifestation of the relationship will be differentiated by sex. Finally, in accordance with adaptive-strategy theory, we expect that variation in evolutionary psychology-relevant variables (i.e., relative physical development, physical attractiveness, and future outlook) will predict variation in sexual behaviors for both males and females (Rowe, 1996, 2002). More specifically, we expect those males and females who are more physically mature and who have more pessimistic views of the future to also have higher levels of sexual activity. In terms of physical attractiveness, it is expected that males who are more physically attractive will have higher levels of sexual activity whereas females who are less physically attractive will have lower levels of sexual activity. To test these hypotheses, we use data from a large-scale prospective study of Americans.

Method

Sample

Data for this study were drawn from the first two waves of the National Longitudinal Study of Adolescent Health (Add Health). The Add Health study is a longitudinal and nationally representative sample of American students enrolled in Grades 7 through 12 during the 1994-1995 school year (Udry, 2003). Employing a stratified random sampling technique, the Add Health study included 80 high schools and 52 middle schools. Approximately 90,000 students attending these schools were administered the Wave 1 in-school questionnaire. Questions asked to the youths covered topics pertaining to the adolescent’s social life, family life, health status, expectations of the future, and involvement in various activities. A follow-up stratified random sample was also drawn from the school rosters to produce a subsample of 20,745 adolescents who completed the Wave 1 in-home survey. The adolescent’s primary caregiver was also interviewed to gain information on the adolescent and the caregiver. The in-home interview covered topics such as peer networks, education, employment, sexual partners, substance use, and delinquent activities, among others (Harris et al., 2003).

After a 1- to 2-year time lapse, a second wave of questionnaires was administered to 14,738 of the respondents from Wave 1. Questions asked at Wave 2 were similar to those asked at Wave 1 and included items pertaining to physical maturity, sexual behaviors, and involvement in delinquency. The present study analyzed data drawn from the public-use version of the Add Health. The public-use sample consists of 6,504 respondents representing a randomly sampled portion of the core sample (Harris et al., 2003).

Measures

Delinquency Scales

A composite delinquency scale was constructed at both Wave 1 and Wave 2. During Wave 1 interviews, respondents were asked to report how often (in the past 12 months) they had engaged in ten different acts of delinquency, including how often they had stolen items valued at both less than US$50 and more than US$50, how often they had engaged in shoplifting, how often they burglarized a building, how often they had damaged property, how often they had used or threatened to use a weapon, how often they were involved in a fight between two groups of people, how often they seriously injured someone, how often they had pulled a gun or knife on someone, and how often they had shot or stabbed someone. Responses to these items were coded such that 0 = never, 1 = one or two times, 2 = three or four times, and 3 = five or more times. Responses to these items were then summed together to create the Wave 1 delinquency scale (α = .80).

The same questions related to delinquency were asked at Wave 2 and, as a result, the Wave 2 delinquency scale is a duplicate of the Wave 1 delinquency scale. Higher scores on the Wave 2 delinquency scale indicate greater involvement in delinquent behaviors (α = .80). The two delinquency measures were significantly correlated (r = .55, p < .01). Importantly, these delinquency scales are similar to scales used in other studies analyzing involvement in delinquency for this sample (Beaver, 2008; Beaver & Wright, 2005; Guo, Roettger, & Cai, 2008; Haynie, Giordano, Manning, & Longmore, 2005).

Sexual Activity

The Add Health data include an array of questions related to sexual behaviors and sexual health. The sexual activity scale for the present study was composed of two items from Wave 1, both of which were dichotomous-response items (0 = no, 1 = yes). The first item asked respondents if they had ever had sexual intercourse and the second item asked if they had ever touched someone else’s genitals. The responses to these two items were then summed to create the sexual activity scale, with a higher score indicating a greater amount of sexual activity (α = .74).

Total Number of Sex Partners

At Wave 1, respondents were asked to report the total number of people with whom they had ever had a sexual relationship. The respondents were provided with a definition of a sexual relationship as the insertion of a penis into a vagina (Harris et al., 2003). The item is therefore based on the number of sexual encounters wherein insertion occurred and is measured herein as a continuous variable. Other researchers analyzing the Add Health sample have also used this item (Kan, Cheng, Landale, & McHale, 2010).

Attractiveness

Evolutionary psychologists have highlighted an association between physical attractiveness and a range of social behaviors, including sexual activity (Barber, 1995; Buss, 1999). As such, we include a measure of physical attractiveness. During Wave 1 in-home surveys, interviewers rated the physical attractiveness of each respondent. This single-item variable was coded so that 1 = very unattractive, 2 = unattractive, 3 = about average, 4 = attractive, and 5 = very attractive. Research has shown that the distribution of attractiveness in the Add Health sample is similar to the distributions obtained from other samples (Mocan & Tekin, 2010).

Physical Maturity

Social scientists have noted that adolescents who are more physically mature than their peers engage in sexual and criminal behaviors earlier and more often than less physically mature adolescents (Cavanagh, 2004; Felson & Haynie, 2002). In addition, researchers using an evolutionary psychology perspective have also highlighted the importance of relative physical maturity (pubertal development) in the analysis of sexual and antisocial behaviors (Barber, 1995; Udry, 1988; Weisfeld & Woodward, 2004). Accordingly, a physical maturity scale was constructed based on four items from the Wave 1 questionnaire. The physical maturity questions were sex specific. Males were asked to report changes in the amount of hair under their arms, the thickness of their facial hair, if their voice had gotten lower, and their physical development relative to their same-aged peers. Females were asked to report changes in the size of their breasts, their body curvature, whether they had ever menstruated, and their physical development relative to their same-aged peers. The responses to these items were summed to produce a physical maturity scale for males (α = .66) and for females (α = .64). Similar scales have been used in prior research (Beaver & Wright, 2005; Haynie & Piquero, 2006).

Life Certainty

Research conducted by evolutionary psychologists has illuminated the potential reasons behind the associations among socioeconomic status, levels of delinquency, and sexual behaviors by focusing on the perceptions of future outlook (Wilson & Daly, 1997). In addition, life-history theorists have illustrated the relationship between negative beliefs about the future and increased levels of risk-taking (Hill et al., 1997). Consequently, we included a life certainty scale that taps the future outlook of the adolescents. This scale is composed of three items that asked respondents to report their perceived likelihood of being killed by age 21, the likelihood of contracting HIV/AIDS, and the likelihood that they will live to age 35 (reverse coded). These items were coded such that 1 = low and 5 = high and were summed to produce the scale (α = .58). Therefore, a higher score on the life certainty scale indicates a more pessimistic future outlook. The life certainty scale is a reproduction of the Basic Life Certainty Scale used by Caldwell, Wiebe, and Cleveland (2006).

Control Variables

Age and race have been found to be associated with both delinquency (Gottfredson & Hirschi, 1990) and sexual activity (Kan et al., 2010). Therefore, to help control for potentially confounding effects, age and race were included as control variables in all analyses. Age was included as a continuous variable measured in years and race was coded as a dichotomous dummy variable, where 0 = non-White and 1 = White.

Analytical Strategy

We estimate a series of ordinary least squares (OLS) regression models to explore the effects of sexual activity, number of sex partners, attractiveness level, physical maturity, and life certainty on adolescent delinquent involvement. First, we examine the differences between males and females in terms of their respective sexual history, evolutionary psychology-relevant items, and participation in delinquent activities. Second, we examine the effects of the sexual-involvement and evolutionary psychology items on Wave 1 delinquency for both males and females. Next, to assess a longitudinal effect of the sexual involvement and evolutionary psychology items we also analyze delinquency at Wave 2 for males and females. Finally, to assess the effect of the evolutionary psychology items on potential reproductive success, we examine their impact on sexual activity and total number of sex partners.

Results

Given evolutionary psychologists hold that males and females experienced different adaptive problems during the course of evolution, and therefore possess different evolved mental mechanisms, we differentiated our analyses based on sex (Buss, 1999; Mealey, 2000). Initial analyses show that males and females differed significantly in terms of the number of sex partners (t = 8.75, p < .05), their rated levels of attractiveness (t = 9.29, p < .05), and their self-reported levels of delinquency at Wave 1 (t = −16.07, p < .05) and Wave 2 (t = −10.99, p < .05). More specifically, males in this sample had a greater number of sex partners, were rated less attractive, and were involved in more delinquency than females. However, males and females did not differ significantly in terms of sexual activity (t = −1.29, p > .05) or life certainty (t = −1.28, p > .05).

Table 1 displays the results of the OLS regression models predicting Wave 1 and Wave 2 delinquency for males. Model 1 includes the sexual activity measure, the evolutionary psychology variables (i.e., attractiveness, physical maturity, and life certainty), and the control variables. The first model reveals that sexual activity, attractiveness and life certainty are all significant predictors of contemporaneous delinquency. In Model 2, the effect of number of sex partners on delinquency is examined. As can be seen, this measure of sexual involvement is also a significant predictor of Wave 1 delinquency. In addition, similar to Model 1, physical maturity and life certainty are also significant predictors of delinquency.

Table 1.

OLS Regression Models Examining the Associations Between Wave 1 Sexual Involvement, Evolutionary Psychology Measures, and Wave 1 and Wave 2 Delinquency for Males

	Wave 1 delinquency						Wave 2 delinquency
	Model 1			Model 2			Model 3			Model 4
	b	Beta	SE	b	Beta	SE	b	Beta	SE	b	Beta	SE
Sexual involvement
Sexual activity	1.18*	.22	.12				.79*	.17	.11
No. of sex partners				.28*	.26	.02				.11*	.11	.02
Ev. psych. Items
Attractiveness	−.08	−.01	.12	−.06	−.01	.11	−.11	−.02	.11	−.08	−.02	.10
Physical maturity	.14*	.10	.03	.15*	.11	.03	.07*	.06	.03	.09*	.07	.03
Life certainty	.41*	.16	.06	.43*	.17	.05	.31*	.14	.05	.34*	.15	.05
Control variables
Age	−.25*	.09	.06	−.18*	−.07	.06	−.27*	−.11	.06	−.18*	−.07	.06
Race	−.28	−.03	.21	−.11	−.01	.20	−.17	−.02	.19	−.16	−.02	.19
Adjusted R²	.09			.11			.05			.04

Note: Ev. psych. = evolutionary psychology.

p < .05.

To analyze the robustness of the association between sexual involvement and delinquency, we next estimated longitudinal models by using the Wave 2 delinquency scale as the outcome variable. Models 3 and 4 display the results of these analyses. Even in these models that span 1 to 2 years, both sexual activity (Model 3) and number of sex partners (Model 4) remained statistically significant predictors of Wave 2 delinquency. Importantly, physical maturity and life certainty remained statistically significant in the longitudinal models, wherein males who were more physically mature and who reported a more pessimistic future outlook at Wave 1 were also more likely to report engaging in delinquency at Wave 2.

Table 2 displays the regression models predicting Wave 1 and Wave 2 delinquency for the female sample. As was seen with males, both sexual activity and number of sex partners were predictors of Wave 1 delinquency for females. Similarly, females who scored higher on the negative life certainty scale also scored higher, on average, on the Wave 1 delinquency scale. In contrast to males, the attractiveness variable was a significant predictor of delinquency for females in Models 1 and 2. Specifically, females judged to be less attractive had a higher score on the contemporaneous delinquency scale. Furthermore, unlike males, physical maturity only had a statistically significant effect on female delinquency in Model 2 but not Model 1.

Table 2.

OLS Regression Models Examining the Associations Between Wave 1 Sexual Involvement, Evolutionary Psychology Measures, and Wave 1 and Wave 2 Delinquency for Females

	Wave 1 delinquency						Wave 2 delinquency
	Model 1			Model 2			Model 3			Model 4
	b	Beta	SE	b	Beta	SE	b	Beta	SE	b	Beta	SE
Sexual involvement
Sexual activity	.83*	.24	.08				.52*	.17	.07
No. of sex partners				.23*	.19	.02				.12*	.11	.02
Ev. psych. items
Attractiveness	−.23*	−.07	.07	−.14*	−.05	.06	−.20*	.07	.06	−.17*	−.06	.06
Physical maturity	.04	.04	.02	.06*	.06	.02	.05*	.05	.02	.06*	.06	.02
Life certainty	.23*	.13	.04	.22*	.13	.04	.19*	.13	.03	.19*	.12	.03
Control variables
Age	−.28*	−.15	.04	−.19*	−.10	.04	−.28*	−.17	.04	−.22*	−.14	.03
Race	−.36*	−.06	.13	−.38*	−.06	.12	−.26*	−.05	.11	−.25*	−.05	.11
Adjusted R²	.08			.07			.06			.05

Note: Ev. psych. = evolutionary psychology.

p < .05.

Models 3 and 4 of Table 2 display the results of the longitudinal analyses for female delinquency at Wave 2. As illustrated, both sexual activity and number of sex partners remained statistically significant predictors of delinquency at Wave 2. In addition, all three of the evolutionary psychology items were significant predictors of Wave 2 delinquency for females in that females who were rated as less attractive, who were more physically mature, and who had a negative future outlook at Wave 1 scored higher on the delinquency scale 1 to 2 years later at Wave 2.

To examine the indirect effects that the evolutionary psychology variables have on delinquency via their effects on sexual involvement, we next calculated OLS regression models using the sexual-involvement items as the outcome measures. Table 3 displays the results of these models for both males and females. As can be seen, physical maturity and life certainty had statistically significant effects on the sexual-involvement variables for both males and females. In substantive terms, those males and females who were more physically mature and had a more pessimistic view of the future scored higher for sexual activity and number of sex partners. However, the attractiveness variable was only related to sexual activity for females and not for males, meaning that more attractive females had higher levels of sexual activity.

Table 3.

OLS Regression Models Examining Associations Between Wave 1 Sexual Involvement and Wave 1 Evolutionary Psychology Measures by Sex

	Males						Females
	Sexual activity			Total number of sex partners			Sexual activity			Total number of sex partners
	b	Beta	SE	b	Beta	SE	b	Beta	SE	b	Beta	SE
Ev. psych. items
Attractiveness	.02	.02	.02	.01	.01	.10	.07*	.07	.02	−.10	−.04	.06
Physical maturity	.04*	.16	.01	.12*	.09	.03	.05*	.15	.01	.08*	.09	.02
Life certainty	.06*	.13	.01	.25*	.11	.05	.03*	.06	.01	.12*	.08	.03
Control variables
Age	.17*	.32	.01	.40*	.16	.05	.20*	.39	.01	.30*	.19	.03
Race	−.23*	−.13	.04	−1.33*	−.16	.18	−.04	−.02	.03	.12	.02	.11
Adjusted R²	.20			.08			.20			.06

Note: Ev. psych. = evolutionary psychology.

p < .05.

Discussion

A consistent finding in the criminological literature is the association between sexual behaviors and antisocial conduct (Ellis & Walsh, 2000). Explanations of this association have typically focused on environmental influences or individual-level factors. This constellation of proposed explanatory variables emanates from theoretical positions which emphasize the “nurture” component to the etiology of human behavior. The present study provided a contrasting view of the association between sexual behaviors and antisocial conduct by making use of a theoretical paradigm which explicitly integrates explanatory factors that encompass both nature and nurture (Barber, 2007; Walsh, 2000).

Three specific hypotheses were tested using data drawn from the Add Health study. First, we hypothesized that there would be a positive association between sexual behavior and antisocial conduct in both males and females. The results of the present study supported this expectation as the models based on males and females indicated a statistically significant association between sexual behaviors and antisocial behaviors. These associations were detected using a cross-sectional design as well as a longitudinal design that spanned 1 to 2 years of adolescent development.

Our second hypothesis predicted that male and female delinquency would be differentially associated with items derived from evolutionary psychology. Analyses only partially supported this hypothesis. The findings from the sex-limited models converged in terms of life certainty predicting delinquency at both Wave 1 and Wave 2. In addition, physical maturity was a significant predictor for males in all models and for females in three of the four models. There was evidence revealing differential effects for males and females, especially with regard to the effects of attractiveness. Specifically, females who were rated as less attractive scored higher on the delinquency scales at Waves 1 and 2. There was no such association observed for males.

Our third hypothesis predicted a relationship between the evolutionary psychology variables and sexual behaviors. This hypothesis received partial support for males as physical maturity and life certainty predicted sexual activity and number of sex partners. The hypothesis received stronger support for females where all three evolutionary psychology items predicted sexual activity; however, number of sex partners was significantly associated with only physical maturity and life certainty. These findings conform to the research findings produced previously in the evolutionary psychology literature. For example, Wilson and Daly (1997) argued that living in a disadvantaged neighborhood can produce an increased discounting of long-term opportunities and a corresponding increase in risky behaviors. The list of risky behaviors includes aggression and violence, homicide, promiscuous sexual behaviors, and early pregnancy. Wilson and Daly (1997; Daly & Wilson, 2005) argue that these behavioral outcomes are not indicative of pathology or low self-control, but are instead rational outcomes guided by a mental module which interprets local stimuli and calculates a predictive probability of survival and reproduction over the long term. The probabilistic calculations conducted by mental modules in turn produce psychological effects which guide behavioral strategies (Schmitt, 2005; Tooby & Cosmides, 1992; Wilson & Daly, 1997). Given stimuli that indicate a reduced likelihood of survival or reproduction over the long term, individuals opt for a short-term behavioral strategy repertoire (Rowe, 1996, 2002). Therefore, from an evolutionary perspective we would expect that individuals with a pessimistic future outlook would engage in an increased level of both sexual behaviors and antisocial conduct, an explanation that is supported by the findings of the present study.

The results of our study need to be interpreted with caution in light of a number of shortcomings. First, our study employed the use of a composite delinquency scale as an outcome measure. There may be differences in how sexual behaviors and the evolutionary psychology items relate to violent offending or property crime. We should note that we explored this possibility by creating separate scales to tap violent offending and property offending. The results of these supplemental analyses (not presented, but available upon request) were virtually identical to those reported using the composite delinquency scale. Even so, given that past research has uncovered differential effects of evolutionary constructs on specific types of antisocial behaviors (Archer & Thanzami, 2009; Goetz & Shackelford, 2009), future researchers should explore whether different types of offending behaviors have different evolutionary pathways. Second, the majority of the participants in the Add Health study were adolescents during Waves 1 and 2. As such, it may be the case that the observed associations are differentially manifested during adulthood. However, arguably the most valid time frame to explore the association between sexual behaviors and antisocial conduct is during adolescence when individuals are initiating their mating and intrasexual competition strategies.

Keeping these limitations in mind, the present study illustrates the potential benefit of integrating an evolutionary perspective into criminological research. Numerous researchers have called for a greater integration of interdisciplinary approaches into criminological analyses (Beaver, 2009; Rowe, 1987, 2002; Walsh, 1997). The present study represents an example of such integration and highlights the suitability of evolutionary psychology as a guiding framework in conducting empirical analyses of criminological phenomena.

Footnotes

Acknowledgements

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

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

No direct support was received for this analysis.

Bios

Joseph L. Nedelec is a doctoral candidate in the College of Criminology and Criminal Justice at Florida State University. His primary research interests are biosocial criminology and evolutionary psychology and he has published articles on these topics in a variety of journals including Child Psychiatry and Human Development, International Criminal Justice Review, Journal of Criminal Justice, Journal of Research in Personality, and Personality and Individual Differences.

Kevin M. Beaver is an associate professor in the College of Criminology and Criminal Justice at Florida State University. His main area of research is biosocial criminology.

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