Exploring the Gender Differences in Protective Factors

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Jennifer L. Hartman, Michael G. Turner, Leah E. Daigle, M. Lyn Exum and Francis T.Understanding Resiliency

Exploring the Gender Differences in Protective Factors : Implications for

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International Journal of Offender Therapy and

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Exploring the Gender Differences in Protective Factors

Implications for Understanding Resiliency

Jennifer L. HartmanMichael G. TurnerUniversity of North Carolina at Charlotte

Leah E. DaigleGeorgia State University, Atlanta

M. Lyn ExumUniversity of North Carolina at Charlotte

Francis T. CullenUniversity of Cincinnati, Ohio

Understanding the causes of why individuals desist from or are resilient to delinquency and drug use has become a salient social concern. Much research has centered on the effects that protective factors possess in fostering resiliency but that research has not fully explored how the effects of protective factors might vary across gender. Using a sample of 711 individuals from the National Longitudinal Survey of Youth, Child–Mother data set, the authors investigate how individual protective factors vary across gender on two measures of resiliency that document the lack of involvement in serious delinquency and drug use. They also examine whether the accumulation of protective factors varies across gender in fostering resiliency. The findings suggest that although males and females rely on different individual protective factors to foster resiliency, the accumulation of protective factors appears to be equally important for males and females in promoting resiliency. The authors discuss theoretical and policy implications.

Keywords: resiliency; gender; delinquency; drugs

The importance of understanding how risk factors increase the probability of involvement in a variety of problem behaviors has a lengthy history in the crimi-

nological literature (see Farrington & Coid, 2003; Farrington et al., 2006; LeBlanc & Loeber, 1998; Lösel & Bliesener, 1990). In fact, much research has documented the salience of the accumulation of these factors and their effects on further increasing probabilities of involvement in detrimental behaviors (Farrington et al., 2006;



250 International Journal of Offender Therapy and Comparative Criminology

Farrington & Loeber, 1999; Rutter, 1990). In concert with these efforts, the extant research has also documented that a significant proportion of individuals identified as “high risk” are resilient to the pressures and strains that are produced within high-risk environments (Farrington et al., 2006; Laub & Sampson, 2001; Rutter & Giller, 1983; Smith, Lizotte, Thornberry, & Krohn, 1995; Turner, Hartman, Exum, & Cullen, 2007; Werner, 1989a). That is, in spite of their development in high-risk environments, esti-mates indicate that between 25% to 50% of these individuals refrain from involve-ment in delinquency and crime (Werner, 1989a).

Thus, resiliency is not simply abstaining from crime. Rather, it involves the abil-ity of those in high-risk social and personal situations to resist the criminogenic conditions that lead many others into crime. These high-risk youths who are able to refrain from wayward conduct are said to be “resilient.” In turn, a key issue is what differentiates resilient youths from nonresilient youths. One promising explanation is that resilience is made possible by the presence of “protective factors” that insulate individuals from the high risks they confront on a daily basis. Protective factors are typically defined as social and personal resources that encourage prosocial coping in the face of criminogenic conditions. Previous research has found that although indi-vidual protective factors have modest effects, the accumulation of protective factors is clearly related to resilience among high-risk youths. That is, it is the confluence of multiple protective factors that appears to be most central to furnishing youths with the capacity to resist the forces that propel others into crime.

Despite the well-intentioned efforts of scholars seeking to understand how pro-tective factors function to foster resiliency, a noticeable omission in the literature has been a systematized attempt at understanding how the importance of protective fac-tors might vary across gender. Although research has clearly demarcated the impor-tance of certain protective factors in fostering resiliency, relatively little research has sought to investigate whether males and females rely on similar (or different) protec-tive factors while continuing their noninvolvement in delinquency and other problem behaviors (but see Hart, O’Toole, Price-Sharps, & Shaffer, 2007; McKnight & Loper, 2002).

The scarcity of research in this area is somewhat surprising given that past research has documented that gender differences exist in relation to the causes of delinquency (Fagan, Van Horn, Hawkins, & Arthur, 2007; Grasmick, Hagan, Blackwell, & Arneklev, 1996; Triplett & Myers, 1995), the explanation of victimiza-tion (Christiansen & Evans, 2005; Ford, 2002; Kilpatrick, Saunders, & Smith, 2003; Widom & Maxfield, 2001; Wood, Foy, Goguen, Pynoos, & Boyd, 2002), and the

Authors’ Note: This article is the recipient of “The William L. Simon/Anderson Publishing Outstanding Paper Award” for 2009 given by the Academy of Criminal Justice Sciences. This project was supported by Grant 98-IJCX-0026 from the National Institute of Justice. Points of view expressed in this article are those of the authors and do not necessarily represent the official position of the National Institute of Justice. Please address correspondence to Jennifer L. Hartman, 5073 Colvard Hall, Department of Criminal Justice, University of North Carolina at Charlotte, Charlotte, NC 28223-0001; e-mail: [email protected].



causes of alcohol and substance use/abuse (Blume, 1999; Jessor & Jessor 1977; Windle, 1994). Moreover, research has emerged from a developmental perspective identifying gender differences in the various dimensions of offending like onset (Farrington & Painter, 2004; Kovacs, Krol, & Voti, 1994; Moffitt, Caspi, Rutter, & Silva, 2001; Silverthorn & Frick, 1999), seriousness (Lanctot & LeBlanc, 2002), and escalation (Elliott, 1994). According to this perspective, female involvement into certain crime types may include unique psychological, physiological, and sociologi-cal pathways from males (Daly, 1992; Hartman, Listwan, & Schaffer, 2007; Hubbard & Matthews, 2008).

The need to theoretically explore gender differences supports the hypothesis by Hagan, Simpson, and Gillis (1987) that males and females are socialized differently and therefore have different prevalence rates of delinquency and crime. FurthermoreGilligan’s (1982) work in moral psychology asserts that females tend to use an ideol-ogy based on “ethic of care,” centered on relationships that are interconnected around a network of needs and care. Although testing Gilligan’s theory on gender differences is beyond the scope of the current project, it should be expected that gender differences exist in the explanation of the factors that foster and sustain resil-iency. Little research has attempted to document these relationships.

In this context, the present study seeks to fill this void in two important ways. First, we use data from the National Longitudinal Survey of Youth (NLSY; Center for Human Resource Research, 2006) to investigate whether males and females rely on the same (or different) protective factors to foster resiliency against involvement in serious delinquency and drug use. Second, we investigate whether the accumula-tion of protective factors varies across gender to foster resiliency against serious delinquency and drug use. We believe that the combined answers to these questions are useful to the development of theories seeking to explain resiliency, as well as policies designed to promote resiliency. As a prelude to this investigation, we review the literature documenting the importance of understanding gender differences as they relate to risk and protective factors.

Gender Differences in Predictors of Problem Behavior

Scant research has addressed gender differences in protective factors for youths at risk of delinquency. Instead, most research examining the etiology of delinquency across gender has focused on identifying gendered differences in risk factors. Accordingly, it is this body of research that can provide avenues for potential inquiry into the study of gender and resiliency.

One set of factors that have been utilized to explore gender differences centers on individual characteristics such as self-worth and self-esteem. Some research indicates that low self-esteem is a risk factor for female delinquency. In terms of its protective power, Kort-Butler (2006) reports that self-esteem protects girls from delinquency, although it is not a significant factor in protecting boys from engaging in delinquency.

Hartman et al. / Gender Differences in Protective Factors 251



Others have found that delinquent girls perceive themselves, and being female, nega-tively (Artz, 1998), and that female juveniles who are incarcerated are more likely than their male counterparts to endorse low self-esteem statements regarding feelings of uselessness, wanting to have more self-respect, being a failure, and being no good at all (Belknap & Holsinger, 2006). It appears, then, that self-esteem may serve to be a significant protective factor for females rather than for males.

In addition to feelings of self-esteem, research has also targeted the family as a potential gendered correlate of delinquency. The findings about the difference in the importance of the family for males and females are equivocal. Some research indi-cates that attachment to the family (Alarid, Burton, & Cullen, 2000), strong bonds to the family, conflict with parents (Heimer & De Coster, 1999), and parental support of identity (Cernkovich & Giordano, 1987) are stronger predictors of delinquency for females than for males. On the other hand, others have found parental attachment to be more relevant in explaining male delinquency (Anderson, Holmes, & Ostresh, 1999; Canter, 1982; Krohn & Massey, 1980). Still others report no gender differences in the importance of the family, with strong attachment (Heimer, 1996) and conflict with parents (Daigle, Cullen, & Wright, 2007) impacting the likelihood of offending for both males and females. Notably, in a comprehensive examination of gender dif-ferences in the risk factors for delinquency, Moffitt and colleagues (2001) show that deviant mother–child interactions, harsh and inconsistent discipline, relationships with parents, and family conflict are all similarly related to adolescent boys’ and girls’ antisocial behavior. Again, conclusions regarding any gendered effect of the family on delinquency are difficult to make given different sampling strategies, measurement protocol of core concepts across studies, and analytic strategies. Furthermore, it remains to be seen how the family may prove to be protective across gender for youths who are at high risk for delinquency and substance use.

The school as a measure of informal social control and academic performance has also been identified as a potential risk and protective factor. Attachment (Anderson et al., 1999; Daigle et al., 2007) and commitment (Krohn & Massey, 1980) to school as well as participating in extracurricular activities (Hart et al., 2007) have been shown to be important inhibitory influences on female delinquency. Indeed, a recent review concludes that school bonding is a stronger protective factor for females than for males (Payne, Gottfredson, & Kruttschnitt, 2005). Beyond being a mechanism of informal social control, academic performance is also relevant for juvenile offend-ing. One study found that grade point averages were higher for nondelinquent boys, as compared to violent boys (Hart et al., 2007). Not all studies, however, have found gender differences in the effect of schools on delinquency. For example, Agnew and Brezina (1997) found that academic achievement, as measured by high grades, is related to offending for both males and females. Similarly, some research suggests that there is not a differential effect of having educational difficulties across gender (Simourd & Andrews, 1994). Related to the research on family constructs, it is dif-ficult to conclude that the school impacts risk differently for males and females;




however, several studies do show that the school bond is important in understanding female delinquency (see Payne et al., 2005).

Along with the family and school, religion may also provide youth protection from delinquency. Although debate ensues regarding the relationship between religiosity and delinquency (e.g., the relationship may be spurious), some research suggests that religion is inversely related to delinquency (Benda, 1995; Johnson, Jang, Larsen, & Li, 2001) and crime (Evans, Cullen, Dunaway, & Burton, 1995). Less studied has been the effect of religiosity on delinquency for males and females; however, in a review of existing research on the link between religion and delin-quency across gender, Agnew (2005) concluded that religion may have a stronger effect on preventing delinquency for females.

Research also indicates that there are psychological, physiological, and social distinctions that differentiate the antecedents and outcomes between male and female drug users (Hartman et al., 2007). Psychologically female drug users are likely to present with more psychiatric disorders (Brady, Dansky, Sonne, & Saladin, 1998; Denier, Thevos, Latham, & Randall, 1991; Fornari, Kent, Kabo, & Goodman, 1994; Lin et al., 2004), although some report there are gender-specific links in the physiological effects of how males and females process drugs (e.g., the dopamine receptor function; Schindler, Bross, & Thorndike, 2002). Finally, there are also unique social risk factors between male and female drug users. That is, females’ drug use may be reactive (e.g., desire to lose weight, as a coping mechanism), whereas males’ initial drug use may be experimentation (Graham & Wish, 1994; Semple, Grant, & Patterson, 2004). Another unique gendered social concern is that female users are more likely to report physical abuse from intimate partners and others (Cohen et al., 2003; Stanton, Leukefeld, & Logan, 2001).

Research Investigating Resiliency

Although resiliency research has only recently drawn the attention of criminolo-gists, its foundation can be traced to work completed by developmental psycholo-gists and developmental psychopathologists several decades ago (see Garmezy, 1985). Drawing on the evidence suggesting that a majority of individuals within high-risk environments engaged in problem behaviors (Barocas, Seifer, & Sameroff, 1985; Coyne & Downey, 1991; Dubow & Luster, 1990; Farrington et al., 2006; Newcomb, Maddahian, & Bentler, 1986; Rutter, 1979, 1990; Sameroff & Seifer, 1990; Thomas & Chess, 1984; Werner, 1985), Garmezy (1985) and others launched a scholarly effort to understand why it was that a substantial percentage of individu-als were able to cope with the pressures of residing in high-risk environments and never engage in serious delinquency and crime (Rutter, 1985; Werner, 1989b; Werner & Smith, 1992). The product of these efforts has been a lengthy foundation of literature suggesting that individuals who are resilient draw on a variety of protec-tive factors to cope with the detriments associated with high-risk environments and never engage in serious delinquency and crime (Smith et al., 1995; Turner et al.,




2007). Overall, research has documented three general categories of important pro-tective factors: (1) individual, (2) family, and (3) external support systems (see Garmezy, 1985). The literature surrounding each of these areas is presented below.

First, scholars have documented that an individual’s temperament or disposition, particularly measured early in the life course, has been found to foster resiliency in adolescence and adulthood (Kolvin, Miller, Fleeting, & Kolvin, 1988; Thomas & Chess, 1984; Werner & Smith, 1992). Related, Lewis and Looney (1983) found that resilient adolescents scored higher on levels of social maturity and sociability in general than those who were not resilient. In a complimentary fashion, Werner (1993) has found that resilient individuals have a higher likelihood of managing stressful situations through an active versus a passive problem-solving approach; active problem- solving skills at age 10 were one of the best predictors of successful adap-tation to risk early in adulthood. Finally, research has also indicated that individuals scoring higher on measures of self-efficacy, self-confidence, and self-worth have a higher probability of abstaining from involvement in delinquency in particularly high-risk situations (Cicchetti, Rogosch, & Holt, 1993; Garmezy, 1985; Werner, 1990). In sum, individual traits have emerged as some of the strongest predictors in fostering resiliency.

Second, much like the importance of risk factors within the family (see Loeber & Stouthamer-Loeber, 1986), research has also documented a number of protective factors emerging within the familial environment that serve to foster resiliency. For example, high-risk children reared within familial environments that place great emphasis on the supportive and caring relationships occurring between the parent and child have been found to be more likely to be resilient (Weinraub & Wolf, 1983; Werner, 1993). Even in environments with severe familial discord, a supportive rela-tionship with at least one parent has resulted in a higher likelihood of resiliency (Egeland, Carlson, & Sroufe, 1993; Kimchi & Schaffner, 1990). Smith and her col-leagues (1995) have documented that the importance of this parent–child relationship is significant in both directions. That is, resiliency emerged regardless of whether it was the parent or the child who perceived the supportive and caring parent–child relationship.

Third, research has documented that those who are resilient are particularly adept at seeking out individuals external to the family to help them navigate the stress experienced within high-risk environments (Grossman & Garry, 1997; Kellam, Ensminger, & Turner, 1977; Smith et al., 1995; Werner & Smith, 1992). External environments, particularly within institutions of education, have also been found to be important mechanisms for adaptation for individuals residing in high-risk envi-ronments. For example, Herrenkohl, Herrenkohl, and Egolf (1994) have found higher levels of self-worth among resilient individuals attending higher quality or more effective schools. Finally, active involvement in religious institutions has been consistently found to insulate high-risk youths and provide them with stability and meaning to their lives (Anthony & Cohler, 1987; Werner & Smith, 1992). In short,




studies examining resiliency have suggested that a variety of individual, familial, and extrafamilial sources of protective factors have been found to distinguish “resilients” from “nonresilients” within high-risk environments.

Cumulative Effects of Protective Factors

Although the isolated effects of protective factors from different sources have been found to be helpful in buffering or moderating the effects of high-risk environ-ments, it might be expected that much like the effects of risk, the cumulative effects of protective factors might empirically have a more substantial effect. In fact, Smith and her colleagues (1995) have documented the effects of the accumulation of pro-tective factors on individuals within high-risk familial environments. The analyses indicated that individuals exposed to at least eight protective factors were 4 times more likely to be resilient than those exposed to fewer than five protective factors. Similarly, Jessor and his colleagues have documented the positive effects that mul-tiple protective factors have within high-risk environments (Jessor et al., 1995). Specifically, among a sample of adolescents, the impact of protection was more significant when levels of risk were high and was often insignificant when levels of risk were low. Using national data, Turner and his colleagues (2007) have also found that a protective factor index that documents the accumulation of protective factors significantly (and positively) impacted resiliency from delinquency and drug use. In short, much like the effects of risk, this emerging body of research generally sug-gests that as protection accumulates, individuals are more likely to remain resilient.

Current Focus

Despite the recent proliferation of research focusing on the importance of pro-tective factors in fostering resiliency, one area that has received relatively scant attention is whether protective factors possess differential effects across gender. Admittedly, some of the extant research has explored the gender-specific effects of protective factors; however, much of this research has centered on understanding whether protective factors have gender-specific effects on various types of offend-ing and victimization (see Christiansen & Evans, 2005; Fagan et al., 2007). Much less research has focused on the gender-specific effects in fostering resiliency and this research has not systematically differentiated whether protective factors vary across gender. For example, although McKnight and Loper (2002) found several important protective factors in promoting resiliency among females, they did not investigate whether these factors were important for males. Similarly, Hart and her colleagues (2007) found that four protective factors (extracurricular activities, an aggressive response to shame, parental responsiveness, and parental demand-ingness) significantly distinguished violent offenders, nonviolent offenders, and nonoffenders (i.e., resilient individuals). No gender-specific differences across the




protective factors emerged within the analysis; however, it should be noted that this study was not focused specifically on high-risk individuals.

In light of the research presented above, this study isolates a high-risk cohort and seeks to understand how protective factors function to insulate youths from involve-ment in delinquency and drug use. Using a cohort of high-risk youth is appropriate because we should expect to observe the effects of protective factors to emerge within this cohort. More specifically, this investigation proceeds along three fronts. First, an investigation is made into whether individual protective factors possess similar (or different) effects across gender on fostering resiliency. Second, in light of the research documenting the importance of the accumulation of protective factors in fostering resiliency (see Turner et al., 2007), an investigation is made inquiring whether gender similarities (or differences) exist in the cumulative effects of protec-tive factors in fostering resiliency. Third, the two investigations above are undertaken across two different measure of resiliency: (1) resiliency against serious delinquency and (2) resiliency against drug use. Consistent with the extant research, we estimate models for each of these behaviors separately. Because it has been reported that the causes of delinquency and crime are not necessarily the same as the causes of drug use, we feel the separate analyses add breadth to the resiliency literature (see Smith et al., 1995; Turner et al., 2007). In short, the results of this study should provide insight into whether the individual and cumulative effects of protective factors are general or specific in nature across two distinct, but related measures of resiliency.

Method

Data

This study is based on analyses of data from Wave 1 through Wave 6 of the merged Child–Mother data set of the NLSY (Center for Human Resource Research, 2006). The NLSY is a prospective longitudinal study supported by the U.S. Department of Labor. It was first administered in 1979 to 12,686 individuals ages 14 to 21 so as to assess their labor market experiences as they completed high school and entered the workforce. A separate data collection effort began in 1986 that included detailed assessments in 2-year intervals of each child born to the female youths of the original 1979 cohort. As of 1996 (Wave 6), these children (n = 7,103) represent a cross-section of individuals born to a nationally representative probabil-ity sample of women between 29 and 36 years of age as of January 1, 1996 (Center for Human Resource Research, 2000).

Sample

The data used in this study were restricted to a subsample of 711 individuals who were the offspring of the original female participants. Two conditions guided the




selection of the sample. First, the sample was restricted to only those individuals who were classified as eligible to be interviewed as a “young adult” in Waves 5 and 6 (1994 and 1996). This restriction was necessary because our measure of resiliency relied on delinquency and drug use items that were only measured on these partici-pants in these two waves. Second, the sample was restricted to the “young adults” who completed a valid interview in each of the six waves (1986 to 1996). This sec-ond condition was applied to ensure that the measures of risk were captured in waves that preceded the measures of protection. As such, the combined effect of these two conditions resulted in a final sample of 711 individuals who were between the ages of 16 and 23 during Wave 6 of the survey administration.1 Of these 711 young adults, 426 are considered here to be “high-risk” individuals, which we define as having at least four risk factors for delinquency.2 It is important to note the designation of “high-risk” individuals because the subsequent analyses are conducted only on those individuals within the “high-risk” cohort. Notably, the approach to identifying high-risk individuals used in this study is consistent with prior research (see Rutter, 1979; Smith et al., 1995; Turner et al., 2007).

Measures

Dependent variable—Resiliency. Consistent with previous research, we investi-gate both resilience to delinquency/crime and resilience to drug-related offenses (Smith et al., 1995). Resiliency to delinquency/crime is defined as those high-risk individuals having no prior involvement in serious criminal behavior during Wave 3 through Wave 6.3 Similarly, resiliency to drug-related offenses is defined as those high-risk individuals having no prior self-reported involvement in drug-related behaviors (across all waves). In line with measures employed in previous resiliency studies, each of these measures allows for the involvement in minor forms of prob-lem behaviors (Smith et al., 1995; Werner, 1989a). The specific items used for each of these measures are reported in the appendix.

Independent variables—Risk and protection. In general, research investigating resiliency involves two sets of important measures: (1) measures of risk and (2) measures of protection. The approach used in this study was first to identify a sample of individuals who were defined as “high risk.” Within the NLSY, seven risk factors were identified from previous research to increase the probability of delinquency and drug use. To be temporally consistent, the measures of risk were taken from waves prior to the measures of protection.4 Consistent with prior research (Farrington & Loeber, 1999), risk factors measured with continuous variables were subsequently dichotomized to represent the presence or absence of that factor. Individuals scoring above the mean were identified as possessing the risk whereas individuals scoring below the mean were identified as not possessing the risk.5




Alternatively, protective factors are those variables that are hypothesized to inhibit or insulate youths from involvement in detrimental behaviors. As Garmezy (1985) indicates, these factors are not just the opposite ends of the continuum of risk factors. Rather, protective factors are hypothesized to have an effect on the outcome behavior that is above the inclusion of risk factors alone. Although the risk factors were dichotomized to isolate a high-risk sample, in many of the subsequent analyses, the protective factors were left in their original metric form, which was continuous in nature. To clarify, risk factors were dichotomized to develop a system of opera-tionalizing an “at-risk” or “high-risk” youth cohort. A classification scheme for understanding the individual impact of protective factors is not needed so the analy-sis below takes full advantage of the continuous nature of such factors.

To investigate the cumulative effects of protective factors, however, a protective factor index (PFI) was constructed by dichotomizing each protective factor at its mean and summing across individuals. This measurement protocol allowed individu-als to score between 0 and 8 on the PFI (M = 4.167, SD = 1.806).6 A detailed descrip-tion of the risk and protective factors and their metrics is outlined in Table 1.

Control variables. To control for the effects that demographic variables have on fostering resiliency, several control variables are included in the subsequent analy-ses. These variables include age, race, gender, and number of years of education. The age and number of years of education variables are taken at Wave 6 and measured continuously in years.7 Race and gender are dummy variables where for race two dummy variables were created with Whites and Hispanics identified as the “1” val-ues and African Americans identified as the “0” value. In terms of the gender vari-able, the category “female” equals “0” and the category “male” equals “1.”

Table 2 lists the demographic characteristics of the unweighted sample of youth. As seen in Table 2, the average age of the sample at Wave 6 is 17.9 years with an average of 10.8 years of education. The sample is split nearly evenly across gender, with approximately one third (33.2%) classified as White. The non-Whites within the sample were distributed as 49.2% Black and 17.6% Hispanic. Approximately three fourths of the sample (78.5%) have prior involvement in delinquency during Waves 3 through 6, and nearly half (46.6%) had prior involvement in drug use. In short, these figures indicate that 21.5% of the sample was resilient to delinquency, whereas 53.4% of the sample was resilient to drug use.8

Findings

Bivariate Differences in Protective Factors

As a first step in this analysis, Table 3 presents the mean differences for each protective factor between those evidencing resiliency and those not evidencing




259

Tabl

e 1

Des

crip

tion

of

Mea

sure

s

Mea

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escr

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SD

R

ange

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ctor

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1 =

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child

is 1

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bel

ow (

Nag

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199

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49

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mily

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e 1 =

Four

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mor

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n (D

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& L

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r, 19

90)

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olve

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t in

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ehav

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0.

69

0.46

0-

1

Non

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1 =

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orce

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ving

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al.,

199

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tudy

chi

ld

0.35

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48

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ancy

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oz

(Tib

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999)

0.

12

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fact

ors

Self

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7 4.

30

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(R

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lpha

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6

Self

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Six-

item

mea

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take

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lf-P

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16

.92

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take

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3.52

6-

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se

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(Har

ter,

1982

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e-sp

ecif

ic p

erce

ntile

sco

res

on th

e m

athe

mat

ics,

0.

00

1.00

−

1.86

-2.3

7

re

adin

g co

mpr

ehen

sion

, and

rea

ding

rec

ogni

tion

sect

ions

of

the

Pe

abod

y In

divi

dual

Ach

ieve

men

t Tes

t

Posi

tive

scho

ol

Five

-ite

m m

easu

re o

f yo

uth’

s pe

rcep

tions

of

scho

ol s

afet

y

14.5

6 2.

63

6-20

en

viro

nmen

t

and

teac

her

invo

lvem

ent,

alph

a =

.56

C

ogni

tive

stim

ulat

ion

Twel

ve-i

tem

mea

sure

of

mat

erna

l rep

orts

and

inte

rvie

wer

obs

erva

tions

of

95

.22

15.1

7 43

.3-1

24.5

th

e qu

ality

of

the

hom

e en

viro

nmen

t in

prov

idin

g co

gniti

ve

stim

ulat

ion

for

child

ren

E

mot

iona

l sup

port

T

hirt

een-

item

mea

sure

of

mat

erna

l rep

orts

and

inte

rvie

wer

obs

erva

tions

of

96

.60

14.6

9 39

.1-1

22.1

pare

ntal

invo

lvem

ent i

n em

otio

nal s

uppo

rtin

g th

e st

udy

child

R

elig

iosi

ty

Stan

dard

ized

two-

item

mea

sure

rep

ortin

g th

e st

udy

child

’s le

vel o

f im

port

ance

0.

00

1.71

−2

.97-

2.81

of

rel

igio

n an

d th

e fr

eque

ncy

of a

ttend

ing

relig

ious

ser

vice

s, a

lpha

= .6

4



resiliency within females and males. This analysis is important because it provides evidence at the bivariate level whether resilients possess significantly greater levels of a protective factor within subgroups defined by gender. Beginning with the resil-iency against delinquency measure, the bivariate analyses suggest that male resil-ients scored significantly higher than male nonresilients only on the measure of self-perceived scholastic competence (17.71 to 15.94, p < .05). It should be noted, however, that five of the remaining seven protective factors were in the predicted direction (i.e., resilients possessing a greater amount of the protective factor com-pared to nonresilients). Turning to the investigation focusing on females, the results in Table 3 indicate that female resilients scored significantly higher than female nonresilients on measures of religiosity (0.72 to −0.12, p < .01) and positive school environment (16.04 to 14.05, p < .01). Similar to males, all but one of the remaining five protective factors was in the predicted direction.

Turning to the resiliency against drug use measure, the results in Table 3 indicate that male resilients scored significantly higher than male nonresilients on measures of religiosity (0.20 to −0.38, p < .05) and cognitive stimulation (95.82 to 90.16, p < .01). Notably, all of the remaining mean differences were in the predicted direction. Focusing on the females, the results in Table 3 indicate that female resilients scored significantly higher than female nonresilients on measures of religiosity (0.38 to −0.35, p < .01), positive school environment (15.35 to 14.11, p < .01), self-per-ceived scholastic competence (17.02 to 15.61, p < .05), and self-perceived global self-worth (19.85 to 18.12, p < .01). Again, all but one of the remaining four factors


Table 2Descriptive Statistics of Unweighted Young-Adult Analytic Sample

Variable Description M SD Range n

Wave 1 Age of youth 7.746 1.575 6-13 709 Male 0.512 0.500 0-1 711 African American 0.492 0.498 0-1 711 Hispanic 0.176 0.464 0-1 711 White 0.332 0.471 0-1 711Wave 6 Age of youth 17.972 1.581 16-23 711 Years of education 10.833 1.403 6-16 708 Marital status (1 = married) 0.028 0.166 0-1 709Waves 3-6a Prevalence of delinquency 0.785 0.411 0-1 711Waves 1-6 Prevalence of drug involvement 0.466 0.499 0-1 711

a. The mean age of the respondents at Wave 3 was 11.64 and the mean age for the respondents at Wave 6 was 17.97.



261

Tabl

e 3

Mea

n C

ompa

riso

ns o

f P

rote

ctiv

e F

acto

rs A

cros

s G

ende

r

R

esili

ent A

gain

st D

elin

quen

cy

Res

ilien

t Aga

inst

Dru

g U

se

M

ales

Fe

mal

es

Mal

es

Fem

ales

R

esili

ent

Not

Res

ilien

t R

esili

ent

Not

Res

ilien

t R

esili

ent

Not

Res

ilien

t R

esili

ent

Not

Res

ilien

t

Prot

ectiv

e Fa

ctor

s M

SD

M

SD

M

SD

M

SD

M

SD

M

SD

M

SD

M

SD

Self

-est

eem

32

.91

4.15

32

.48

4.45

33

.32

4.83

31

.88

4.10

33

.20

4.06

32

.08

4.59

32

.53

4.29

31

.74

4.25

Rel

igio

sity

−0

.63

2.04

−0

.06

1.75

0.

72

1.49

−0

.12*

* 1.

70

0.20

1.

76 −0

.38*

1.

78

0.38

1.

66 −0

.35*

* 1.

65Po

sitiv

e sc

hool

13

.86

2.92

14

.07

2.83

16

.04

1.99

14

.45*

* 2.

93

14.1

1 3.

08

13.9

8 2.

65

15.3

5 2.

56

14.1

1**

3.00

envi

ronm

ent

Self

-per

ceiv

ed

17.7

1 3.

41

15.9

4*

4.09

16

.75

4.70

16

.31

4.24

16

.86

3.79

15

.69

4.17

17

.02

4.00

15

.61*

4.

60

sc

hool

com

pete

nce

Self

-per

ceiv

ed

20.3

8 3.

44

20.2

3 3.

34

20.0

3 3.

71

18.8

5 4.

06

20.7

9 2.

83

19.8

3 3.

65

19.8

5 3.

82

18.1

2**

4.06

glob

al s

elf-

wor

thC

ogni

tive

stim

ulat

ion

94.0

5 14

.59

92.4

2 15

.32

91.0

1 13

.99

90.6

5 15

.76

95.8

2 14

.68

90.1

6**

15.1

9 90

.93

15.2

8 90

.48

15.6

0E

mot

iona

l sup

port

98

.84

14.2

2 93

.98

16.7

4 89

.28

15.5

3 90

.25

18.2

0 97

.02

15.5

2 92

.97

16.9

7 87

.96

17.9

2 92

.31

17.2

5A

cade

mic

com

pete

nce

0.56

1.

03 −0

.24

1.26

0.

76

0.98

0.

19

1.04

0.

02

0.93

−0

.12

1.19

0.

63

0.92

−0

.03

0.88

*p <

.05.

**p

< .0

1.



was in the predicted direction. Combined, the results at the bivariate level indicate that several protective factors distinguish resilients from nonresilients within sub-groups defined by gender. It should also be noted that nonresilients never possessed significantly greater levels of a protective factor than resilients.

Investigation of Gender Differences in Protective Factors: Resiliency Against Delinquency

The analytic strategy to address the gender differences question at the multivari-ate level will proceed in two stages. First, a full model will be contrasted with two gender-specific models by comparing the −2 log likelihoods. That is, if the absolute difference between the summed −2 log likelihoods of the gender-specific models and from the −2 log likelihood of the full model exceeds the critical chi-square value at degrees of freedom equal to the number of independent variables in the model plus the constant, then this would suggest that the processes giving rise to resiliency dif-fer across gender. In the second stage, assuming that the null hypothesis is rejected, a comparison of coefficients test using the formula presented in Clogg et al. (1995) will be conducted to identify where the differences exist. This analysis will be con-ducted using each individual protective factor and the cumulative measure of protec-tion (as measured by the PFI) described earlier.

Table 4 reports the results of three logistic regressions (one for the full sample and one separately for males and females) predicting resiliency against self-reported delinquency using the demographic control variables and each of the eight protective factors. Beginning with the model predicted on the full sample, although each of the protective factors, with the exception of self-perceived scholastic competence, is in the predicted direction, none had a significant influence in predicting resiliency. In fact, the only variables reaching conventional levels of significance were gender and being White. That is, males (B = −.83, p < .05) and those who were White (B = −.92, p < .05) were less likely to be resilient against self-reported delinquency. As such, the results in the full model presented in Table 4 suggest that the independent effects of the protective factors in predicting resiliency against self-reported delinquency appear to be relatively trivial.

Turning to the gender-specific models, Table 4 suggests that two protective fac-tors for females emerge as significantly increasing resiliency and each is in the predicted direction. That is, religiosity (B = .32, p < .05) and positive school environment (B = .17, p < .05) are both positively related to being resilient against self-reported delinquency. In terms of an effect size, the odds ratios (ORs) indicate that each unit change in our religiosity measure corresponded with a 1.38 increase in the likeli-hood that an individual would remain resilient. In terms of one’s positive school environment, each unit change corresponded with a 1.19 increase in the likelihood they would remain resilient. Examination of the model using only males revealed no significant predictors. The absolute difference between the summed −2 log




263

Tabl

e 4

Log

isti

c R

egre

ssio

n C

oeff

icie

nts

Pre

dict

ing

Res

ilien

cy f

or S

elf-

Rep

orte

d D

elin

quen

cy:

F

ull M

odel

and

Gen

der-

Spec

ific

Mod

els

Gen

der-

Spec

ific

Mod

els

F

ull M

odel

(n =

426)

Fe

mal

es (

n =

203)

M

ales

(n =

223)

Var

iabl

e B

SE

W

ald

Exp

(B

) B

SE

W

ald

Exp

(B

) B

SE

W

ald

Exp

(B)

Age

−.

01

.10

.02

1.01

.0

3 .1

3 .0

6 1.

03

−.01

.1

7 .0

1 .9

9M

ale

−.83

.2

9 8.

01*

.44

—

—

—

—

—

—

—

—H

ispa

nic

.47

.40

1.40

1.

60

.85

.56

2.32

2.

33

.03

.71

.01

1.03

Whi

te

−.92

.4

5 4.

20*

.40

−1.1

2 .7

2 2.

41

.33

−.46

.6

1 .5

7 .6

3Y

ears

edu

catio

n .0

5 .1

2 .1

7 1.

05

−.03

.1

6 .0

4 .9

7 .1

9 .2

1 .8

3 1.

21Se

lf-e

stee

m

.02

.04

.45

1.02

.0

3 .0

5 .3

7 1.

03

−.01

.0

6 .0

1 1.

00R

elig

iosi

ty

.09

.09

1.01

1.

10

.32

.14

5.00

* 1.

38

−.17

.1

4 1.

48

.85

Posi

tive

scho

ol e

nvir

onm

ent

.05

.06

.89

1.06

.1

7 .0

9 3.

92*

1.19

−.

07

.09

.68

.93

Self

-per

ceiv

ed s

choo

l com

pete

nce

.01

.04

.02

.99

−.06

.0

5 1.

28

.94

.10

.07

1.82

1.

10Se

lf-p

erce

ived

glo

bal s

elf-

wor

th

.07

.04

2.24

1.

07

.10

.06

2.78

1.

10

.01

.08

.01

1.00

Cog

nitiv

e st

imul

atio

n .0

1 .0

1 .3

1 1.

01

−.01

.0

1 .1

9 .9

9 .0

3 .0

2 1.

76

1.03

Em

otio

nal s

uppo

rt

.01

.01

.49

1.01

.0

1 .0

1 .2

6 1.

01

.01

.02

.32

1.01

Aca

dem

ic c

ompe

tenc

e .1

5 .1

7 .8

0 1.

17

.15

.24

.37

1.16

.1

4 .2

7 .2

8 1.

15C

onst

ant

−6.0

8 2.

48

5.99

*

−6.2

3 3.

56

3.06

*

−8.2

0 4.

02

4.16

* −2

log

likel

ihoo

d 34

2.04

3

178.

655

14

3.41

6

C

hi-s

quar

e 28

.343

28.3

30

12

.847

p

.008

.005

.380

M

odel

pre

dict

ion

rate

84

.24%

79.8

0%

88

.74%

*p <

.05.



likelihoods of the gender-specific models from the full model (19.972) fails to exceed the critical chi-square value at 14 degrees of freedom (χ2 = 23.68), thus sug-gesting that significant differences do not exist between females and males in the predictors of resiliency.

Table 5 reports the results of the logistic regression predicting resiliency against self-reported delinquency using the demographic control variables and the PFI that measure the cumulative effects of protective factors. Similar to the analysis investi-gating the individual effects of protective factors, two demographic control variables, gender and White (compared to African American), emerged as significantly related to resiliency against self-reported delinquency. That is, males (B = −.81, p < .05) and those who were White (B = −.96, p < .05) were significantly less likely to be resil-ient. As Table 5 also reveals, however, the addition of the PFI (B = .30, p < .05) emerged as a strong predictor of resiliency and in the predicted direction. Individuals with a greater number of protective factors were more likely to be resilient against self-reported delinquency. The results presented in Tables 4 and 5 together suggest that although protective factors might only have trivial independent effects, their cumulative effect is both significant and fairly robust.

Turning to the gender-specific models, the results in Table 5 identify two signifi-cant predictors for the female model and one significant predictor for the male model. For females, being White (B = −1.62, p < .05) is inversely related to being resilient against self-reported delinquency whereas scoring higher on the PFI (B = .29, p < .05) is positively related to being resilient. For the male subgroup, only higher levels of the PFI (B = .33, p < .05) corresponded with a higher likelihood of being resilient. Notably, in terms of effect sizes, the ORs indicate that each unit change in the PFI corresponded with moderate (1.33 for females and 1.39 for males) increases in the likelihoods that an individual would remain resilient (Long, 1997). The absolute dif-ference between the summed −2 log likelihoods of the gender-specific models from the full model (5.297) fails to exceed the critical chi-square value at 7 degrees of freedom (χ2 = 14.07), thus suggesting that differences do not exist between females and males in the cumulative predictors of resiliency against delinquency.

Investigation of Gender Differences in Protective Factors: Resiliency Against Drug Use

As displayed in Table 6, with the exception of years of education, each of the demo-graphic control variables were inversely related to resiliency. Again, older individuals (B = −.22, p < .05), males (B = −.57, p < .05), those who are Hispanic (B = −1.02, p < .05) and those who are White (B = −1.17, p < .05) are significantly less likely to be resilient. Turning to the eight protective factors, Table 6 also suggests that all but two of the protective factors, self-esteem and academic competence, were in the predicted direction. More importantly, two of the protective factors were signnifi-cantly related to resiliency in the predicted direction. Individuals with higher levels of




265

Tabl

e 5

Log

isti

c R

egre

ssio

n C

oeff

icie

nts

Pre

dict

ing

Res

ilien

cy f

or S

elf-

Rep

orte

d D

elin

quen

cy:

F

ull M

odel

and

Gen

der-

Spec

ific

Par

sim

onio

us M

odel

s

Gen

der-

Spec

ific

Par

sim

onio

us M

odel

s

Fu

ll M

odel

(n =

426)

Fe

mal

es (

n =

203)

M

ales

(n =

223)

Var

iabl

e B

SE

W

ald

Exp

(B

) B

SE

W

ald

Exp

(B

) B

SE

W

ald

Exp

(B

)

Age

.0

4 .0

9 .1

7 1.

04

.08

.13

.50

1.09

−.

03

.15

.07

.96

Mal

e −.

81

.28

8.07

* .4

5 —

—

—

—

—

—

—

—

His

pani

c .4

3 .3

8 1.

28

1.54

.5

2 .4

8 1.

15

1.68

.3

9 .6

4 .3

9 1.

48W

hite

−.

96

.41

5.44

* .3

8 −1

.62

.64

6.46

* .2

0 −.

21

.56

.14

.81

Yea

rs e

duca

tion

.01

.12

.01

1.01

−.

09

.15

.35

.92

.16

.19

.70

1.18

Prot

ectiv

e Fa

ctor

Ind

ex

.30

.09

11.2

9*

1.36

.2

9 .1

2 5.

84*

1.33

.3

3 .1

4 5.

26*

1.39

Con

stan

t −3

.27

1.64

3.

95*

−2

.89

2.30

1.

58

−4

.55

2.43

3.

50

−2 lo

g lik

elih

ood

339.

770

18

8.75

1

145.

722

Chi

-squ

are

30.6

15

18

.234

10.5

41

p .0

00

.0

03

.0

61

Mod

el p

redi

ctio

n ra

te

84.2

4%

79

.31%

88.7

4%

*p <

.05.



266

Tabl

e 6

Log

isti

c R

egre

ssio

n C

oeff

icie

nts

Pre

dict

ing

Res

ilien

cy f

or S

elf-

Rep

orte

d D

rug

Use

:

Ful

l Mod

el a

nd G

ende

r-Sp

ecif

ic M

odel

s

Gen

der-

Spec

ific

Mod

els

Fu

ll M

odel

(n =

426)

Fe

mal

es (

n =

203)

M

ales

(n =

223)

Var

iabl

e B

SE

W

ald

Exp

(B)

B

SE

Wal

d E

xp (B

) B

SE

W

ald

Exp

(B

)

Age

−.

22

.08

8.06

* .8

0 −.

28

.12

5.70

* .7

5 −.

17

.11

2.75

.8

4M

ale

−.57

.2

2 6.

66*

.56

—

—

—

—

—

—

—

—H

ispa

nic

−1.0

2 .3

5 8.

74*

.36

−1.6

2 .5

4 9.

01*

.20

−.66

.4

8 1.

91

.52

Whi

te

−1.1

7 .2

9 16

.12*

.3

1 −2

.05

.49

17.7

0*

.13

−.65

.3

9 2.

79

.52

Yea

rs e

duca

tion

.15

.09

2.53

1.

16

.21

.14

2.10

1.

23

.08

.13

.37

1.08

Self

-est

eem

−.

01

.03

.03

1.00

−.

01

.04

.01

1.00

.0

1 .0

4 .0

4 1.

01R

elig

iosi

ty

.12

.07

3.29

1.

13

−.03

.1

2 .0

6 .9

7 .1

5 .0

9 2.

76

1.16

Posi

tive

scho

ol e

nvir

onm

ent

.03

.04

.50

1.03

.1

3 .0

7 3.

56*

1.14

−.

04

.06

.52

.96

Self

-per

ceiv

ed s

choo

l com

pete

nce

.04

.03

1.54

1.

04

.07

.05

2.32

1.

07

.02

.04

.18

1.02

Self

-per

ceiv

ed g

loba

l sel

f-w

orth

.0

7 .0

3 4.

07*

1.07

.0

7 .0

5 2.

02

1.07

.0

5 .0

5 1.

17

1.05

Cog

nitiv

e st

imul

atio

n .0

2 .0

1 4.

25*

1.02

.0

2 .0

1 1.

83

1.02

.0

2 .0

1 2.

54

1.02

Em

otio

nal s

uppo

rt

.01

.01

.19

1.00

−.

01

.01

.10

1.00

.0

1 .0

1 1.

10

1.01

Aca

dem

ic c

ompe

tenc

e −.

10

.13

.57

.91

−.18

.2

1 .7

7 .8

3 .0

2 .1

8 .0

1 1.

02C

onst

ant

−1.0

4 1.

83

.33

−1

.64

2.98

.3

0

−1.5

9 2.

49

.41

−2 lo

g lik

elih

ood

518.

158

22

2.80

4

278.

752

Chi

-squ

are

70.9

02

56

.437

25.4

65

p .0

00

.0

00

.0

13

Mod

el p

redi

ctio

n ra

te

67.5

3%

73

.40%

65.3

2%

*p <

.05.



self-perceived global self-worth (B = .07, p < .05), and cognitive stimulation (B = .02, p < .05) were more likely to be resilient against self-reported drug use. The ORs indi-cate that each unit changes in self-perceived global self-worth and cognitive stimula-tion corresponded with moderate (1.07 and 1.02) increases in the likelihoods that an individual would remain resilient.

Table 6 also reports the results of the gender-specific models predicting resiliency against drugs. The findings in Table 6 reveal four significant factors for the female model and zero significant factors for the male model. Beginning with the female sample, three of the demographic control variables are significant in the inverse direction. Older individuals (B = −.28, p < .05), Hispanics (B = −1.62, p < .05), and Whites (B = −2.05, p < .05) each were significantly less likely to be resilient. Females with school environments that are positive (B = .13, p < .05), however, have a greater likelihood of being resilient. Similar to those reported above, the OR for positive school environment was positive, yet relatively trivial (OR = 1.14). Turning to the male model, none of the protective factors emerged as significantly predicting resiliency against drug use. The absolute difference between the summed −2 log likelihoods of the gender-specific models from the full model (16.602) fails to exceed the critical chi-square value at 14 degrees of freedom (χ2 = 23.68), thus sug-gesting that significant differences do not exist between females and males in the predictors of resiliency.

Table 7 reports the results of the logistic regression predicting resiliency against self-reported drug use using the demographic control variables and the PFI. As reflected in Table 7, four of the demographic control variables are each significantly and inversely related to resiliency for the full sample. That is, older individuals (B = −.20, p < .05), males (B = −.52, p < .05), Hispanics (B = −1.06, p < .05), and Whites (B = −1.26, p < .05) are less likely to be resilient. In addition, the PFI had a positive effect on resiliency. Therefore, individuals were more likely to be resilient as protec-tive factors accumulated (B = .21, p < .05). In terms of effect size, for each unit change in the PFI, the odds of remaining resilient are 1.24 times greater. Similar to the models predicting resiliency against self-reported delinquency, these data sug-gest that although protective factors might only have trivial independent effects, their cumulative effects are both significant and fairly robust.

Table 7 also reports the results of the gender-specific models predicting resiliency against drug use. Specifically, the results in Table 7 reveal four significant predictors for the female model and two significant predictors for the male model. For females, older individuals (B = −.24, p < .05), being Hispanic (B = −1.58, p < .05), and being White (B = −2.11, p < .05) are each inversely related to being resilient against self-reported drug use. For males, older individuals (B = −.20, p < .05) corresponded to being less likely to be resilient against drug use. Scoring higher on the PFI, however, positively corresponded with being resilient for both females (B = .21, p < .05) and males (B = .23, p < .05). Again, the ORs for females and males were positive indicat-ing that each additional protective factor increased the odds of remaining resilient




268

Tabl

e 7

Log

isti

c R

egre

ssio

n C

oeff

icie

nts

Pre

dict

ing

Res

ilien

cy f

or S

elf-

Rep

orte

d D

rug

Use

:

Ful

l Mod

el a

nd G

ende

r-Sp

ecif

ic P

arsi

mon

ious

Mod

els

Gen

der-

Spec

ific

Par

sim

onio

us M

odel

s

Fu

ll M

odel

(n =

426)

Fe

mal

es (

n =

203)

M

ales

(n =

223)

Var

iabl

e B

SE

W

ald

Exp

(B

) B

SE

W

ald

Exp

(B

) B

SE

W

ald

Exp

(B

)

Age

−.

20

.07

7.91

* .8

2 −.

24

.11

4.59

* .7

9 −.

20

.10

4.07

* .8

2M

ale

−.52

.2

1 6.

21*

.59

—

—

—

—

—

—

—

—H

ispa

nic

−1.0

6 .3

3 10

.08*

.3

5 −1

.58

.49

10.2

9*

.21

−.62

.4

6 1.

86

.54

Whi

te

−1.2

6 .2

6 23

.18*

.2

8 −2

.11

.40

27.6

3*

.12

−.51

.3

5 2.

07

.60

Yea

rs e

duca

tion

.12

.09

1.96

1.

13

.18

.13

1.93

1.

20

.09

.12

.59

1.09

Prot

ectiv

e Fa

ctor

Ind

ex

.21

.07

11.0

7*

1.24

.2

1 .1

0 4.

21*

1.24

.2

3 .0

9 6.

84*

1.25

Con

stan

t 2.

22

1.23

3.

26

2.

53

1.98

1.

63

1.

64

1.59

1.

07

−2 lo

g lik

elih

ood

528.

824

23

2.62

8

285.

668

Chi

-squ

are

60.2

36

46

.613

18.5

49

p .0

00

.0

00

.0

02

Mod

el p

redi

ctio

n ra

te

67.2

9%

72

.91%

64.4

1%

*p <

.05.



(OR = 1.24 and 1.25, respectively). The absolute difference between the summed −2 log likelihoods of the gender-specific models from the full model (10.528) fails to exceed the critical chi-square value at 7 degrees of freedom (χ2 = 14.07), thus sug-gesting that differences do not exist between females and males in the cumulative predictors of resiliency against drug use.

Discussion

A sustained research agenda has emerged that documents the importance that protective factors possess in fostering resiliency. A noticeable absence in this agenda has involved any systematized effort to understand whether protective factors vary, in terms of their importance, across gender. The present study has attempted to fill this void by investigating whether the individual and cumulative effects of protective factors are similar (or different) across males and females. Three key findings have emerged from these efforts.

First, across each of the measures of resiliency at the bivariate level, the data indicated that male and female resilient individuals possessed significantly higher levels of several protective factors. Although notable consistent patterns of important protective factors across gender failed to materialize, it is important to highlight that nonresilient individuals never possessed significantly more of a protective factor than did resilient individuals. Combined, these findings suggest that the protective factors used in this study operate in the predicted direction and, more importantly, were often able to distinguish between resilients and nonresilients.

Second, across each of the measures of resiliency at the multivariate level, the data indicated that males and females relied on different sets of protective factors to remain resilient. Specifically, in terms of the resiliency from delinquency measure, certain pro-tective factors (i.e., the religiosity and positive school environment measures) signifi-cantly influenced resiliency among females but not males. Alternatively, the analyses investigating the protective factors associated with being resilient from drug use indi-cated that a positive school environment measure was positive and significant for females but not for males. None of these differences, however, were significant across gender. That is, although some protective factors were seemingly important for males and unim-portant for females (and vice versa), significant differences never emerged across gender. These findings suggest that the effects of protective factors appear to be general, rather than gender-specific, in nature.

It is noteworthy to add that the data appear to suggest that the minor differences are driven, at least in part, by the reversal of signs for these protective factors between the females and males. For example, although females resilient to delinquency tend to have significantly higher levels of religiosity and attend school in a more positive environment, these protective factors do not seem to be important for males. In fact, although not significant, each of these factors is inversely related to the likelihood of




being resilient for males. This pattern also emerged in the analysis documenting the protective factors predicting resiliency against drug use. Specifically, although a posi-tive school environment and religiosity were significant for females and males, respec-tively, the signs were in the inverse direction for the nonsignificant gender category.

Third, consistent with the assessment of the individual effects of protective fac-tors, the analyses investigating the cumulative effects of protective factors also sug-gested that their effects were general in nature. Specifically, the measure of the accumulation of protective factors was significant and positive for both males and females across each measure of resiliency. Despite one’s gender, this investigation indicates that those who are resilient generally accumulate a greater number of pro-tective factors. Combined, these two findings provide significant evidence that, unlike other behaviors that possess gender-specific predictors, the factors associated with being resilient appear to be general in nature. This finding is of particular importance because it extends previous research on the importance of the accumula-tion of protection (see Turner et al., 2007) by uncovering these identical effects separately across males and females.

In terms of implications for policy, the findings from the present study suggest that policies promoting or fostering resiliency might be more effective if they were general in nature (vs. gender-specific). In contrast to suggestions that a “one size fits all” approach is ineffective at reducing delinquency (see Frabutt & White, 2002, MacKinnon-Lewis et al., 2002), programs or efforts to instill resiliency among indi-viduals in high-risk environments can target the development and sustainment of certain protective factors regardless of the individual’s gender. It would likely follow that these polices or programs would be less complex given that males and females do not significantly differ in the factors they utilize to remain resilient. In addition, the findings related to the cumulative effects suggest that policies might be most effective if they target the development of multiple protective factors from a variety of different domains.

Although a theory of resiliency, to our knowledge, has yet to be developed, the findings from the present study also suggest that the development of such a theory could be considerably simplified by not having to account for gender-specific path-ways to become or remain resilient. In fact, much like theories of delinquency and other problem behaviors, a theory of resiliency that focuses on the generality of the predictors would be likely to account for a significant proportion of the explained variation in resiliency. Such a theoretical approach would inevitably result in a more simplistic theoretical model that is likely to yield a greater number of empirical tests by scholars investigating resiliency.

Although this research has uncovered several key contributions to whether pro-tective factors possess general or specific effects across gender, it is important to acknowledge the limitations of these efforts. First, because the delinquency measures— and consequently the resiliency measures—are left censored, it is possible that at




least one of our measures of resiliency may be somewhat incomplete. That is, because youths younger than the age of 15 within the NLSY are not requested to respond to delinquency items, the measurement of resiliency was only examined at two consecutive points in time in mid-adolescence and into adulthood. This meas-urement strategy is problematic because it is possible that individuals may be involved in delinquency and crime either prior to or subsequently following the waves used in this study. As such, we encourage future scholars to extend measures of resiliency into these important developmental periods.

Second, the categorization of a high-risk sample in this study was restricted to those individuals experiencing risks early in the life course. The purpose of this restriction was twofold: (1) to identify the effects of protective factors subsequent to the experience of risk and (2) to maximize the effects of risk during a developmental period (i.e., childhood) when individuals are particularly susceptible to the impacts of risks. The unintended drawback of this approach, however, is that risks are often age graded and may be experienced at each stage of development. Future researchers are encouraged to explore different categorizations of high-risk samples so as to investigate the generalizability of the present research findings. In short, future stud-ies considering risk factors such as delinquent peers and peer pressure would sub-stantially contribute to our understanding of resiliency.

Third, the restrictions of the data did not permit an investigation of the effects of a number of other known and effective protective factors that have been documented in prior research. For example, future studies should examine such factors as an individual’s problem-solving skills, coping strategies, temperamental characteristics, locus of control, educational aspirations, and parental competence (Rutter, 1990, Smith et al., 1995, Thomas & Chess, 1984, Werner, 1989a). Exploring whether these effects are general or gender-specific would aid in attempts at articulating a theory of resiliency.

In closing, it is no mistake that research efforts seeking to understand why high-risk individuals are capable of refraining from the pressures to participate in delin-quency and crime are gaining momentum from scholars within a variety of disciplines. Arguably, the knowledge gained by this research is potentially critical in the development of policies and programs designed to prevent delinquency, crime, and other problem behaviors. Considering the variability that has been documented showing that the causes of delinquency and crime often vary by gender, it is hoped that the results of the present study provide beginning insight into the gender- specific nature of the effects that protective factors possess in fostering resiliency. Although the findings from the present study point to the generalities of protective factors across gender, by no means have the gender effects been explored among all protective factors. It would be prudent of future researchers to explore such effects in hopes of further refining for whom the effects are strongest. Such clarification will ultimately be invaluable to scholars seeking to articulate theoretical attempts at explaining resiliency.




Appendix

Delinquency Items: In the past year have you ever . . . 1. intentionally damaged or destroyed property of others? 2. got into a fight at school or work? 3. taken something without paying for it? 4. taken something worth under $50? 5. taken something worth more than $50? 6. used force to get money from someone? 7. hit or seriously threatened someone? 8. attacked someone with the idea of seriously hurting or killing them? 9. tried to con someone?10. taken a vehicle without the owner’s permission?11. broken into a building or vehicle to steal something?12. knowingly sold or held stolen goods?13. helped in a gambling operation like running numbers or books?

Drug Items: In your lifetime, on how many different occasions have you used . . . 1. marijuana or hashish? 2. glue, gas, or other fluids? 3. powder cocaine? 4. crack cocaine? 5. LSD, uppers, and downers?

Notes

1. The effects of sample attrition were examined. The analyses indicate that the attrition subsample was significantly more conventional on three measures, less conventional on one measure, and exhibited no differences on the remaining six measures examined. In particular, the sample used in the subsequent analyses was more likely to be involved in delinquency (78% vs. 57%) and involved in drug related offenses (47% vs. 31%). In addition, the attrition subsample was more likely to have an adolescent mother (67% vs. 58%) but less likely to have a mother who was involved in prior criminal activity (62% vs. 69%). Demographically, the attrition subsample was older, more educated, and consisted of fewer Whites. These differences suggest that the sample used for the subsequent analyses was more likely to manifest problem behaviors but was not at a significantly greater risk than the attrition subsample.

2. As discussed below, the risks used in this study include adolescent motherhood, family size, paren-tal deviance, nonintact marriage, persistent poverty, maternal smoking during pregnancy, and child’s low birth weight.

3. It should be noted that measures of serious forms of delinquency were unavailable prior to Wave 3. It is unlikely, however, that serious forms of delinquency were committed by the sample given that the mean age during Wave 1 was 7.7 and the mean age for the sample during Wave 2 was 9.8.

4. To be clear, each static risk factor (i.e., low birth weight, adolescent motherhood, maternal smoking during pregnancy) was measured during the wave in which the event occurred (i.e., typically during Wave 1). Each dynamic risk factor (i.e., persistent poverty, family size, parental deviance, and nonintact mar-riage) was measured during each of the waves that preceded the collection of the protective factors. In other words, no risk factor was measured during the same wave as the measurement of a protective factor (all of which were measured in Wave 6). Prior research suggests it is important to measure risk prior to




protection because the effects of risks are not always immediate (see Smith et al., 1995). Measurement of risks at waves prior to the measurement of protection allows for the effects of the risks to occur and have an impact on the individual.

5. We explored the effects of different methods to categorize our risk factors (i.e., top quartile of the distribution) but we found only trivial differences. As such, we elected to use the method consistently used in previous research.

6. All protective factors were coded so that higher scores reflected more protection.7. Age is also considered a control variable for “time at risk.” As such, we would expect it to be

negatively related to resiliency because older individuals have more of an opportunity to engage in delin-quency and crime.

8. Multicollinearity does not appear to be a problem in the subsequent analyses. In fact, the highest cor-relation was between self-perceived scholastic competence and academic competence (r = .44, p < .01).

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Exploring the Gender Differences in Protective Factors

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