Extending Social Learning Theory to Explain Victimization Among Gang and Ex-Gang Offenders

Abstract

This study is among the first to extend and test social learning theory’s ability to understand property and violent victimization. It specifically tests whether aspects of definitions, differential reinforcement, and differential association/modeling can explain the three types of victimization of gang members: actual experience, perception of likelihood, and fear. The sample consists of over 300 male and female gang members incarcerated in jails throughout Florida. The results show that all three types of victimization can be explained by the three aspects of social learning theory.

Keywords

victimization crime theory social learning theory gangs victim–offender overlap

Gang members adapt to many of their experiences based on their perceptions of the actions of those around them. Their reaction to victimization may be one such adaptation. For scholars, the relationship between gang membership and increased risk for victimization is well known (DeLisi, Barnes, Beaver, & Gibson, 2009; Melde, Taylor, & Esbensen, 2009; Taylor, Peterson, Esbensen, & Freng, 2007). Yet, it appears that while gang members are aware of their increased risk, they are not fearful of victimization (Lane & Fox, 2012). In fact, many gang members even cite protection as a reason for joining (Decker & van Winkle, 1996; Peterson, Taylor, & Esbensen, 2004).

Researchers know that gang members are at a greater risk for victimization than non-gang members and that there is a notable difference in victimization rates between gang members, non-gang members, and ex-gang members (Peterson et al., 2004; Taylor et al., 2007). This link between offending and victimization is referred to as the victim–offender overlap and has been established in several works (Gottfredson, 1981; Jennings, Piquero, & Reingle, 2012; Lauritsen, Sampson, & Laub, 1991). In short, the victim–offender overlap suggests that those who engage in delinquent and criminal behavior put themselves into riskier situations, thereby increasing their likelihood of victimization. For instance, gang members have been found to have higher rates of both property and personal victimization than non-gang members (Fox, Lane, & Akers, 2010). Ex-gang members do see a reduction in their victimization; however, they are still at an increased risk compared with those without any gang affiliations (Peterson et al., 2004). Types of victimization range from intergang-related victimization to victimization associated with gang-related activity. Examples of intergang-related victimization include having to go through a “beat-in” process as a part of their initiation and use of violence to punish fellow gang members who have disobeyed the rules (Decker & van Winkle, 1996; Padilla, 1993). Victimization associated with gang activity could include possessing items desirable to potential robbers (e.g., drugs or money) and being at risk for retaliation from rival gangs (Esbensen & Winfree, 1998; Howell & Gleason, 1999; Huff, 1999; Jacobs, 2000; Sanders, 1994; Taylor et al., 2007). However, in spite of its importance, comparatively little research has been conducted on the victimization of gang members (Decker, Katz, & Webb, 2008; Melde et al., 2009), and applying criminological theory to explain the increased victimization among gang members is relatively absent from the extant literature (for review, see Fox, 2013).

This article addresses the issues by extending social learning theory to explain victimization. Social learning theory states that the modeling of actions and behaviors are determined by a person’s definition of that action or behavior as favorable, the differential reinforcement by others, and the differential associations between the action or behavior and positive outcomes (Akers, 1973; Bandura, 1977). The likelihood of modeling occurring is significantly affected by the duration of the exposure to these factors (Jensen & Brownfield, 1986). A more in-depth explanation of the theory will be presented in the literature review below.

Victimization—of any population—is problematic and should be of importance to the public as well as to criminologists. Using criminological theories to explain victimization is a recent trend in criminology, although it is largely undertested (Schreck, 1999; Stewart, Elifson, & Sterk, 2004). The importance of applying criminological theory to victimization is threefold. First, use of criminological theories to explain victimization may introduce knowledge regarding offending behavior due to the victim–offender overlap. Second, there are policy implications regarding prevention and support that could be implemented from such findings. Third, extending criminological theories to victimization will lend itself to expanding the generalizability of the theory. Overall, extending criminological theory strengthens multiple facets of criminological research.

In extending criminological theory to address victimization, consideration is given to Christopher Schreck’s (1999) advice regarding the necessity of the theory’s ability to explain both personal and property crimes. Finally, while limited, this article also keeps in mind concerns that have been noted in past research geared at applying criminological research to victimization studies.

This is among the first studies to use the aspects of social learning theory to attempt to explain gang victimization. Using a sample of over 300 male and female gang members in Florida jails, this article applies the aspects of definitions, differential reinforcement, and differential association/modeling to address the likelihood of victimization among this population compared with their ex-gang counterparts. The author posits that definitions favorable to victimization, peer and family influence, and the imitation of such behaviors are directly linked to the increased experience, perception of likelihood, and fear of victimization among current gang members. Essentially, this study examines if social learning theory can explain victimization differences between current and ex-gang members.

Literature Review

Gang Victimization

Extant literature tells us that gang members have an increased risk of being victimized (DeLisi et al., 2009; Melde et al., 2009; Taylor et al., 2007). This has been found true for both personal and property crimes (Fox et al., 2010). Furthermore, gang members are found to have higher victimization rates than either non-gang members or ex-gang members, with ex-gang members having the second highest rate of victimization of the three groups (Peterson et al., 2004). Interestingly, one study found that ex-gang members were the only group to see a decrease in their experienced victimization over a 4-year period (Melde et al., 2009). Ironically, many gang members cite protection as one of their biggest motivators for their gang affiliation (Decker & van Winkle, 1996; Peterson et al., 2004).

Some scholars believe that the connection between gang members and their increased likelihood for victimization is similar to the explanation for the increased likelihood of victimization for those engaged in other types of delinquent activity. Delinquent activities shape lifestyles that inherently include an increased exposure to risk factors and a decreased exposure to protective factors (Taylor et al., 2007). Other characteristics affecting victimization risk include family factors and demographic variables (Jensen & Brownfield, 1986; Sampson & Lauritsen, 1990; Spano, Freilich, & Bolland, 2008). However, gangs that depict greater organization also show evidence of increased victimization (Decker et al., 2008), indicating that there may be a difference in victimization risk between those who engage in delinquent activity and those who are members of a more structured group geared toward delinquency.

While social learning theory has yet to be applied to the victimization of gang members, several scholars have utilized other theories to explain the gang-victimization link (Fox, 2013). One such theory is the lifestyle/routine activity theory. In relation to the gang-victimization link, lifestyle/routine activity theory claims that a person’s characteristics and behaviors can determine their likelihood for victimization through their increased interactions with motivated offenders and lack of capable guardians making them suitable targets (Cohen & Felson, 1979; Hindelang, Gottfredson, & Garofalo, 1978). The link between lifestyle/routine activity theory and gang victimization was established in two works (Spano et al., 2008; Taylor, Freng, Esbensen, & Peterson, 2008) and supported by the idea of collective liability in which the very structure of gangs promotes violence and victimization (Decker, 1996). Social disorganization, which is exemplified by low socioeconomic residents of varying racial backgrounds, is another theory used to explain the gang-victimization link by stating that aspects of perceived social disorganization were related to victimization of gang members (Fox et al., 2010; Fox, Rufino, & Kercher, 2012).

Furthermore, though limited, the biosocial approach was used to explain the gang-victimization link. One article found that genetic factors, followed by nonshared environmental factors (i.e., environmental factors that cause dissimilarities between siblings), were significantly related to increased victimization among gang members (Barnes, Boutwell, & Fox, 2012). Finally, attempts at using self-control theory to explain the gang-victimization link have been made. Briefly, the self-control theory posits that persons with low self-control, when presented with the opportunity, will commit a criminal act (Gottfredson & Hirschi, 1990). In one study, a link between high self-control and property victimization was found among gang members (Fox et al., 2012). However, a different study using a youth sample found no connection between self-control and gang members’ victimization at all (Childs, Cochran, & Gibson, 2009).

Social Learning Theory

The aspects of social learning theory have been applied to many areas of research, both within criminology and outside of the field, such as business management, computer science, and environmental studies (Manz & Sims, 1980; Tu, 2000; Webler, Kastenholz, & Renn, 1995). Social learning theory claims that definitions favorable to a behavior, the differential reinforcement of the behavior, and the differential association with that behavior will increase a person’s probability of committing—or modeling—that behavior (Akers, 1973; Bandura, 1977). Specifically, definitions favorable to a behavior refers to the actor’s expressed approval or disapproval of the behavior. Differential reinforcement is the expressed approval or disapproval of the behavior as observed by the actor. This can also include any costs or benefits that the actor believes are procured by engaging in the behavior. Differential association refers to any anticipated effects of participating in the behavior along with the actor’s perception of others’ engagement in the behavior. Finally, modeling is when the actor observes others, whom he or she respects, engaging in the behavior. For the purposes of this article, and in following with recent trends in criminology, variables indicating modeling have been encompassed within differential association.

Social learning states that proximity and exposure to those accepting a behavior increases the probability that the actor will imitate the behavior (Jensen & Brownfield, 1986). The most influential groups tend to be peers and family (Akers, Krohn, Lanza-Kaduce, & Radosevich, 1979; Fox, Nobles, & Akers, 2011). According to a meta-analysis, differential association and definitions have been found to have the strongest predictive power among all of the social learning theory factors (Pratt et al., 2010, p. 788). It is through these associations that the social environment is developed allowing the other factors of social learning to take shape.

This notion is indirectly reflected in the “intergenerational cycle of violence” model. This proposition claims that being exposed to violence—whether experienced or observed—can affect attitudes toward those behaviors in the future (Laner & Thompson, 1982; Marshall & Rose, 1988; Steinmetz, 1977). For example, a child exposed to abuse in the home at an early age has an increased likelihood of replicating violent behaviors or being susceptible to being victim to the behaviors in the future.

Typically, social learning theory has been applied to explaining offending behaviors as opposed to patterns of victimization. Nonetheless, social learning has evident ability to explain victimization. For one, victims generally seek support after their victimization from their peer networks. This has direct ties to the concepts of differential reinforcement and differential association. Second, as social learning has been presented as a general theory, the similarities between learning criminal behaviors and learning behaviors that can increase the risk of victimization are evident (Fox et al., 2011). While there have been some attempts to expand social learning theory to explain victimization (see Foshee, Bauman, & Linder, 1999; Fox et al., 2011), efforts have not been fully exhausted.

Extending Social Learning Theory to Explain Victimization

Extending criminological theory to explain victimization has three beneficial consequences for criminology scholars. First, as previously stated, there is clear evidence that there is a link between victims and offenders in their characteristics and lifestyles. Moreover, the “intergenerational cycle of violence” suggests that offending may be a result of past victimization experiences (Kalmuss, 1984; Mihalic & Elliott, 1997; Straus, Gelles, & Steinmetz, 1982). Addressing the similarities between victims and offenders as well as the transfer of violent attitudes from offender to victim can advance our knowledge on the subjects of not just victimization but offending behaviors as well. In doing so, scholars can apply these findings to guide the second consequence for this line of research.

The second consequence is that there are multiple policy implications that could be derived from using criminological theory to explain victimization (Fox et al., 2011). If researchers can establish the connections between victims and offenders, prevention measures for both populations can be aimed in a more effective direction. We can also use this knowledge to aid those who would benefit from specialized support measures. Furthermore, in recognizing the victim–offender overlap, policies directed at decreasing victimization may reduce gang activity as well (Fox, 2013). Third, in extending criminological theory to explaining victimization, we can improve the generalizability of the theory as a whole.

Still, even with these improvements, researchers must be guided in their effort by prior research. For example, Schreck (1999) has stated the necessity for a theory to be able to explain both property and personal crimes while being applied to the current knowledge of victimization. This article takes this suggestion into consideration and furthers the idea by separating victimization into three types: violent, property, and gang-related victimizations.

Measures

Sample

Questions implemented for this study were derived from a survey distributed to nearly 2,500 inmates in 14 separate jails throughout the state of Florida in 2008 and 2009. Of this population, approximately 300 claimed gang membership. The survey was implemented on-site at the participating jails. Due to limitations faced during the distribution period, no responses were collected from inmates with mental disorder or who were in specialized punitive confinements.

The survey asked questions involving both perceived and experienced exposure to offending and victimization. The survey also asked questions that were geared toward measuring multiple theoretical concepts. Multiple demographic variables were included in the survey as well.

It must be acknowledged that social learning theory was not one of the theoretical concepts directly considered in the survey. Readers may derive from this a limitation of the present study. However, Akers himself lends credibility to the use of such data for this purpose when he stated that empirical findings could be considered supportive of the theory if the variables used were based on an operationalization of the aspects of social learning theory (Akers & Jensen, 2006).

Dependent Variables

The survey used for this research included 14 items to measure victimization. For the purposes of this study, victimization was separated into three indexes: violent victimization, property victimization, and gang-related victimization. Responses indicating victimization were summed together and then divided by the number of items to create these indexes of victimization. Gang-related victimization being separated from violent and property crimes was guided by previous research that has done the same (see Lane & Fox, 2012; Lane & Fox, 2013; Lane & Meeker, 2003). Each response to questions of victimization was coded dichotomously. Responses indicating no victimization were assigned a zero for that category.

Violent victimization questions asked whether the respondent had ever been the victim of a robbery, been attacked with a weapon, attacked without a weapon, raped, stabbed, shot, shot at, or threatened with a weapon, during and after the duration of their membership. Responses were coded as Yes = 1 and No = 0 for each time period.

Property victimization included theft and vandalism, during and after the duration of their membership. Again, responses were coded as Yes = 1 and No = 0 for each time period.

Finally, gang-related victimization questions included carjacking, home invasion, drive-by shooting, and witness intimidation, during and after the duration of their membership. Once again, responses were coded as Yes = 1 and No = 0 for each time period.

Independent Variables

The independent variables for this research were selected to reflect the aspects of social learning theory. These aspects are definitions, differential reinforcement, and differential association/modeling. Note that differential association and modeling have been combined into one measurement. In total, five questions were chosen to represent these aspects.

The first question selected to measure definitions was “Are you currently or have you ever been in a gang?” The variable was renamed “Membership” and responses were coded as “Gang Member” = 1 and “Ex-Gang Member” = 0. Those who responded that they were not and never have been in a gang (i.e., non-gang members) were excluded from the analysis.

The second question selected to measure definitions was “When you get out of jail, do you plan on staying in the gang?” The variable was renamed “After_Jail_Membership.” “Get out” collapsed several responses which were “I would like to get out,” “I will get out,” and “I would like to get out, but can’t.” This selection was made based on definitions reflecting approval or disapproval of the behavior. Responses were coded as “Stay In” = 1 and “Get Out” = 0. Once again, respondents who claimed that they were not or never have been in a gang were excluded from the analysis.

Recall that the social learning aspect of definitions refers to an individual’s approval or disapproval of a certain behavior. By identifying as a gang member, the argument is that this person holds definitions favorable to gang membership. Furthermore, by claiming that one intends to continue gang membership after release from incarceration, the individual portrays definitions favorable of gang membership through their expressed desire to remain a member. Likewise, those who state a desire to discontinue their membership are portraying definitions unfavorable toward gang membership.

Differential reinforcement was measured by asking the respondent “How much of a problem was people who looked like they are in gang in your neighborhood?” Responses were coded as “Not a Problem” = 0, “Some Problem” = 1, and “Big Problem” = 2. Again, recall that differential reinforcement is the expressed approval or disapproval of a behavior. Viewing gang activity in one’s neighborhood as a problem expresses some level of disapproval of that activity.

Differential reinforcement was also measured by asking “After you joined a gang, what was good about it?” The variable was renamed “Benefits.” “Peers” included responses such as “Friends were members” and “Family were members.” “Other” responses indicating the benefits of membership were “Protection,” “Respect,” “Money,” “For Fun,” and “Other.” Responses were coded as “Peers” = 1 and “Other” = 0. Recall, too, that differential reinforcement can include any costs or benefits that the individual believes will result from their actions in a given activity. By asking what was good about the individual’s membership, this variable taps into their beliefs about the benefits of their gang membership.

In the original statement of the theory, differential association and modeling were listed as two separate aspects. In recent studies, these two aspects have been combined into one. This was replicated in this article and will from hereon be referred to as differential association/modeling. The variable was measured by asking “Why did you first join a gang?” and was renamed “Motivation.” “Peers” included such responses as “Friends were members” and “Family were members.” “Other” responses indicating their reasons for joining included “Protection,” “Respect,” “Money,” “For Fun,” and “Other.” Responses were coded as “Family and Friends” = 1 and “Other” = 0. Differential association/modeling, again, refers to an individual’s perception of the effects of participation in a certain activity as well as the individual’s understanding of others’ engagement in that behavior. Here, the variable “Motivation” measures differential association/modeling by capturing the individual’s perception of the anticipated effects of their membership as well as recognition of their peers’ gang involvement.

Control Variables

There were four demographic variables selected for control. These control variables were “sex” (Male = 0, Female = 1) and “race” (White = 1, Non-White = 0). Age was also included as a continuous variable.

Analytic Strategy

The analyses proceeded in three stages. First, descriptive qualities of the current and former gang members are presented. Second, bivariate analyses feature significant differences. Finally, a series of multivariate models were estimated to examine the extent to which social learning explains (a) victimization, (b) perceived risk of victimization, and (c) fear of victimization.

Results

Descriptive Results

Table 1 presents the descriptive statistics among current and ex-gang members. A bivariate analysis and a chi-square test were conducted to evaluate the differences between the two groups. The majority of both groups were non-White males; however, there was a significant difference in the age of either group. That is, current members tended to be younger than ex-gang members.

Table 1.

Descriptive Statistics Among Current and Ex-Gang Members.

	Current members (n = 145; 39%)	Ex-members (n = 225; 61%)
Female*	13 (9%)	40 (19%)
White*	88 (42%)	41 (29%)
Age* (M)	Range 18-43 (24)	Range 18-59 (30)
Neighborhood gang problem
Not a problem	44 (12%)	70 (19%)
Some problem*	35 (10%)	87 (24%)
Big problem*	65 (18%)	63 (17%)
Future membership plan
In*	62 (33%)	6 (3%)
Out*	51 (14%)	37 (10%)
Not sure*	26 (7%)	7 (2%)
Reasons for joining	81 (56%)	130 (62%)
Benefits from joining	70 (52%)	115 (59%)
Property victimization	73 (50%)	116 (52%)
Violent victimization	45 (31%)	56 (25%)
Gang victimization*	101 (71%)	106 (48%)
Likelihood of property victimization	46 (33%)	63 (29%)
Likelihood of violent victimization*	46 (33%)	43 (20%)
Likelihood of gang victimization*	62 (44%)	54 (25%)
Fear of property victimization*	8 (6%)	26 (12%)
Fear of violent victimization	46 (33%)	65 (31%)
Fear of gang victimization*	11 (8%)	32 (15%)

p < .05.

The majority of current and former gang members were men (91% and 81%, respectively). Almost three quarters of the current gang members and just over half of the ex-gang members were non-White (71% and 58%, respectively). The average age was 24 years for current and 30 years for ex-gang members, and this age difference was statistically significant. About a third of both current (30%) and ex-gang members (31%) believed gangs were not a problem in their neighborhoods, yet most of both groups believed gangs were a problem. Ex-gang members believed neighborhood gangs were significantly more likely to be “some problem” in their neighborhood compared with current members (39% vs. 24%). Current gang members thought gangs were a “big problem” in their neighborhood significantly more than ex-gang members (45% vs. 28%).

Over half of both current and ex-gang members responded that friends and family influenced their decision to join a gang (56% and 62%, respectively). Similarly, over half of both current and ex-gang members found that family and friends were a benefit of joining a gang (52% and 59%, respectively). However, neither of these findings was statistically significant.

For both current and ex-gang members, gang victimization was statistically significant, with 44% of current and 25% of ex-gang members reporting experiencing this type of victimization. However, property victimization was experienced by more ex-gang members than gang victimization (52% vs. 48%). Violent victimization was the least experienced victimization type for current and ex-gang members (31% and 25%, respectively).

Perceived risk of gang victimization was the most common victimization type for current gang members (44%). For current gang members, perceived risk of property and violent victimization risk were equal (33%), while for ex-gang members perceived risk of property victimization (29%) was greater than their perceived risk of both violent and gang victimization (20% and 25%, respectively). Perceived risk of violent and gang victimization were both statistically significant.

Finally, violent victimization was the most commonly feared victimization type with 33% of current and (31%) ex-gang members reporting fear of this type of victimization. Fear of gang victimization was the second most common victimization type among both current and ex-gang members (8% and 15%, respectively), followed by property victimization (6% and 12%, respectively). Fear of property and gang victimization were both statistically significant.

Multivariate Results

Binary logistic regression models were estimated to examine the effects of (a) victimization, (b) perceived risk, and (c) fear of victimization. All models are estimated separately among current and ex-gang members to uncover any group differences. The analyses proceeded in three stages. For each of the dependent variables (victimization, perceived risk of victimization, and fear of victimization), models are featured among current and ex-gang members in terms of (a) property victimization, (b) violent victimization, and (c) gang victimization.

Table 2 presents the results of the logistic regression for victimization experiences. Ex-gang members who believed gangs were not a problem in their neighborhood were significantly (α = 0.1) more likely to be property crime victims. Not seeing gangs as a problem (differential reinforcement) increasing the likelihood of being victimized could indicate that an individual seeing gangs as nonproblematic is derived from their perception that those around them accept the gang life. As previously discussed, acceptance of gang life and related activities leads to victimization through the perception that victimization is an accepted part of life.

Table 2.

Logistic Regression of Experienced Property Victimization, Violent Victimization, or Gang-Related Victimization Among Current and Ex-Gang Members.

	Property victimization		Violent victimization		Gang victimization
	Current member	Ex-member	Current member	Ex-member	Current member	Ex-member
	b (SE)	b (SE)	b (SE)	b (SE)	b (SE)	b (SE)
Female	0.679 (0.696)	0.254 (1.120)	0.161 (1.089)	0.458 (1.235)	0.164 (0.731)	0.999 (15,289.590)
White	0.617 (0.416)	0.236 (0.819)	0.085 (0.432)**	0.726 (0.910)	0.611 (0.473)	0.890 (1.024)
Age	0.764 (0.034)	0.337 (0.043)	0.348 (0.035)	0.062 (0.051)**	0.409 (0.039)	0.129 (0.045)
Neighborhood gang problem	0.420 (0.219)	0.420 (0.537)	0.535 (0.237)	0.102 (0.785)	0.475 (0.250)	0.248 (0.606)
Future membership plan	0.338 (0.258)	0.137 (0.838)	0.617 (0.277)	0.600 (0.954)	0.178 (0.295)	0.958 (0.917)
Reasons for joining	0.275 (0.436)	0.864 (0.880)	0.638 (0.482)	0.880 (1.152)	0.833 (0.498)	0.090 (1.155)**
Benefits from joining	0.147 (0.429)	0.808 (0.819)	0.844 (0.474)	0.080 (1.465)**	0.106 (0.500)	0.409 (1.074)
Constant	0.687 (1.025)	0.677 (2.263)	0.013 (1.360)	0.177 (2.569)	0.192 (1.137)	0.999 (15,289.590)

Note. Standard errors in parentheses.

p < .05. **p < .1.

The results of the logistic regression for perceived risk of victimization are presented in Table 3. With perceptions of individual risk of victimization, current gang members who were unsure of their future gang membership were significantly (α = 0.05) more likely to perceive a risk of victimization, particularly property victimization. Implications from this finding are that being unsure of one’s future membership (definitions) implies some remaining favorability toward gangs, and therefore an acceptance of the lifestyle and consequences. This may also show that there is a remaining belief that the gang has a protective capability.

Table 3.

Logistic Regression Predicting Likelihood of Property Victimization, Violent Victimization, or Gang-Related Victimization Among Current and Ex-Gang Members.

	Likelihood of property victimization		Likelihood of violent victimization		Likelihood of gang victimization
	Current member	Ex-member	Current member	Ex-member	Current member	Ex-member
	b (SE)	b (SE)	b (SE)	b (SE)	b (SE)	b (SE)
Female	0.101 (1.095)	0.246 (1.499)	0.377 (0.834)	0.999 (14,256.397)	.094 (0.843)**	0.795 (1.782)
White	0.949 (0.450)	0.297 (0.983)	0.439 (0.426)	0.263 (1.286)	.094 (0.422)**	.050 (1.602)*
Age	0.657 (0.037)	0.274 (0.060)	0.081 (0.035)**	0.475 (0.056)	0.678 (0.034)	0.792 (0.060)
Neighborhood gang problem	0.296 (0.241)	0.209 (0.710)	0.998 (0.228)	0.835 (0.733)	0.171 (0.224)	0.368 (0.772)
Future membership plan	0.013 (0.297)*	0.036 (1.485)*	0.332 (0.273)	0.853 (1.299)	0.227 (0.264)	.055 (1.326)**
Reasons for joining	0.177 (0.496)	0.719 (0.992)	0.588 (0.468)	0.062 (1.459)**	0.158 (0.463)	0.268 (1.171)
Benefits from joining	0.893 (0.477)	0.095 (1.033)**	0.602 (0.456)	0.578 (1.254)	0.616 (0.448)	0.763 (1.126)
Constant	0.241 (1.355)	0.346 (3.231)	0.018 (1.147)	0.999 (14,256.398)	0.070 (1.151)	0.999 (3.163)

Note. Standard errors in parentheses.

p < .05. **p < .1.

Similar to the findings from the victimization experience variable, ex-gang members were significantly (α = 0.05) more likely to perceive a risk of gang-related victimization if they did not see gangs in their neighborhood as a problem (differential reinforcement). This indicates that by not seeing gangs as a problem, they are also more likely to perceive future risk of victimization as victimization has been identified by the individual as an accepted, recurring part of life by the community.

Finally, Table 4 presents the findings from the logistic regression of the fear of property, violent, and gang-related victimization. Results of the regression show that current gang members were significantly (α = 0.1) more likely to fear property victimization if they had indicated that family and friends were the main influence behind their decision to join a gang (differential association/modeling). This finding suggests that, as previously discussed, family and friends as an influential factor can increase the participation in gang activity, thereby exposing the individual to more potential victimization. It follows that the group that perceives a greater risk of property victimization would also be significantly more likely to fear that same type of victimization.

Table 4.

Logistic Regression of Fear of Property Victimization, Violent Victimization, or Gang-Related Victimization Among Current and Ex-Gang Members.

	Fear of property victimization		Fear of violent victimization		Fear of gang victimization
	Current member	Ex-member	Current member	Ex-member	Current member	Ex-member
	b (SE)	b (SE)	b (SE)	b (SE)	b (SE)	b (SE)
Female	0.233 (1.318)	0.995 (5,286.605)	0.838 (0.733)	0.904 (1.113)	0.999 (12,418.686)	0.067 (1.353)**
White	0.074 (1.419)**	0.997 (9,423.060)	0.614 (0.432)	0.041 (0.851)*	0.642 (0.760)	0.151 (1.276)
Age	0.011 (0.070)*	0.997 (906.038)	0.747 (0.035)*	0.429 (0.041)	0.027 (0.055)**	0.785 (0.055)
Neighborhood gang problem	0.847 (0.467)	0.994 (7,691.225)	0.914 (0.226)	0.603 (0.529)	0.794 (0.422)	0.905 (0.759)
Future membership plan	0.661 (0.553)	0.998 (11,702.646)	0.773 (0.264)	0.853 (0.804)	0.089 (0.663)	0.470 (1.165)
Reasons for joining	0.078 (1.000)**	1.000 (10,646.544)	0.609 (0.456)	0.477 (0.904)	0.359 (0.928)	0.433 (1.224)
Benefits from joining	0.149 (0.957)	0.995 (5,463.082)	0.811 (0.451)	0.370 (0.834)	0.839 (0.830)	0.783 (1.055)
Constant	0.004 (2.093)	0.997 (35,526.008)	0.329 (0.951)	0.322 (2.391)	0.999 (12,418.686)	0.604 (3.359)

Note. Standard errors in parentheses.

p < .05. **p < .1.

Discussion

The goal of this article was to extend social learning theory by using its aspects to explain current and ex-gang members’ victimization. This was achieved through applying all three aspects of the theory (i.e., definitions, differential reinforcement, and differential association / modeling) to three different types of victimization (i.e., property, violent, and gang victimization). The findings contribute to the current literature in several ways.

First, the majority of the sample being young, non-White males lends generalizability to the study as this mirrors the typical demographic variables of gang members in the country. Second, the ability of this study to find significance for all three types of victimization (i.e., property, personal, and gang) for all three forms of victimization (i.e., experience, risk, and fear) lends generalizability to the study in following suggestions from Schreck (1999). Third, the results of this study found that variables measuring definitions and differential association were the most common variables in predicting the different types of victimization. This reflects findings from Pratt et al. (2010) which showed that differential association and definition variables had the strongest predictive power in their meta-analysis.

Fourth, this study found that current gang members experience gang and property victimization at statistically significant rates. This reflects previous research that tells us gang members are at increased risk for victimization (DeLisi et al., 2009; Melde et al., 2009; Taylor et al., 2007). Finally, there are two other similarly important contributions to the literature provided by this study: (a) current gang members reference family and friends as reasons for both joining a gang as well as benefits of their gang membership and (b) the majority of current gang members believed gangs were a problem in their neighborhoods. These findings reflect current knowledge that family factors can increase the risk of victimization, as well as strengthens ideals put forward by the intergenerational cycle of violence model (Jensen & Brownfield, 1986; Laner & Thompson, 1982; Marshall & Rose, 1988; Sampson & Lauritsen, 1990; Spano et al., 2008; Steinmetz, 1977).

This study was among the first to apply social learning theory to victimization. The author’s expectation is that such an application will be repeated in future studies. In further guiding future researchers who pursue this course of analysis, the authors offer suggestions that will most likely result in even greater findings than discussed in this article. First, it is recognized that the data used for this project was derived from a self-report survey. Self-report data, especially in surveying victims, has been praised by some researchers who believe it captures more than official data can, while others believe that self-report data has been underscrutinized and may be unreliable due to false reporting (Junger-Tas & Marshall, 1999; Lynch & Addington, 2010). This potential limitation was likely avoided through the use of anonymous survey methods.

Second, the measures used to test definitions, differential reinforcement, and differential association/modeling are representative of the aspects of social learning theory, but limitedly so. In addition, the data used only provided one or two possible questions that addressed the meaning of each aspect. This limited the broadness of the measures. The author suggests that future researchers create a survey directed at specifically measuring the aspects of social learning theory so as to provide more variables to be tested. Furthermore, creating a survey directed intentionally at testing whether social learning theory can explain victimization experiences, as well as the perceived risk and fear of victimization, will strengthen the validity of the measures. Moreover, the author believes that the validity of the overall application of social learning theory to explaining victimization may be increased through the inclusion of more control variables such as offending and additional background factors.

A third limitation of the current study was the small sample size. This limitation can be addressed in correcting a fourth limitation: lack of a juvenile sample. Recall this study was conducted in 14 jails across the state of Florida thereby removing the possibility of surveying juveniles. If future researchers survey a different population that could include juveniles, this could potentially increase the sample size. Moreover, including juveniles in the sample will capture an understudied population which is at increased risk for recruitment and involvement in gang activity (Decker & Curry, 2000; Pyrooz & Sweeten, 2015).

The author’s future directions include following the above recommendations so as to promote the application of criminological theory to victimization. This includes not only social learning theory—though there is certainly ample space to do so—but other criminological theories such as life course theory and control balance theory (as suggested in Fox, 2013).

Footnotes

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Analisa Gagnon

References

Akers

R. L.

(1973). Deviant behavior: A social learning approach. Belmont, CA: Wadsworth.

Akers

R. L.

Jensen

G. F.

(2006). The empirical status of social learning theory of crime and deviance: The past, present, and future. In Taking stock: The status of criminological theory, (Vol. 15, pp. 37-76). New Brunswick, NJ: Transaction Publishers.

Akers

R. L.

Krohn

M. D.

Lanza-Kaduce

Radosevich

(1979). Social learning and deviant behavior: A specific test of a general theory. American Sociological Review, 44, 636-655.

Bandura

(1977). Social learning theory. New York, NY: General Learning Press.

Barnes

J. C.

Boutwell

B. B.

Fox

K. A.

(2012). A biosocial examination of the gang-victimization relationship. Youth Violence and Juvenile Justice, 10, 227-244.

Childs

K. K.

Cochran

J. K.

Gibson

C. L.

(2009). Self-control, gang membership, and victimization: An integrated approach. Journal of Crime and Justice, 32, 35-60.

Cohen

L. E.

Felson

(1979). Social change and crime rate trends: A routine activity approach. American Sociological Review, 44, 588-608.

Decker

S. H.

(1996). Collective and normative features of gang violence. Justice Quarterly, 13, 243-264.

Decker

S. H.

Curry

G. D.

(2000). Addressing key features of gang membership: Measuring the involvement of young members. Journal of Criminal Justice, 28, 473-482.

10.

Decker

S. H.

Katz

C. M.

Webb

V. J.

(2008). Understanding the black box of gang organization: Implications for involvement in violent crime, drug sales, and violent victimization. Crime & Delinquency, 54, 153-172.

11.

Decker

S. H.

van Winkle

(1996). Life in the gang: Family, friends, and violence. New York, NY: Cambridge University Press.

12.

DeLisi

Barnes

J. C.

Beaver

K. M.

Gibson

C. L.

(2009). Delinquent gangs and adolescent victimization revisited: A propensity score matching approach. Criminal Justice and Behavior, 36, 808-823.

13.

Esbensen

F. A.

Winfree

L. T.

(1998). Race and gender differences between gang and nongang youths: Results from a multisite survey. Justice Quarterly, 15, 505-526.

14.

Foshee

V. A.

Bauman

K. E.

Linder

G. F.

(1999). Family violence and the perpetration of adolescent dating violence: Examining social learning and social control processes. Journal of Marriage and the Family, 61, 331-342.

15.

Fox

K. A.

(2013). New developments and implications for understanding the victimization of gang members. Violence & Victims, 28, 1015-1040.

16.

Fox

K. A.

Lane

Akers

R. L.

(2010). Do perceptions of neighborhood disorganization predict crime or victimization? An examination of gang member versus non-gang member jail inmates. Journal of Criminal Justice, 38, 720-729.

17.

Fox

K. A.

Nobles

M. R.

Akers

R. L.

(2011). Is stalking a learned phenomenon? An empirical test of social learning theory. Journal of Criminal Justice, 39, 39-47.

18.

Fox

K. A.

Rufino

K. A.

Kercher

G. A.

(2012). Crime victimization among gang and nongang prison inmates: Examining perceptions of social disorganization. Victims & Offenders, 7, 208-225.

19.

Gottfredson

M. R.

(1981). On the etiology of criminal victimization. Journal of Criminal Law and Criminology, 72, 714-726.

20.

Gottfredson

M. R.

Hirschi

(1990). A general theory of crime. Palo Alto, CA: Stanford University Press.

21.

Hindelang

M. J.

Gottfredson

M. R.

Garofalo

(1978). Victims of personal crime: An empirical foundation for a theory of personal victimization. Cambridge, MA: Ballinger.

22.

Howell

J. C.

Gleason

D. K.

(1999). Youth gang drug trafficking. Washington, DC: Office of Juvenile Justice and Delinquency Prevention, Office of Justice Programs, U.S. Department of Justice.

23.

Huff

C. R.

(1999). Comparing the criminal behavior of youth gangs and at-risk youths. Alternatives to Incarceration, 5(2), S7-S9.

24.

Jacobs

B. A.

(2000). Robbing drug dealers: Violence beyond the law. Hawthrone, NY: Transaction Publishers.

25.

Jennings

W. G.

Piquero

A. R.

Reingle

J. M.

(2012). On the overlap between victimization and offending: A review of the literature. Aggression and Violent Behavior, 17, 16-26.

26.

Jensen

G. F.

Brownfield

(1986). Gender, lifestyles, and victimization: Beyond routine activity. Violence & Victims, 1, 85-99.

27.

Junger-Tas

Marshall

I. H.

(1999). The self-report methodology in crime research. Crime and Justice, 25, 291-367.

28.

Kalmuss

(1984). The intergenerational transmission of marital aggression. Journal of Marriage and the Family, 46, 11-19.

29.

Lane

Fox

K. A.

(2012). Fear of crime among gang and non-gang offenders: Comparing the effects of perpetration, victimization, and neighborhood factors. Justice Quarterly, 29, 491-523.

30.

Lane

Fox

K. A.

(2013). Fear of property, violent, and gang crime: Examining the shadow of sexual assault thesis among male and female offenders. Criminal Justice and Behavior, 40, 472-496.

31.

Lane

Meeker

J. W.

(2003). Women’s and men’s fear of gang crimes: Sexual and nonsexual assault as perceptually contemporaneous offenses. Justice Quarterly, 20, 337-371.

32.

Laner

M. R.

Thompson

(1982). Abuse and aggression in courting couples. Deviant Behavior, 3, 229-244.

33.

Lauritsen

J. L.

Sampson

R. J.

Laub

J. H.

(1991). The link between offending and victimization among adolescents. Criminology, 29, 265-292.

34.

Lynch

J. P.

Addington

L. A.

(2010). Identifying and addressing response errors in self-report surveys. In Piquero

Weisburd

(Eds.), Handbook of quantitative criminology (pp. 251-272). New York, NY: Springer.

35.

Manz

C. C.

Sims

H. P.

(1980). Self-management as a substitute for leadership: A social learning theory perspective. Academy of Management review, 5, 361-367.

36.

Marshall

L. L.

Rose

(1988). Family of origin violence and courtship abuse. Journal of Counseling & Development, 66, 414-418.

37.

Melde

Taylor

T. J.

Esbensen

F. A.

(2009). “I got your back”: An examination of the protective function of gang membership in adolescence. Criminology, 47, 565-594.

38.

Mihalic

S. W.

Elliott

(1997). A social learning theory model of marital violence. Journal of Family Violence, 12, 21-47.

39.

Padilla

(1993). The working gang. In Cummings

Scott

Monti

Daniel J.

(Eds. ), Gangs: The origins and impact of contemporary youth gangs in the United States (pp. 173-192). Albany, NY: State University of New York Press.

40.

Peterson

Taylor

T. J.

Esbensen

F. A.

(2004). Gang membership and violent victimization. Justice Quarterly, 21, 793-815.

41.

Pratt

T. C.

Cullen

F. T.

Sellers

C. S.

Winfree

L. T.

Madensen

T. D.

Jr. Daigle

L. E.

. . . Gau

J. M.

(2010). The empirical status of social learning theory: A meta-analysis. Justice Quarterly, 27, 765-802.

42.

Pyrooz

D. C.

Sweeten

(2015). Gang membership between ages 5 and 17 years in the United States. Journal of Adolescent Health, 56, 414-419.

43.

Sampson

R. J.

Lauritsen

J. L.

(1990). Deviant lifestyles, proximity to crime, and the offender-victim link in personal violence. Journal of Research in Crime and Delinquency, 27, 110-139.

44.

Sanders

W. B.

(1994). Gangbangs and drive-bys: Grounded culture and juvenile gang violence. Hawthorne, NY: Transaction Publishers.

45.

Schreck

C. J.

(1999). Criminal victimization and low self-control: An extension and test of a general theory of crime. Justice Quarterly, 16, 633-654.

46.

Spano

Freilich

J. D.

Bolland

(2008). Gang membership, gun carrying, and employment: Applying routine activities theory to explain violent victimization among inner city, minority youth living in extreme poverty. Justice Quarterly, 25, 381-410.

47.

Steinmetz

S. K.

(1977). The cycle of violence: Assertive, aggressive, and abusive family interaction. Santa Barbara, CA: Praeger.

48.

Stewart

E. A.

Elifson

K. W.

Sterk

C. E.

(2004). Integrating the general theory of crime into an explanation of violent victimization among female offenders. Justice Quarterly, 21, 159-181.

49.

Straus

M. M. A.

Gelles

R. J.

Steinmetz

S. K.

(Eds.). (1982). Behind closed doors: Violence in the American family. New Brunswick, NJ: Transaction Publishers.

50.

Taylor

T. J.

Freng

Esbensen

F. A.

Peterson

(2008). Youth gang membership and serious violent victimization: The importance of lifestyles and routine activities. Journal of Interpersonal Violence, 23, 1441-1464.

51.

Taylor

T. J.

Peterson

Esbensen

F. A.

Freng

(2007). Gang membership as a risk factor for adolescent violent victimization. Journal of Research in Crime and Delinquency, 44, 351-380.

52.

C. H.

(2000). On-line learning migration: from social learning theory to social presence theory in a CMC environment. Journal of network and computer applications, 23, 27-37.

53.

Webler

Kastenholz

Renn

(1995). Public participation in impact assessment: a social learning perspective. Environmental impact assessment review, 15, 443-463.