The Intersection of Victimization and Delinquency in Adolescents: Comparative Evidence from Mexico and Spain

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Grijalva-Eternod, Esther Fernández-Molina, Raquel Bartolomé-Gutiérrez This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8002022/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Apr, 2026 Read the published version in International Criminology → Version 1 posted You are reading this latest preprint version Abstract This study examines the configuration and contextual correlates of the victim-offender overlap in adolescents. Using data from the International Self-Report Delinquency Study (ISRD-4), we compare this phenomenon across two countries with markedly contrasting socio-legal and security environments: Mexico and Spain, in two periods of reference: lifetime and last year. We apply Latent Class Analysis (LCA) to identify adolescent risk profiles, consistently finding two distinct classes across both nations and periods: a low-risk group and a victim-offender class. Subsequent regression analyses reveal that emotional well-being is a consistent universal predictor of membership to this profile, along with country-specific variables that exhibit differential effects. These findings highlight the need for culturally and contextually sensitive prevention programs to address the complex nature of this overlap in high-violence versus low-violence settings. Victim-Offender overlap Latent Class Analysis Adolescence Victimization Delinquency Comparative analysis Figures Figure 1 Introduction Adolescent involvement in crime remains a central concern in criminology and a cornerstone of crime prevention, as young people account for a substantial share of global offending. Adolescence represents both a developmental period marked by peak levels of criminal behavior—consistently reflected in the age–crime curve across diverse contexts (Benson, 2013 ; Le Blanc, 2020 ; White, 2014 )—and a critical phase of legal socialization, during which individuals internalize norms, recognize authority, and form enduring attitudes toward law and institutions (Oliveira & Jackson, 2021; Reisig et al., 2011; Tyler & Trinkner, 2017). Young people, however, are not only involved in delinquency as offenders, but also, and to a great extent, are subjected to situations of victimization, which may include child maltreatment, conventional crime, community violence, cyberviolence, exposure to parental violence, and peer or sibling violence. While, in the past, researchers often examined single types of violence against youth, this focus has shifted in recent decades. Violence against youth is often complex and does not occur in isolated ways. Hence, the present study seeks to explore the circumstances and contexts in which adolescents are victims of crime (Radtke et al, 2024 ). Delving into the study of adolescents’ experiences of crime entails significant methodological complexities. Researchers face difficulties in obtaining representative samples, navigating increasingly stringent ethical requirements, securing access through schools and parents, and maintaining participants’ engagement throughout surveys (Fernández-Molina & Bartolomé Gutiérrez, 2023 ). Consequently, despite extensive research on youth offending and victimization, the availability of data suitable for comparative analysis remains limited. The International Self-Report Delinquency Study (ISRD) represents a major international collaboration designed to collect data on adolescents’ experiences with crime, traditionally through school-based surveys. However, access to schools has become increasingly difficult, prompting researchers to explore alternative data collection strategies (Enzmann et al., 2018 ; Marshall et al., 2022 ). In response, the fourth wave of the study (ISRD4) supplemented the conventional school survey with a shortened online version administered to adolescents recruited via digital panels. This innovation aimed to diversify samples and assess the feasibility of online data collection. Among the participating countries, Spain and Mexico adopted parallel recruitment procedures by contracting the same survey firm, Metroscopia, which applied comparable sampling strategies across both contexts. This alignment provides a valuable basis for cross-national comparison. The present study seeks to advance understanding of adolescents’ involvement in delinquency in Spain and Mexico, particularly the connection between victimization and offending during adolescence. Despite there being an extensive body of literature on the so-called victim-offender overlap, much of the research on adolescents’ experiences with crime continues to analyze victimization and offending as independent phenomena, often using additive scales or mean scores of affirmative responses to a list of experiences. Here, we aim to identify patterns of co-occurrence between victimization and offending using latent class analysis (LCA) based on multiple indicators of delinquent experiences. The contrast between the national contexts makes the present study particularly relevant. Mexico faces severe challenges regarding security and the rule of law, as reflected in its 2024 Rule of Law Index score of 0.41—well below Spain’s 0.71 (World Justice Project, 2024 ). This disparity is even greater in specific dimensions such as crime control, where Mexico records the lowest value among 142 countries (0.38), compared with Spain’s 0.86. Likewise, Mexico’s homicide rate of 24.86 per 100,000 inhabitants far exceeds Spain’s 0.69 (United Nations Office on Drugs and Crime [UNODC], 2024 ). Mexican youth are exposed to particularly high contextual risks. Official statistics indicate that 378,816 individuals are currently reported missing, 35.4% of whom are between 10 and 19 years old (Comisión Nacional de Búsqueda [CNB], 2025 ). Paradoxically, despite the severe security situation, a greater proportion of Mexicans report feeling happy compared to Spaniards (92.1% vs. 88.8%) (Haerpfer et al., 2022 ). Data from the OECD (2025), however, offer a more nuanced picture: Mexico ranks low on the Better Life Index, particularly in the safety dimension, while Spain scores highest on this indicator. These marked contextual contrasts provide a valuable backdrop for examining whether the victim–offender overlap is shaped by broader cultural and structural conditions. Adolescents’ contact with delinquency Criminological research determined decades ago that victims and offenders are often the same people. This phenomenon, known as the victim-offender overlap, has been documented across different countries, cultures and types of crimes, and has been acknowledged as a criminological fact (Jennings et al., 2012). The correlation between victimization and offending is robust compared with other effects reported in criminological studies (r values of between 0.24 and 0.50) (Beckley et al, 2018). The victim-offender overlap was initially documented in studies on victims, who were found to be more likely to have criminal records (Berg & Schreck, 2022; Jennings et al., 2012). Subsequently, self-report studies that included questions on experiences of both victimization and offending have shown that a part of the sample reported having been victims of one or more offenses while also having committed one or several offenses (Birbeck et al., 2023). Given the observational and cross-sectional nature of most of these studies, it has not been possible to identify causal mechanisms, and the phenomenon thus remains difficult to understand and explain (Berg & Mulford, 2017). Despite these limitations, it has been found that the overlap manifests across diverse contexts—regardless of age, gender, race, or country (Berg & Schreck, 2022). Moreover, during adolescence, the degree of overlap is higher (Beckley et al., 2018), particularly among high-level offenders (Erdmann & Reinecke, 2019), while in disadvantaged environments, the likelihood of simultaneously being both a victim and an offender is multiplied (Delong & Reichert, 2019). Likewise, the overlap appears to be stronger in violent offenses (e.g., fights, assaults, homicides) than in property crimes (e.g., thefts, burglaries). Furthermore, longitudinal research and trajectory analyses have identified a victim-offender profile that is distinct from that of individuals who are only offenders or only victims. It has been evidenced that victim-offenders show differences from the other groups, reporting lower levels of psychological well-being, social interaction, and engagement in conventional activities (Birkbeck et al., 2023). Nevertheless, the three groups share certain individual and social risk factors, including low self-control, alcohol and drug use, association with delinquent peers, neighborhood disorganization, and low parental monitoring (Jennings et al., 2012). Despite the overwhelming evidence, as noted by Birkbeck et al. (2023), there has been no debate on the definition and measurement of this overlap that might help clarify its scope and so improve the understanding of delinquency. In particular, the authors underline two factors worth noting. On the one hand, the available evidence is derived both from probabilistic research, which has sought to explain the possible causal relationships between victimization and offending, and from categorical research, which has explored the victim-offender group as either a cause or a consequence. On the other hand, little thought has been given to the time periods used to document victimization and offending experiences in self-report studies, although these might account for very different processes. Hence, the authors conclude that the overlap remains an elastic concept, requiring further research and deeper reflection on the varying scope of the available evidence. Theoretical frameworks to explain the overlap Although, as explained, the victim-offender overlap is a phenomenon that has been more widely observed than understood, criminological research has sought to explain it through the explanatory frameworks commonly used in the discipline. Thus, two theoretical frameworks have been particularly influential in attempts to explain the overlap: Gottfredson and Hirschi’s self-control theory, and situational theories, including Felson’s routine activity theory. Both perspectives suggest that the same processes explain both victimization and offending, challenging the classical notion that victims and offenders are separate and opposing populations (Berg & Schreck, 2022). Following self-control theory, victimization and offending are not independent events but manifestations of a shared underlying factor, that is low self-control and a limited ability to foresee consequences. Individuals with low self-control are more inclined to engage in impulsive, risky, and often antisocial behaviors, simultaneously leading them to be more prone to offending and exposure to situations in which they may be victimized. From a more contemporary perspective, low self-control can be understood as part of a heterogeneous risk, whereby victimization, injury, accidents, and antisocial behavior tend to concentrate within the same persons (Berg & Mulford, 2017). Situational theories, meanwhile, posit the many crimes, particularly violent ones, are the result of interactions in which both victims and offenders actively participate, without their having fixed roles. Thus, an individual may start as a victim but respond as an aggressor, or vice versa. Under this perspective, everyday behaviors and social dynamics determine the opportunities to both commit and experience offenses. This approach shows that risky lifestyles, in contexts of unstructured peer socialization, increase the opportunities for offending and victimization, or even for both at the same time. As suggested by Berg and Mulford (2017), empirical evidence from qualitative and longitudinal studies supports this view, which neither assumes that victims are passive or that offenders act unilaterally However, these two opposing theoretical paradigms are unable to explain why more young people from disorganized communities have greater contact with crime in either of its two roles (Berg & Mulford, 2017). To this end, community subcultural explanations have made their own particular contribution to understanding overlap, whereby subculture theories emphasize the role of culture in generating criminal opportunity. The code of the street that exists in certain environments in which young people socialize, where aggression and the use of force to maintain reputation are admired, exposes them to situations of both victimization and offending in order to maintain their status in the group (Jennings et al 2012). Recently, perspectives that are more psychological than criminological have proposed life course’s explanations. Developmental research indicates that the overlap does not randomly arise in adolescence, but rather responds to the sum of multiple childhood adversities that foster the accumulation of risks (Beckley et al., 2018, Malvaso et al, 2022). This could be the result of learning and reinforcement processes triggered by evocative traits, as would be the case with low self-control. It could also be due to experiences of adversity, particularly in childhood and especially polyvictimization, altering psychological, social, and neurological development, shaping trajectories of both victimization and criminalization (McLachlan, 2025, Malvaso et al., 2022). Notwithdstanding the above, and as Berg and Schreck (2022) acknowledge, the evidence on the victim-offender overlap demands a redefinition of criminological theory and policy, replacing the question of “How do we reduce offending?” to an equally challenging one: “How do we reduce vulnerability and exposure to risks generated by both victimization and offending?” Some authors advocate the development of an integrative theory (Jennings et al., 2012), while others suggest that it is arguably time to set aside criminological theories and develop an alternative approach that can explain the differences between victim-offenders and all the other roles (offenders only, victims only, and those not involved in crime) (Birkbeck et al., 2023). Meanwhile, authors overall find it is necessary to seek more innovative designs that allow for a better understanding of the dynamics of victimization and offending processes (among others, Berg & Mulford, 2017, Jennings et al., 2012). Moreover, most of the scientific literature in this regard stems from the Global North, and it is thus crucial to obtain further evidence from other regions of the world so as to be able to generalize on the nature, causes, and consequences of the overlap (Birkbeck et al., 2023). The present study seeks to contribute to the existing knowledge by means of a comparative study. Specifically, it aims to identify subgroups of adolescents based on their experiences as victims and/or offenders, and to determine whether these patterns differ between a country with high levels of violence (Mexico) and one with low levels (Spain). Method Data and Participants The data in this study come from the fourth wave of the International Self-Report Delinquency Study (ISRD4), using a shortened version of the instrument. The data were collected by the Metroscopia firm in Mexico (from January 25 to February 4, 2023) and in Spain (from June 2 to June 21, 2022). Using a non-probabilistic quota sampling procedure, the participants were recruited from among young people registered in Metroscopia ’s online panels. Quotas were established by gender (female, male) and age (16, 17, 18, and 19 years), with the goal of recruiting a minimum of 100 participants for each of the eight resulting combinations. Data collection concluded once all the quotas were filled, resulting in a final sample that exceeded the initial target of 800 participants per country. To reduce respondent burden, a split-ballot questionnaire design was employed. The participants in each country were randomly assigned to one of two versions of the instrument, meaning that some of the variables used in this study were not available for the entire sample. The survey was administered online and was completed in a mean time of 20 minutes. The participants were informed of the objectives of the study and gave their explicit written consent before completing the questionnaire. Confidentiality and anonymity were guaranteed throughout the process; no personal information that could allow respondents to be identified was collected. In both countries, the study was approved by an ethics committee: in Mexico, by the Research Ethics Committee of the University Center for Social Sciences and Humanities at the University of Guadalajara, and in Spain, by the Social Research Ethics Committee of the University of Castilla-La Mancha. After data cleaning, 1 the final sample comprised 1797 adolescents (898 Spanish and 899 Mexican participants), following the exclusion of individuals that provided implausible responses. The mean age was 7.51 (SD = 1.11), while 48.6% of the participants were female (n = 873) and 45.5% were male; 2% identified as non-binary (n = 35). Measures The dependent variables were of two types. The first was a set of observable indicators used to construct the latent profiles, while the second was membership in these profiles, which was used as a categorical variable in the regression analyses. For the LCA, we used 24 dichotomous items (0 = No, 1 = Yes), grouped into four categories: lifetime victimization, last-year victimization, lifetime offending and last-year offending. 2 All of these variables were analyzed separately for Mexico and Spain. For both lifetime victimization and last-year victimization, respondents were asked whether they had been victims of robbery with violence , assault , non-consensual online sharing of intimate content (NCSIC) , or cyber hate . In the case of lifetime offending and last-year offending, participants were asked whether they had engaged in any of the following behaviors: graffiti , shoplifting , burglary , weapon carrying , group fight , assault , NCSIC , and hacking . Based on the LCA, four dichotomous dependent variables were generated, representing participants’ membership in one of the two profiles identified, namely, a low-risk profile with limited experience of crime in either of the two roles (coded as 0) and a victim-offender profile (coded as 1). This variable was estimated for each of the four analyses: Mexico-lifetime, Mexico-last year, Spain-lifetime, and Spain-last year. Furthermore, three groups of dependent variables were included in the analysis, as follows: To assess the influence of situational factors on the overlap, we used a proxy measure of unstructured activities in public spaces . This was measured through a 5-point Likert-type item asking participants how frequently, in a typical week, they spent time in the street, in shopping malls, or in their neighborhood (1 = Never to 5 = Every day). Beyond the situational perspective, we incorporated a set of variables referring to psychosocial indicators, which have also been associated with the overlap. Specifically, the online version of the ISRD 4 includes additive Likert-type scales that measure, on the one hand, self-control (5 items, 5 points; e.g. I act on the spur of the moment without stopping to think, Sometimes I will take a risk just for the fun of it) and morality (6 items, 4 response points; e.g. Purposely damage or destroy someone else’s property, Hack or break into a private account or computer to acquire data, get control of an account, or destroy data). We also used the scales that measure violence perception sensitivity (6 items, 4 response points; e.g. Sharing online an intimate photo or video of someone that he or she did not want others to see, Hitting another person without causing injury) and revenge (4 items, 4 response points; e.g. It is important for me to get back at people who have hurt me, There is nothing wrong in getting back at someone who has hurt you). All the scales presented an adequate internal consistency, measured using Cronbach’s alpha: self-control (α = 0.79), morality (α = 0.83), violence perception sensitivity (α = 0.90) and revenge (α = 0.78). As control variables, we included participants’ age (continuous variables), gender (dichotomous variables, 0 = female and 1 = male) 3 and a proxy to capture the adolescents’ level of well-being, assessed on the a 6-point Likert-type scale on their level of happiness over the past six months (1 = very unhappy to 6 = very happy). Analytical Strategy The analysis was conducted in three phases. First, we compared the characteristics of the Mexican and Spanish samples by examining the differences in the prevalences of the 24 indicators of victimization and delinquent behavior, as well as the mean values or proportions of the independent variables. To this end, chi-square tests were used for the categorical variables and independent-samples t-tests for the continuous variables. Second, we performed an LCA to identify the profiles of the adolescents based on their victimization and offending experiences. Models with two to four classes were estimated for each of the four subgroups (Spain LTP, Mexico LTP, Spain LYP, and Mexico LYP), for the purpose of determining the optimal number of classes. Subsequently, the participants’ profiles were created, according to their membership in one of the latent classes identified in the process. The selection of the final model was primarily based on the Bayesian information criterion (BIC), prioritizing the lowest value obtained. This was chosen as it is the most reliable fit statistic for model selection, as it indicates the model with the greatest parsimony (Weller et al., 2020). Finally, to identify the variables associated with membership in the victim-offender profile, we estimated a series of binomial logistic regressions. Due to the split questionnaire design, the analysis was structured in two independent blocks for each of the four subsamples (Spain LTP, Mexico LTP, Spain LYP, and Mexico LYP). We used the same baseline model, which included the control variables and unstructured activities. Based on this model, in Block A, we evaluated the contribution of Model A, which included the self-control and morality scales. In Block B, similarly, the contribution of the violence perception sensitivity and revenge scales to the base model was assessed. In both blocks, the incremental contribution of the added scales was evaluated using the likelihood ratio test. Our data analysis was conducted using R software (version 4.3.1). The LCA was performed using the poLCA package (Linzer & Lewis, 2011 ); the regression models were estimated with the stats (R Core Team, 2023 ), lmtest (Zeileis & Hothorn, 2002 ) and fmsb (Nakasawa, 2024) packages; and data filtering was conducted through the haven (Wickham & Miller, 2023) and dplyr (Wickham et al., 2024) packages. Results Descriptive statistics and comparison between countries Table 1 shows the descriptive statistics for the total sample and by country. As can be seen, various significant differences were found between respondents in Mexico and Spain. The mean age of the Spanish adolescents was slightly higher (t = 2.8**) than that of their Mexican counterparts. They also scored higher on engagement in unstructured activities (t = 13***) and revenge (t = 3.4***), while also reporting lower self-control (t=-3.8***). Substantial differences were also observed in experience of crime. The Spanish adolescents reported higher lifetime involvement in offending, particularly in the following categories: graffiti (χ²=86***), shoplifting(χ²=135***), burglary (χ²=8.5**), group fight (χ²=4.5*), assault (χ²=5.3*), and hacking (χ²=11***). In contrast, the only lifetime offense with a higher prevalence among Mexican adolescents was weapon carrying (χ²=9.3**). No statistically significant differences were found between the two samples in any of the lifetime victimization variables. As regards experience in the last year, the results were similar. The Spanish adolescents reported higher involvement in offending, although the differences were only significant in the cases of graffiti (χ²=24***), shoplifting (χ²=49***), and burglary (χ²=3.9*), while the Mexican participants again reported carrying weapons with greater frequency (χ²=6.2*). Victimization experience in the last year was similar in both countries, except in the case of cyber hate (χ²=8.3**), which was more prevalent in Mexico Finally, it is worth highlighting several interesting findings from this descriptive analysis. First, the most frequently reported form of victimization in both countries and across both periods (lifetime and last year) was cyber hate. Second, the most commonly reported lifetime and last-year offense in Spain was shoplifting (LTP = 35% and LYP = 11%), whereas in Mexico it was weapon carrying (LTP = 13% and LYP = 7.3%). Third, a consistent pattern was observed across all measures of experience with crime, in both countries and as both victim and as offender: the lifetime prevalence reported was substantially higher than that reported for the last year. Table 1 Descriptive statistics for the total sample and by country Overall N = 1797 1 Spain N = 898 1 Mexico N = 899 1 Comparison 2 Socioeconomic variables Age 17.51 (1.11) 17.58 (1.09) 17.44 (1.12) 2.8** Gender 2.4 Male 889 (49.5) 445 (49.6) 444 (49.4) Female 873 (48.6) 440 (49.0) 433 (48.2) Non-binary 35 (2.0) 13 (1.5) 22 (2.5) Lifestyle Emotional well-being 4.42 (1.14) 4.37 (1.17) 4.47 (1.11) -1.9 Situational factors Unstructured activities 2.82 (1.11) 3.14 (1.10) 2.49 (1.02) 13*** Psychosocial scales Self-control 15.04 (4.54) 14.48 (4.36) 15.68 (4.66) -3.8*** Morality 22.55 (2.46) 22.46 (2.59) 22.65 (2.30) -1.1 Violence perception sensitivity 15.36 (5.70) 15.27 (5.43) 15.44 (5.94) -0.44 Revenge 10.46 (3.90) 10.93 (3.95) 10.03 (3.82) 3.4*** Victimization LTP Robbery with violence 242 (14) 127 (14) 115 (13) 0.55 Assault 135 (7.6) 73 (8.2) 62 (7.1) 0.66 NCSIC 129 (7.3) 65 (7.3) 64 (7.4) 0.00 Cyber hate 300 (17) 140 (16) 160 (18) 1.6 Offending LTP Graffiti 189 (11) 156 (18) 33 (3.9) 86*** Shoplifting 394 (23) 301 (35) 93 (11) 135*** Burglary 29 (1.7) 23 (2.7) 6 (0.7) 8.5** Weapon carrying 173 (10) 68 (8.0) 105 (13) 9.3** Group fight 199 (12) 116 (14) 83 (10) 4.5* Assault 35 (2.1) 25 (2.9) 10 (1.2) 5.3* NCSIC 47 (2.8) 21 (2.5) 26 (3.2) 0.54 Hacking 92 (5.5) 63 (7.4) 29 (3.6) 11*** Victimization LTP Robbery with violence 124 (7.0) 68 (7.6) 56 (6.3) 1.0 Assault 64 (3.6) 36 (4.0) 28 (3.2) 0.68 NCSIC 56 (3.2) 27 (3.1) 29 (3.3) 0.04 Cyber hate 203 (12) 82 (9.4) 121 (14) 8.3** Offending LYP Graffiti 67 (3.9) 54 (6.3) 13 (1.5) 24*** Shoplifting 114 (6.7) 94 (11) 20 (2.4) 49*** Burglary 12 (0.7) 10 (1.2) 2 (0.2) 3.9* Weapon carrying 98 (5.8) 37 (4.4) 61 (7.3) 6.2* Group fight 109 (6.5) 64 (7.6) 45 (5.5) 2.57 Assault 20 (1.2) 12 (1.4) 8 (1.0) 0.38 NCSIC 24 (1.4) 12 (1.4) 12 (1.5) 0.00 Hacking 40 (2.4) 26 (3.1) 14 (1.7) 2.6 1 Mean score (SD) for continuous variables and n (%) for categorical variables. 2 T-test for continuous variables, χ²-test for continuous variables; *p < 0.05; **p < 0.01; ***p < 0.001. Identification of latent profiles Latent class analysis (LCA) is an approach that focuses on the subjects of a study, in our case, the survey respondents, being designed to identify unobserved subgroups (latent classes) within a larger, heterogenous population. The technique consists of grouping participants into internally homogenous groups, based on their response patterns across a series of categorical variables (Collins & Lanza, 2010; Eshima, 2022). Hence, in contrast to methods that center on variables and the differences between them, LCA organizes individuals according to their common characteristics. Given that the overlap between victimization and offending is grounded in the idea that being a victim and being an offender are interconnected phenomena, LCA is an appropriate tool for identifying the patterns underlying this connection within a sample. It is intended to reveal individual profiles according to their combined experiences of victimization and delinquent behaviors. With the aim of exploring whether the overlap between victimization and offending is shaped differently across the two countries, the LCA models were independently estimated for Mexico and Spain, considering two periods of analysis: lifetime (LT) and the last year (LY). Table 2 presents the fit statistics for all the models estimated. In all four cases, the lowest BIC value was found in the two-class latent solution, indicating that it was the most parsimonious model and that with the best fit (BIC Spain LT = 5,667.08; BIC Mexico LT = 4,381.12; BIC Spain LY = 3,268.88; BIC Mexico LY = 2,658.17). The entropy value, which measures the clarity of classification, was consistently higher in the analyses of the last-year data (LYP) compared to those referring to lifetime prevalence (LTP). Following the criterion proposed by Weller et al. (2020), the entropy values obtained are considered acceptable, being moderate for LTP (Mexico = 0.63; Spain = 0.70) and high for LYP (Mexico = 0.79; Spain = 0.82). In light of these results, the two-class models were selected for all the subsequent analyses. Table 2. Model Fit Statistics for Latent Class Analyses Figure 1 shows the profiles identified in each country and for each timeframe. As can be seen, two distinct profiles were identified across both countries and time periods. The first, labeled low-risk (depicted in blue), is characterized by minimal experience with crime, either as victim or offender, suggesting low probabilities of affirmative responses across all items. The second profile, labeled victim-offender (depicted in red), is characterized by higher probabilities of contact with crime in either or the two roles. This second profile, which, as reported in Table 3 , was a minority in all cases (Spain LTP = 28.1%, Mexico LTP = 28.4%, Spain LYP = 14.9%, Mexico LYP = 13.9%), exhibited notable differences between Spain and Mexico. The characteristics defining the victim-offender profile, detailed in Table 3 , reveal a key distinction between the two countries. Although in both countries the three variables combine experiences of victimization and offending, in Spain, both over the lifetime and during the last year, the most salient variables were primarily offenses considered as minor, specifically shoplifting, graffiti (lifetime), and group fight (last year). In contrast, the victim-offender profile in Mexico was defined, in both periods, first by victimization (cyber hate and robbery) and subsequently by a high-risk offense, namely, weapon carrying. A noteworthy finding common to all the four cases examined pertains to the role of cyber hate victimization. As mentioned in the previous section, not only was this the most prevalent form of victimization in both countries, but it was also consistently among the three key variables for defining the victim-offender profile in all the overlap subgroups analyzed. Table 3 Summary of Victim-Offender Profiles Country Timeframe Prevalence Top 3 Characteristics Spain LTP 28.1% Shoplifting (O) (66%) | Graffiti (O) (44%) | Cyber Hate (V) (38%) Mexico LTP 28.4% Cyber Hate (V) (47%) | Robbery (V) (34%) | Weapon Carrying (O) (33%) Spain LYP 14.9% Shoplifting (O) (43%) | Group Fight (O) (42%) | Cyber Hate (V) (36%) Mexico LYP 13.9% Cyber Hate (V) (51%) | Robbery (V) (37%) | Weapon Carrying (O) (34%) Predictors of Membership in the Victim-Offender Profile To identify the variables predicting membership in the victim-offender profile, a series of hierarchical binomial logistic regressions were conducted. Given the split-ballot design of the questionnaire, we were unable to jointly assess the influence of all the independent variables. Consequently, the analysis was performed using two independent blocks of models for each country and timeframe. Block A compared a baseline model, which included sex, age, and emotional well-being as control variables, and, as an explanatory variable, unstructured activities, with Model A, which further incorporated the scales of self-control and morality. Block B started from an identical baseline model, estimated on the corresponding subsample, and compared it with Model B, which included the violence perception sensitivity and revenge scales. The results of the estimation for the lifetime period are presented in Table 4, while those for the last year are detailed in Table 5 . The results of the logistic regressions for both the periods analyzed reveal the predictors of the victim-offender profile differ between Spain and Mexico. Below, we describe some of the most salient findings for the lifetime period, as shown in Table 4. In Spain, being male was found to be a key predictor. It showed a significant positive association in the Block A baseline model (OR = 2.20**) and in the Block B comparison (OR = 2.02** and OR = 2.01*). It is worth noting that the significance of gender disappeared once the variables of Model A were introduced (OR = 1.49), with only low self-control being statistically significant (OR = 0.83***). This suggests that the effect of gender may be mediated by differences in self-control between males and females. Additionally, in Block B, revenge (OR = 1.09**) was also significantly associated with membership in the victim-offender profile. In Mexico, age (OR = 1.34*) and engagement in unstructured activities (OR = 1.38* and OR = 1.39*) had a significant effect in the Block A comparison. In contrast to the Spanish case, neither gender nor self-control was key in this profile. An interesting finding in the Mexican sample was that age did not remain significant in the baseline model of Block B, despite being estimated on a very similar subgroup of participants. Additionally, the effect of unstructured activities lost its significance in Model B (OR = 1.23) once violence perception sensitivity (OR = 1.05*) was included, which was the only variable that exhibited statistical significance. This suggests that, within this adolescent profile, the effect of unstructured activities may be mediated by violence perception sensitivity. For this period, two findings were consistent across the countries. Emotional well-being showed a robust and significant negative effect in all the estimated models, which highlights its important role in understanding the victim-offender overlap. Meanwhile, the model comparisons conducted using the p -value indicated that incorporating the scales significantly improved the fit of the model in almost all cases, with the only exception of Model A in Mexico ( p = 0.7), where self-control and morality provided no additional explanatory power beyond the baseline model. Binomial Logistic Regression Predicting Membership in the Victim-Offender Profile (LTP) Spain Mexico Block A Block B Block A Block B Predictor Baseline Model A Baseline Model B Base Model A Baseline Model B Gender (male) 2.20 (1.37–3.59)** 1.49 (0.87–2.57) 2.02 (1.21–3.43)** 2.01 (1.16–3.52)* 1.43 (0.85–2.44) 1.39 (0.82–2.39) 1.31 (0.82–2.11) 1.42 (0.88–2.32) Age 1.19 (0.96–1.49) 1.19 (0.95–1.51) 1.11 (0.89–1.40) 1.12 (0.89–1.42) 1.34 (1.07–1.70)* 1.34 (1.07–1.69)* 1.21 (0.98–1.50) 1.19 (0.96–1.47) Emotional well-being 0.75 (0.61–0.92)** 0.70 (0.55–0.87)** 0.71 (0.57–0.88)** 0.74 (0.59–0.92)** 0.72 (0.56–0.91)** 0.71 (0.56–0.91)** 0.65 (0.53–0.80)*** 0.67 (0.54–0.83)*** Unstructured activities 1.07 (0.86–1.32) 1.07 (0.86–1.34) 1.10 (0.88–1.38) 1.07 (0.85–1.35) 1.38 (1.06–1.81)* 1.39 (1.06–1.82)* 1.26 (1.01–1.58)* 1.23 (0.98–1.54) Self-control — 0.83 (0.78–0.89)*** — — — 1.00 (0.94–1.06) — — Morality — 0.90 (0.81-1.00) — — — 0.95 (0.84–1.08) — — Violence perception sensitivity — — — 1.05 (0.99–1.10) — — — 1.05 (1.00-1.10)* Revenge — — — 1.09 (1.02–1.17)** — — — 1.04 (0.97–1.10) N (cases) 384 384 370 370 317 317 381 381 Pseudo R² (Nagelkerke) 0.07 0.22 0.07 0.11 0.09 0.10 0.10 0.12 p-value (vs. Baseline) — < 0.001 — 0.008 — 0.74 — 0.06 Note. Odds ratios are shown (CI 95%). *p < .05, **p < .01, ***p < .001. In the last year (LYP) analysis (Table 5 ), the predictors of the victim-offender profile also showed substantial differences between the two countries. In Spain, being male remained the most robust predictor in Block A (OR = 3.45*** y OR = 2.49*), although its effect was not significant in either of the two Block B models, which might point to a difference between the two subsamples used as baseline. Similarly, after incorporating the psychological variables, both low self-control (OR = 0.90**) and low morality (OR = 0.87*) emerged as significant risk factors. In Block B, revenge (OR = 1.19***) and perception of violence (OR = 1.07*) were identified as key predictors of membership in the victim-offender profile. In Mexico, in contrast to the LTP analysis, neither age nor unstructured activities were significant in the last year model. Instead, low morality (OR = 0.84*) was the only significant predictor in Block A. For Block B, none of the psychological variables (violence perception sensitivity and revenge) was statistically significant, and the model comparison confirmed that their inclusion did not improve the fit of the baseline model (p = 0.7). Cross-sectionally, for this period, emotional well-being remained a salient variable in almost all the models for both countries (except in Block A in Mexico). Table 5 Binomial Logistic Regression Predicting Membership in the Victim-Offender Profile (LYP) Spain Mexico Block A Block B Block A Block B Predictor Baseline Model A Baseline Model B Predictor Baseline Model A Baseline Gender (male) 3.65 (1.91–7.35)*** 2.49 (1.24–5.20)* 1.31 (0.70–2.46) 1.22 (0.61–2.43) 1.54 (0.76–3.18) 1.26 (0.60–2.68) 1.36 (0.75–2.51) 1.36 (0.74–2.54) Age 1.03 (0.78–1.36) 1.01 (0.76–1.36) 1.11 (0.85–1.48) 1.15 (0.86–1.54) 1.24 (0.91–1.69) 1.22 (0.89–1.67) 1.23 (0.94–1.62) 1.22 (0.93–1.61) Emotional well-being 0.73 (0.55–0.95)* 0.70 (0.53–0.92)* 0.68 (0.52–0.87)** 0.72 (0.55–0.94)* 0.76 (0.56–1.06) 0.72 (0.52–1.01) 0.75 (0.58–0.96)* 0.76 (0.59–0.99)* Unstructured activities 1.38 (1.04–1.83)* 1.37 (1.04–1.83)* 1.32 (1.00-1.74) 1.26 (0.95–1.68) 1.44 (1.00-2.05)* 1.43 (0.99–2.07) 1.25 (0.94–1.64) 1.25 (0.94–1.65) Self-control — 0.90 (0.83–0.97)** — — — 0.94 (0.86–1.02) — — Morality — 0.87 (0.76–0.99)* — — — 0.84 (0.71–0.97)* — — Violence perception sensitivity — — — 1.07 (1.00-1.16)* — — — 1.01 (0.95–1.07) Revenge — — — 1.19 (1.09–1.30)*** — — — 1.03 (0.96–1.12) N (cases) 377 377 367 367 311 311 378 378 Pseudo R² (Nagelkerke) 0.11 0.18 0.07 0.17 0.06 0.12 0.05 0.06 p-value (vs. Baseline) — < 0.001 — < 0.001 — 0.007 — 0.71 Note. Odds ratios are shown (CI 95%). *p < .05, **p < .01, ***p < .001. A comparison of the results for both timeframes reveals three main findings. First, emotional well-being is the most consistent variable in both countries, being significant in the vast majority of the analyses performed, for both LTP and LYP. Second, the explanatory power of the variables differs by country. In Spain, being male, low self-control and revenge are robust predictors in both periods. In contrast, in Mexico, variables such as age and unstructured activities are key in predicting membership in the profile over the lifetime but lose their predictive power when the analysis is only for the last year, where low morality appears to play a more notable role. Finally, the models that include psychological scales tend to have a greater predictive power in Spain (maximum R² of 0.22) compared to Mexico (maximum R² of 0.12), suggesting that the variables included are particularly relevant to understanding the victim-offender profile in the Spanish sample. Discussion The results of the present study open several avenues for discussion on the phenomenon of the overlap among adolescents, particularly in relation to its nature and its implications for intervention and prevention in this profile of youth, which are addressed below. The configuration of the overlap and the influence of context The results of the LCA provide important findings on the nature of the overlap. The first and most compelling finding is the consistent identification of a two-class solution. The adolescents in all four subsamples are best grouped into two profiles that distinguish between those with minimal contact with crime and those who have experienced it in both roles. Pure profiles, that is, victims only or offenders only, could not be identified, which suggests strong empirical support for the existence of the victim-offender overlap, even across contexts with notably different levels of security and rule of law (Berg & Schreck, 2022; Jennings et al., 2012). This does not mean there are no individuals in the sample that are only victims or only offenders, but rather that these cases do not form a sufficiently numerous or homogeneous profile, with consistent response patterns, to constitute a distinct latent class. The two-class model was thus the most parsimonious solution for both Mexican and Spanish adolescents, and for both lifetime and last-year experience of crime. Second, as we used a diverse series of experiences of victimization and offending and not general indicators of delinquent behavior (Berg & Mulford, 2017), we were able to establish that some experiences have a greater impact on the overlap than others. Cyber hate is the most frequent victimization experience in the victim-offender profile across both periods and countries, even being more prevalent than robbery. This suggests that the discriminatory dynamics of aggression in digital environments are not an innocuous or isolated phenomenon, but rather are interconnected with crime and victimization in the physical world, arguably acting as an indicator of cross-sectional risk. In fact, hate crime victimization has been shown to have more serious effects on physical, mental and behavioral health than other offenses (Mellgren et al. 2021 ), leading to loss of social identification and mortification, although their impact upon each individual is different (Funnell, 2015 ). Furthermore, differences between Spain and Mexico were also found in the victim-offender profiles, revealing that their configuration is not universal but is instead shaped by contextual influences. In Spain, compared to Mexico, this profile is characterized primarily by the commission of minor offenses typical of youth populations, such as shoplifting, graffiti, or group fights. This is linked to the greater engagement of Spanish adolescents in unstructured activities in public spaces, as has been observed in various other countries. (Buil et al, 2025). Meanwhile, in Mexico, compared to Spain, the profile is defined by higher victimization and greater probability of carrying weapons. These results add strength to the idea that, in contexts of high insecurity and violence, as is the case of Mexico, young people’s experience of delinquency is more linked to their exposure to victimization and the adoption of protection and survival strategies, such as carrying weapons (Kopf & Gresham, 2025 ; Kemal et al., 2024 ; Lizotte et al., 2000 ; Moss et al., 2024 ; Oliphant et al., 2019 ; Simon et al., 2022 ), or to lower involvement in unstructured activities, as suggested by our results. According to the 2025 National Victimization and Public Security Perception Survey (ENVIPE), in 2024, 63.1% of respondents reported having stopped allowing minors in their households to go out alone for fear of their being victims of crime. Finally, it is worth highlighting that the prevalences of the latent profiles are notably similar across both countries when comparing data for the last year with those for lifetime experiences, although the prevalence of the victim-offender profile is lower in the latter period. This finding supports the idea that the overlap between victimization and offending is linked to similar experiences that accumulate throughout adolescents’ lives. Further research is thus needed to elucidate the temporal sequencing of these different experiences and the causal relationships among them. (Birbeck et al, 2023, Berg & Mulford, 2017). Predictors of the victim-offender profile and implications for prevention The results of the regression analyses reveal a series of variables that are capable of predicting the risk of developing a victim-offender profile. These variables are not the same in the two countries, which suggests that the context provides key insights into adolescents’ experience with crime. This is discussed below. The results of comparing the lifetime and last-year models differ across the two countries. In the case of Spain, the variables associated with adolescents in the victim-offender profile in both models are being male, experiencing emotional distress, lower self-control, and displaying revenge-oriented attitudes. The last-year model reveals further factors, namely, greater involvement in unstructured activities, low morality, and violence perception sensitivity, suggesting that although the profile develops and consolidates over time around emotional distress, during adolescence, situational and attitudinal variables play a greater role in explaining these youths’ experiences with crime. In the Spanish case, membership in the victim-offender profile appears to be linked to a greater individual propensity toward antisocial behavior and attitudes that favor such conduct. This is a highly salient finding, suggesting that Situational Action Theory (SAT) (Wikström, 2014; Wikström et al., 2013) might explain not only delinquent behavior but also the victim-offender phenomenon. Although childhood maltreatment has been associated with lower self-control, lower morality, and greater exposure to criminogenic contexts (Doelman et al., 2023 ), very few studies have drawn on SAT to explore the relationship between victimization and offending. Furthermore, boys are at greater risk of belonging to this profile. In the lifetime model, this higher risk among boys can be explained by their greater individual propensity. In this sense, our results align with previous studies showing that the gender gap in adolescence can be explained, at least in part, by differences in individual propensity, in agreement with Situational Action Theory (SAT) (Hirtenlehner & Treiber, 2017). However, in the model for the last year, boys show a higher risk of belonging to this profile independently of opportunity and individual propensity (low self-control and low morality), as measured in the ISRD. This finding suggests there remains a need for further exploration of the gender gap in relation to both the victim-offender overlap and SAT, which has hitherto paid scant attention to questions of gender (see, for example, Hardie & Rose, 2025 ) In Spain, then, adolescents that have been both victims and offenders in the last year are typically boys that exhibit high individual propensity and high participation in unstructured activities, but also greater sensitivity to violence and pro-revenge attitudes. Although further research is needed, these findings are consistent with previous studies showing that different experiences of victimization in youth contribute not only to moral disengagement (Luo & Bussey, 2022 ), but also to a greater sensitivity to violence. In this regard, the combination of a high level of pro-revenge attitudes and high sensitivity to violence suggest that the more recent profile might be explained by a more expressive-affective than instrumental pattern of victimization and offending (Anderson & Huesmann, 2003 ). This is consistent with the type of less serious offenses that characterize this profile and the type of crimes of which they have been victim, that is, cyber hate. Hate speech causes multiple forms of harm; however, as argued by Díaz-Faes and Pereda ( 2022 ), online hate crimes are not simply common offenses with a moral aggravating factor, but rather complex manifestations of identity, power, and social structure, with the impact going beyond the individual to group identity. That this profile is predominantly observed among boys may suggest that the acts of harm they experience and perpetrate are closely linked to the social construct of masculinity. In this regard, several studies have pointed out that online violence is a practice that reinforces traditional gender roles (Cosma et al., 2022 ). Similarly, research on cyberbullying has highlighted the importance of identity aspects such as sexual orientation and gender expression for understanding and preventing different forms of harassment on social networks (Navarro, 2015 , Ojeda et al., 2023 ). In the case of Mexico, albeit with a lower predictive capacity, older age, emotional distress, engagement in unstructured activities, and violence perception sensitivity appear to play a role in the lifetime model. This suggests that, in Mexico, the phenomenon depends on experiences accumulated during adolescence that do not occur only as a result of greater individual propensity or higher participation in unstructured activities, although these remain risk factors. Instead, it may be the reflection of a highly insecure social environment that facilitates such experiences over the lifetime, even among individuals that present low individual propensity. In this sense, it seems likely that such adolescents are exposed to direct or indirect victimization, which heightens their sensitivity to violence and leads them to adopt self-protective behaviors that are typically identified as antisocial (such as carrying weapons). These variables do not remain significant, however, in the last-year model, with the exception of emotional distress, which, together with low morality, are the only distinguishing factors at this stage. The model thus combines both situational and individual variables, which, however, change over the course of life. In short, these findings help broaden understanding of the victim-offender overlap. First, the variables employed in this study more effectively predict the victim-offender profile among Spanish adolescents than among their Mexican counterparts. This confirms the need to delve deeper into the nature of this profile in countries of the Global South, given the bias of theoretical frameworks developed primarily in the Global North (Carrington et al., 2016 ). In our view, these frameworks fail to properly account for the contextual and cultural factors that arguably play a significant role in this phenomenon, and are thus insufficiently robust to enhance understanding of this criminological issue. In addition, it is worth noting that the variables associated with the most prominent theoretical explanations of the victim-offender overlap fail to generate models capable of explaining the variability between the low-risk and overlap groups, with R² values ranging between 0.6% and 22%. Further research is therefore needed to introduce novel explanatory hypotheses that help advance our understanding of this profile in young people, about whom there is still much to be understood. Indeed, as has been suggested, it is arguably time to expand criminological theory and embrace alternative perspectives (Berg & Schreck, 2022; Birbeck et al., 2023). In this vein, some authors have interpreted the victim-offender overlap as the result of the moral harm brought about by adverse experiences. This moral harm might successfully explain the trajectory from victimization to offending not through trauma itself and its emotional impact, but instead through the ethical and relational crisis that disrupts cognitions, emotions, and social bonds. Antisocial and violent behavior would thus be the consequence of morally injured young people that have lost their compass (Ava & Kerig, 2025). In this regard, an important finding is that emotional distress is the variable most consistently associated with the victim-offender profile, in both countries under study and across both lifetime and last-year models. We cannot know whether the emotional distress is prior these experiences, and is thus a key factor that might explain this profile as adolescents whose vulnerability and risk situation expose them to victimization or offending (McLachlan, 2025) or, conversely, whether it is a consequence of having experienced such situations (Houbre et al., 2006 ; McLoughlin et al., 2022 ). In any event, emotional distress should be addressed in the prevention of antisocial behavior and in interventions with these young people, regardless of whether intervention is a response to victimization or offending. In this sense, both child protection systems and the juvenile justice system should attend to the harm endured by adolescents beyond their specific experience with crime. Our results also suggest that prevention strategies should be contextualized. In both Spain and Mexico, attention should clearly be given to the interconnection between adolescents’ online and offline lives. However, in Spain, interventions could focus on the identity-related harm that young people perceive and inflict, particularly as regards experiences they interpret as grievances that justify revenge or diminish their sensitivity to violence, especially among boys. In this sense, harnessing a gender perspective may be particularly useful, as it allows us to understand the identity-related harm both experienced and inflicted during a crucial stage of development, and how differential socialization influences self-control and morality. In Mexico, although the results appear to confirm that reducing exposure to risk is both necessary and effective, coinciding with the arguments of Mulford et al. ( 2018 ), this presents a paradox: limiting exposure to public spaces to protect adolescents, may, at the same time, undermine their right to development and participation in the community. This constitutes a complex challenge for public policy and for the adults involved in adolescents’ development. Considerations on the participant recruitment method Finally, some considerations should be made on the method used to recruit our participants that might have affected the scope of the results. First, the analyses were conducted using two convenience samples; therefore, it is important to clarify that no conclusions can be drawn regarding the prevalence of the experiences reported in either country. The aim of this study is purely exploratory; we sought to broaden the understanding of the victim-offender overlap by means of a comparative analysis of two cultural contexts with markedly different levels of exposure to violence. Furthermore, it is necessary to consider the specific biases associated with the method used, namely panelist recruitment. Beyond the fatigue bias that may participants might experience after participation in multiple surveys, the literature has identified that the use of panelists introduces a bias related to socioeconomic status, as participation tends to be more prevalent among young people with greater leisure time and economic resources (Murray & Xie, 2024 ). Nonetheless, this type of non-probabilistic sampling is preferable to the use of river sampling, which entails an even greater coverage bias and offers less control over who participates (Lehdonvirta et al., 2020). As discussed at the outset of this study, we employed two samples of panelists from the same company, Metroscopia , which operates in both countries, using the same sample stratification method. Nevertheless, it is important to consider that although the socioeconomic bias of online panels is structural in nature, it is also influenced by national context, particularly among younger participants, since their availability is more strongly impacted by economic (Lehdonvirta et al., 2020) and digital factors (Blom et al., 2016 ). In this sense, the digital divide is larger in Mexico 4 , and thus the Mexican sample may be affected by an overrepresentation of adolescents of a socioeconomic status with greater access to Internet, thereby neglecting young people from more vulnerable environments with more extensive exposure to crime. Finally, it is worth noting that the literature has identified an overrepresentation of online victimization experiences in such samples (Oksanen et al., 2014 ). Nonetheless, given that the focus of this study is not on examining the prevalence of the behaviors experienced, the impact of this bias on the analysis is reduced and may even be considered a “useful bias,” which allows us to explore how victims and offenders interact within groups of young people with greater exposure to the Internet (Lehdonvirta et al., 2020). Declarations Author Contribution A.E.G.E. was responsible for data collection in Mexico, data curation, and the statistical analysis for this study. E.F.M. was responsible for data collection in Spain. E.F.M. wrote the theoretical framework; R.B.G. and A.E.G.E. expanded it. All three authors made joint decisions on variables and proposed models, discussed the results, and participated in writing and revising the discussion. References Anderson C. A., & Huesmann L. R. (2003). Human aggression: A social-cognitive view. In Hogg M.A., Cooper J. (Eds.), The Sage Handbook of Social Psychology (pp. 259–287). Sage. 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Journal of Adolescent Health, 74(1), 9-17. https://doi.org/10.1016/j.jadohealth.2023.06.032 Nakazawa M (2024)._fmsb: Functions for Medical Statistics Book with some Demographic Data. https://CRAN.R-project.org/package=fmsb Navarro, R. (2015). Gender issues and cyberbullying in children and adolescents: From gender differences to gender identity measures. Cyberbullying across the globe: Gender, family, and mental health , 35-61. Ojeda, M., Elipe, P., & Del Rey, R. (2023). LGBTQ+ Bullying and Cyberbullying: Beyond Sexual Orientation and Gender Identity. Victims & Offenders , 19 (3), 491–512. https://doi.org/10.1080/15564886.2023.2182855 Oksanen, A., Hawdon, J., Holkeri, E., Näsi, M., & Räsänen, P. (2014). Exposure to online hate among young social media users. In M.Nicole Warehime (Ed.). Soul of society: A focus on the lives of children & youth (pp. 253-273). Emerald Group Publishing Limited. https://doi.org/10.1108/S1537-466120140000018021 Oliphant, S. N., Mouch, C. A., Rowhani-Rahbar, A., Hargarten, S., Jay, J., Hemenway, D., Zimmerman, M., & Carter, P. (2019). A scoping review of patterns, motives, and risk and protective factors for adolescent firearm carriage. Journal of Behavioral Medicine , 42, 763-810. https://doi.org/10.1007/s10865-019-00048-x Organization for Economic Co-operation and Development [OECD]. (2025). OECD Better Life Index. Recuperado de https://www.oecd.org/en/data/tools/oecd-better-life-index.html R Core Team (2023). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ Radtke, S. R., Wretman, C. J., Fraga Rizo, C., Franchino-Olsen, H., Williams, D. Y., Chen, W. T., & Macy, R. J. (2024). A systematic review of conceptualizations and operationalizations of youth polyvictimization. Trauma, violence, & abuse , 25 (4), 2721-2734. https://doi.org/10.1177/15248380231224026 Simon, T. R., Clayton, H. B., Dahlberg, L. L., David-Ferdon, C., Kilmer, G., & Barbero, C. (2022). Gun Carrying Among Youths, by Demographic Characteristics, Associated Violence Experiences, and Risk Behaviors - United States, 2017-2019. MMWR. Morbidity and Mortality Weekly Report , 71(30), 953-957. https://doi.org/10.15585/mmwr.mm7130a1 United Nations Office on Drugs and Crime [UNODC]. (2024). Intentional homicide victims [Data set]. UNODC Data Portal. Recuperado de https://dataunodc.un.org/dp-intentional-homicide-victims Vilalta, C. & Fondevila, G. (2020). Perceived Police Corruption and Fear of Crime in Mexico. Mexican Studies, 36(3), 425-450. https://doi.org/10.1525/msem.2020.36.3.425 Walters, B. (2023). The Future of Free Speech. Palgrave Macmillan. White, N. A. (2014). Age and Crime. In J. M. Miller (Ed.), The Encyclopedia of Theoretical Criminology. Wiley-Blackwell. Wickham H, Miller E, Smith D (2023). haven: Import and Export 'SPSS', 'Stata' and 'SAS' Files . https://CRAN.R-project.org/package=haven Wickham H., François, R., Henry L., Müller K., Vaughan, D. (2023). dplyr: A Grammar of Data Manipulation . https://CRAN.R-project.org/package=dplyr. World Justice Project. (2024). WJP Rule of Law Index 2024. Recuperado de https://worldjusticeproject.org/rule-of-law-index/ Zeileis, A. & Hothorn, T. (2002). Diagnostic Checking in Regression Relationships. R News , 2(3), 7-10. https://journal.r-project.org/articles/RN-2002-018/ Footnotes In total, 33 cases (1.8% of the original sample) were excluded from the analysis, 10 from Spain and 23 from Mexico. The exclusion criterion was a control variable identifying the number of implausible responses provided by each participant. Of the cases excluded, 29 presented one implausible response, three presented two, and one case showed three. A novel aspect of the present study is the individual use of the items that comprise the measures of victimization and offending, rather than grouping them, as has typically been done in previous research. This approach responds not only to the catalogue of offenses included in this survey being relatively limited, but also to our consideration that the integration of items should not be assumed without prior testing, particularly when evaluating the overlap between victimization and offending, rather than the diversification of the behaviors experienced. In the regression analyses, only comparisons between male and female participants were conducted; therefore, the cases in which participants identified as non-binary were not included in these analyses. According to 2024 data from INEGI, in Mexico, 83,1% of the population aged six years and older are frequent internet users, whereas in Spain, according to data from the National Statistics Institute, 95,8% of individuals aged sixteen years and older report frequent use. In Mexico, only 41,8% of households have a computer (16,3% in rural areas), compared with 83% in Spain (70,9% in rural areas, according to the National Observatory for Technology and Society). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Apr, 2026 Read the published version in International Criminology → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8002022","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":543559096,"identity":"ef396927-4eb2-48c9-8fe2-7dca0e2dbeaa","order_by":0,"name":"Áurea E. 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1","display":"","copyAsset":false,"role":"figure","size":130952,"visible":true,"origin":"","legend":"\u003cp\u003eLatent Class Profiles of Victimization and Offending by Country and Timeframe\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8002022/v1/e512b8d5f47c6247db1a2b46.png"},{"id":106809389,"identity":"3400656e-09d7-421e-902b-f82e91e39146","added_by":"auto","created_at":"2026-04-13 16:10:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1228320,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8002022/v1/858ae786-33e5-4b2b-a5df-3795fef15f24.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Intersection of Victimization and Delinquency in Adolescents: Comparative Evidence from Mexico and Spain","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAdolescent involvement in crime remains a central concern in criminology and a cornerstone of crime prevention, as young people account for a substantial share of global offending. Adolescence represents both a developmental period marked by peak levels of criminal behavior\u0026mdash;consistently reflected in the age\u0026ndash;crime curve across diverse contexts (Benson, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Le Blanc, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; White, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u0026mdash;and a critical phase of legal socialization, during which individuals internalize norms, recognize authority, and form enduring attitudes toward law and institutions (Oliveira \u0026amp; Jackson, 2021; Reisig et al., 2011; Tyler \u0026amp; Trinkner, 2017).\u003c/p\u003e\u003cp\u003eYoung people, however, are not only involved in delinquency as offenders, but also, and to a great extent, are subjected to situations of victimization, which may include child maltreatment, conventional crime, community violence, cyberviolence, exposure to parental violence, and peer or sibling violence. While, in the past, researchers often examined single types of violence against youth, this focus has shifted in recent decades. Violence against youth is often complex and does not occur in isolated ways. Hence, the present study seeks to explore the circumstances and contexts in which adolescents are victims of crime (Radtke et al, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDelving into the study of adolescents\u0026rsquo; experiences of crime entails significant methodological complexities. Researchers face difficulties in obtaining representative samples, navigating increasingly stringent ethical requirements, securing access through schools and parents, and maintaining participants\u0026rsquo; engagement throughout surveys (Fern\u0026aacute;ndez-Molina \u0026amp; Bartolom\u0026eacute; Guti\u0026eacute;rrez, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consequently, despite extensive research on youth offending and victimization, the availability of data suitable for comparative analysis remains limited.\u003c/p\u003e\u003cp\u003eThe International Self-Report Delinquency Study (ISRD) represents a major international collaboration designed to collect data on adolescents\u0026rsquo; experiences with crime, traditionally through school-based surveys. However, access to schools has become increasingly difficult, prompting researchers to explore alternative data collection strategies (Enzmann et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Marshall et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In response, the fourth wave of the study (ISRD4) supplemented the conventional school survey with a shortened online version administered to adolescents recruited via digital panels. This innovation aimed to diversify samples and assess the feasibility of online data collection. Among the participating countries, Spain and Mexico adopted parallel recruitment procedures by contracting the same survey firm, Metroscopia, which applied comparable sampling strategies across both contexts. This alignment provides a valuable basis for cross-national comparison.\u003c/p\u003e\u003cp\u003eThe present study seeks to advance understanding of adolescents\u0026rsquo; involvement in delinquency in Spain and Mexico, particularly the connection between victimization and offending during adolescence. Despite there being an extensive body of literature on the so-called victim-offender overlap, much of the research on adolescents\u0026rsquo; experiences with crime continues to analyze victimization and offending as independent phenomena, often using additive scales or mean scores of affirmative responses to a list of experiences. Here, we aim to identify patterns of co-occurrence between victimization and offending using latent class analysis (LCA) based on multiple indicators of delinquent experiences.\u003c/p\u003e\u003cp\u003eThe contrast between the national contexts makes the present study particularly relevant. Mexico faces severe challenges regarding security and the rule of law, as reflected in its 2024 Rule of Law Index score of 0.41\u0026mdash;well below Spain\u0026rsquo;s 0.71 (World Justice Project, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This disparity is even greater in specific dimensions such as crime control, where Mexico records the lowest value among 142 countries (0.38), compared with Spain\u0026rsquo;s 0.86. Likewise, Mexico\u0026rsquo;s homicide rate of 24.86 per 100,000 inhabitants far exceeds Spain\u0026rsquo;s 0.69 (United Nations Office on Drugs and Crime [UNODC], \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMexican youth are exposed to particularly high contextual risks. Official statistics indicate that 378,816 individuals are currently reported missing, 35.4% of whom are between 10 and 19 years old (Comisi\u0026oacute;n Nacional de B\u0026uacute;squeda [CNB], \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Paradoxically, despite the severe security situation, a greater proportion of Mexicans report feeling happy compared to Spaniards (92.1% vs. 88.8%) (Haerpfer et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Data from the OECD (2025), however, offer a more nuanced picture: Mexico ranks low on the Better Life Index, particularly in the safety dimension, while Spain scores highest on this indicator. These marked contextual contrasts provide a valuable backdrop for examining whether the victim\u0026ndash;offender overlap is shaped by broader cultural and structural conditions.\u003c/p\u003e\n\u003ch3\u003eAdolescents’ contact with delinquency\u003c/h3\u003e\n\u003cp\u003eCriminological research determined decades ago that victims and offenders are often the same people. This phenomenon, known as the victim-offender overlap, has been documented across different countries, cultures and types of crimes, and has been acknowledged as a criminological fact (Jennings et al., 2012). The correlation between victimization and offending is robust compared with other effects reported in criminological studies (r values of between 0.24 and 0.50) (Beckley et al, 2018).\u003c/p\u003e\u003cp\u003eThe victim-offender overlap was initially documented in studies on victims, who were found to be more likely to have criminal records (Berg \u0026amp; Schreck, 2022; Jennings et al., 2012). Subsequently, self-report studies that included questions on experiences of both victimization and offending have shown that a part of the sample reported having been victims of one or more offenses while also having committed one or several offenses (Birbeck et al., 2023). Given the observational and cross-sectional nature of most of these studies, it has not been possible to identify causal mechanisms, and the phenomenon thus remains difficult to understand and explain (Berg \u0026amp; Mulford, 2017). Despite these limitations, it has been found that the overlap manifests across diverse contexts\u0026mdash;regardless of age, gender, race, or country (Berg \u0026amp; Schreck, 2022). Moreover, during adolescence, the degree of overlap is higher (Beckley et al., 2018), particularly among high-level offenders (Erdmann \u0026amp; Reinecke, 2019), while in disadvantaged environments, the likelihood of simultaneously being both a victim and an offender is multiplied (Delong \u0026amp; Reichert, 2019). Likewise, the overlap appears to be stronger in violent offenses (e.g., fights, assaults, homicides) than in property crimes (e.g., thefts, burglaries).\u003c/p\u003e\u003cp\u003eFurthermore, longitudinal research and trajectory analyses have identified a victim-offender profile that is distinct from that of individuals who are only offenders or only victims. It has been evidenced that victim-offenders show differences from the other groups, reporting lower levels of psychological well-being, social interaction, and engagement in conventional activities (Birkbeck et al., 2023). Nevertheless, the three groups share certain individual and social risk factors, including low self-control, alcohol and drug use, association with delinquent peers, neighborhood disorganization, and low parental monitoring (Jennings et al., 2012).\u003c/p\u003e\u003cp\u003eDespite the overwhelming evidence, as noted by Birkbeck et al. (2023), there has been no debate on the definition and measurement of this overlap that might help clarify its scope and so improve the understanding of delinquency. In particular, the authors underline two factors worth noting. On the one hand, the available evidence is derived both from probabilistic research, which has sought to explain the possible causal relationships between victimization and offending, and from categorical research, which has explored the victim-offender group as either a cause or a consequence. On the other hand, little thought has been given to the time periods used to document victimization and offending experiences in self-report studies, although these might account for very different processes. Hence, the authors conclude that the overlap remains an elastic concept, requiring further research and deeper reflection on the varying scope of the available evidence.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eTheoretical frameworks to explain the overlap\u003c/h2\u003e\u003cp\u003eAlthough, as explained, the victim-offender overlap is a phenomenon that has been more widely observed than understood, criminological research has sought to explain it through the explanatory frameworks commonly used in the discipline. Thus, two theoretical frameworks have been particularly influential in attempts to explain the overlap: Gottfredson and Hirschi\u0026rsquo;s self-control theory, and situational theories, including Felson\u0026rsquo;s routine activity theory. Both perspectives suggest that the same processes explain both victimization and offending, challenging the classical notion that victims and offenders are separate and opposing populations (Berg \u0026amp; Schreck, 2022).\u003c/p\u003e\u003cp\u003eFollowing self-control theory, victimization and offending are not independent events but manifestations of a shared underlying factor, that is low self-control and a limited ability to foresee consequences. Individuals with low self-control are more inclined to engage in impulsive, risky, and often antisocial behaviors, simultaneously leading them to be more prone to offending and exposure to situations in which they may be victimized. From a more contemporary perspective, low self-control can be understood as part of a heterogeneous risk, whereby victimization, injury, accidents, and antisocial behavior tend to concentrate within the same persons (Berg \u0026amp; Mulford, 2017).\u003c/p\u003e\u003cp\u003eSituational theories, meanwhile, posit the many crimes, particularly violent ones, are the result of interactions in which both victims and offenders actively participate, without their having fixed roles. Thus, an individual may start as a victim but respond as an aggressor, or vice versa. Under this perspective, everyday behaviors and social dynamics determine the opportunities to both commit and experience offenses. This approach shows that risky lifestyles, in contexts of unstructured peer socialization, increase the opportunities for offending and victimization, or even for both at the same time. As suggested by Berg and Mulford (2017), empirical evidence from qualitative and longitudinal studies supports this view, which neither assumes that victims are passive or that offenders act unilaterally\u003c/p\u003e\u003cp\u003eHowever, these two opposing theoretical paradigms are unable to explain why more young people from disorganized communities have greater contact with crime in either of its two roles (Berg \u0026amp; Mulford, 2017). To this end, community subcultural explanations have made their own particular contribution to understanding overlap, whereby subculture theories emphasize the role of culture in generating criminal opportunity. The code of the street that exists in certain environments in which young people socialize, where aggression and the use of force to maintain reputation are admired, exposes them to situations of both victimization and offending in order to maintain their status in the group (Jennings et al 2012).\u003c/p\u003e\u003cp\u003eRecently, perspectives that are more psychological than criminological have proposed life course\u0026rsquo;s explanations. Developmental research indicates that the overlap does not randomly arise in adolescence, but rather responds to the sum of multiple childhood adversities that foster the accumulation of risks (Beckley et al., 2018, Malvaso et al, 2022). This could be the result of learning and reinforcement processes triggered by evocative traits, as would be the case with low self-control. It could also be due to experiences of adversity, particularly in childhood and especially polyvictimization, altering psychological, social, and neurological development, shaping trajectories of both victimization and criminalization (McLachlan, 2025, Malvaso et al., 2022).\u003c/p\u003e\u003cp\u003eNotwithdstanding the above, and as Berg and Schreck (2022) acknowledge, the evidence on the victim-offender overlap demands a redefinition of criminological theory and policy, replacing the question of \u003cem\u003e\u0026ldquo;How do we reduce offending?\u0026rdquo;\u003c/em\u003e to an equally challenging one: \u003cem\u003e\u0026ldquo;How do we reduce vulnerability and exposure to risks generated by both victimization and offending?\u0026rdquo;\u003c/em\u003e Some authors advocate the development of an integrative theory (Jennings et al., 2012), while others suggest that it is arguably time to set aside criminological theories and develop an alternative approach that can explain the differences between victim-offenders and all the other roles (offenders only, victims only, and those not involved in crime) (Birkbeck et al., 2023). Meanwhile, authors overall find it is necessary to seek more innovative designs that allow for a better understanding of the dynamics of victimization and offending processes (among others, Berg \u0026amp; Mulford, 2017, Jennings et al., 2012). Moreover, most of the scientific literature in this regard stems from the Global North, and it is thus crucial to obtain further evidence from other regions of the world so as to be able to generalize on the nature, causes, and consequences of the overlap (Birkbeck et al., 2023).\u003c/p\u003e\u003cp\u003eThe present study seeks to contribute to the existing knowledge by means of a comparative study. Specifically, it aims to identify subgroups of adolescents based on their experiences as victims and/or offenders, and to determine whether these patterns differ between a country with high levels of violence (Mexico) and one with low levels (Spain).\u003c/p\u003e\u003c/div\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eData and Participants\u003c/h2\u003e\u003cp\u003eThe data in this study come from the fourth wave of the International Self-Report Delinquency Study (ISRD4), using a shortened version of the instrument. The data were collected by the \u003cem\u003eMetroscopia\u003c/em\u003e firm in Mexico (from January 25 to February 4, 2023) and in Spain (from June 2 to June 21, 2022). Using a non-probabilistic quota sampling procedure, the participants were recruited from among young people registered in \u003cem\u003eMetroscopia\u003c/em\u003e\u0026rsquo;s online panels. Quotas were established by gender (female, male) and age (16, 17, 18, and 19 years), with the goal of recruiting a minimum of 100 participants for each of the eight resulting combinations. Data collection concluded once all the quotas were filled, resulting in a final sample that exceeded the initial target of 800 participants per country. To reduce respondent burden, a split-ballot questionnaire design was employed. The participants in each country were randomly assigned to one of two versions of the instrument, meaning that some of the variables used in this study were not available for the entire sample. The survey was administered online and was completed in a mean time of 20 minutes.\u003c/p\u003e\u003cp\u003e The participants were informed of the objectives of the study and gave their explicit written consent before completing the questionnaire. Confidentiality and anonymity were guaranteed throughout the process; no personal information that could allow respondents to be identified was collected. In both countries, the study was approved by an ethics committee: in Mexico, by the Research Ethics Committee of the University Center for Social Sciences and Humanities at the University of Guadalajara, and in Spain, by the Social Research Ethics Committee of the University of Castilla-La Mancha.\u003c/p\u003e\u003cp\u003eAfter data cleaning,\u003csup\u003e1\u003c/sup\u003e the final sample comprised 1797 adolescents (898 Spanish and 899 Mexican participants), following the exclusion of individuals that provided implausible responses. The mean age was 7.51 (SD\u0026thinsp;=\u0026thinsp;1.11), while 48.6% of the participants were female (n\u0026thinsp;=\u0026thinsp;873) and 45.5% were male; 2% identified as non-binary (n\u0026thinsp;=\u0026thinsp;35).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003eThe dependent variables were of two types. The first was a set of observable indicators used to construct the latent profiles, while the second was membership in these profiles, which was used as a categorical variable in the regression analyses.\u003c/p\u003e\u003cp\u003eFor the LCA, we used 24 dichotomous items (0\u0026thinsp;=\u0026thinsp;No, 1\u0026thinsp;=\u0026thinsp;Yes), grouped into four categories: lifetime victimization, last-year victimization, lifetime offending and last-year offending.\u003csup\u003e2\u003c/sup\u003e All of these variables were analyzed separately for Mexico and Spain. For both lifetime victimization and last-year victimization, respondents were asked whether they had been victims of \u003cem\u003erobbery with violence\u003c/em\u003e, \u003cem\u003eassault\u003c/em\u003e, \u003cem\u003enon-consensual online sharing of intimate content (NCSIC)\u003c/em\u003e, or \u003cem\u003ecyber hate\u003c/em\u003e. In the case of lifetime offending and last-year offending, participants were asked whether they had engaged in any of the following behaviors: \u003cem\u003egraffiti\u003c/em\u003e, \u003cem\u003eshoplifting\u003c/em\u003e, \u003cem\u003eburglary\u003c/em\u003e, \u003cem\u003eweapon carrying\u003c/em\u003e, \u003cem\u003egroup fight\u003c/em\u003e, \u003cem\u003eassault\u003c/em\u003e, \u003cem\u003eNCSIC\u003c/em\u003e, and \u003cem\u003ehacking\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eBased on the LCA, four dichotomous dependent variables were generated, representing participants\u0026rsquo; membership in one of the two profiles identified, namely, a low-risk profile with limited experience of crime in either of the two roles (coded as 0) and a victim-offender profile (coded as 1). This variable was estimated for each of the four analyses: Mexico-lifetime, Mexico-last year, Spain-lifetime, and Spain-last year.\u003c/p\u003e\u003cp\u003eFurthermore, three groups of dependent variables were included in the analysis, as follows:\u003c/p\u003e\u003cp\u003eTo assess the influence of situational factors on the overlap, we used a proxy measure of \u003cem\u003eunstructured activities in public spaces\u003c/em\u003e. This was measured through a 5-point Likert-type item asking participants how frequently, in a typical week, they spent time in the street, in shopping malls, or in their neighborhood (1\u0026thinsp;=\u0026thinsp;Never to 5\u0026thinsp;=\u0026thinsp;Every day).\u003c/p\u003e\u003cp\u003eBeyond the situational perspective, we incorporated a set of variables referring to psychosocial indicators, which have also been associated with the overlap. Specifically, the online version of the ISRD 4 includes additive Likert-type scales that measure, on the one hand, \u003cem\u003eself-control\u003c/em\u003e (5 items, 5 points; e.g. I act on the spur of the moment without stopping to think, Sometimes I will take a risk just for the fun of it) and \u003cem\u003emorality\u003c/em\u003e (6 items, 4 response points; e.g. Purposely damage or destroy someone else\u0026rsquo;s property, Hack or break into a private account or computer to acquire data, get control of an account, or destroy data). We also used the scales that measure \u003cem\u003eviolence perception sensitivity\u003c/em\u003e (6 items, 4 response points; e.g. Sharing online an intimate photo or video of someone that he or she did not want others to see, Hitting another person without causing injury) and \u003cem\u003erevenge\u003c/em\u003e (4 items, 4 response points; e.g. It is important for me to get back at people who have hurt me, There is nothing wrong in getting back at someone who has hurt you). All the scales presented an adequate internal consistency, measured using Cronbach\u0026rsquo;s alpha: self-control (α\u0026thinsp;=\u0026thinsp;0.79), morality (α\u0026thinsp;=\u0026thinsp;0.83), violence perception sensitivity (α\u0026thinsp;=\u0026thinsp;0.90) and revenge (α\u0026thinsp;=\u0026thinsp;0.78).\u003c/p\u003e\u003cp\u003eAs control variables, we included participants\u0026rsquo; \u003cem\u003eage\u003c/em\u003e (continuous variables), \u003cem\u003egender\u003c/em\u003e (dichotomous variables, 0\u0026thinsp;=\u0026thinsp;female and 1\u0026thinsp;=\u0026thinsp;male)\u003csup\u003e3\u003c/sup\u003e and a proxy to capture the adolescents\u0026rsquo; level of well-being, assessed on the a 6-point Likert-type scale on their level of happiness over the past six months (1\u0026thinsp;=\u0026thinsp;very unhappy to 6\u0026thinsp;=\u0026thinsp;very happy).\u003c/p\u003e\n\u003ch3\u003eAnalytical Strategy\u003c/h3\u003e\n\u003cp\u003eThe analysis was conducted in three phases. First, we compared the characteristics of the Mexican and Spanish samples by examining the differences in the prevalences of the 24 indicators of victimization and delinquent behavior, as well as the mean values or proportions of the independent variables. To this end, chi-square tests were used for the categorical variables and independent-samples t-tests for the continuous variables.\u003c/p\u003e\u003cp\u003eSecond, we performed an LCA to identify the profiles of the adolescents based on their victimization and offending experiences. Models with two to four classes were estimated for each of the four subgroups (Spain LTP, Mexico LTP, Spain LYP, and Mexico LYP), for the purpose of determining the optimal number of classes. Subsequently, the participants\u0026rsquo; profiles were created, according to their membership in one of the latent classes identified in the process. The selection of the final model was primarily based on the Bayesian information criterion (BIC), prioritizing the lowest value obtained. This was chosen as it is the most reliable fit statistic for model selection, as it indicates the model with the greatest parsimony (Weller et al., 2020).\u003c/p\u003e\u003cp\u003eFinally, to identify the variables associated with membership in the victim-offender profile, we estimated a series of binomial logistic regressions. Due to the split questionnaire design, the analysis was structured in two independent blocks for each of the four subsamples (Spain LTP, Mexico LTP, Spain LYP, and Mexico LYP). We used the same baseline model, which included the control variables and unstructured activities. Based on this model, in Block A, we evaluated the contribution of Model A, which included the self-control and morality scales. In Block B, similarly, the contribution of the violence perception sensitivity and revenge scales to the base model was assessed. In both blocks, the incremental contribution of the added scales was evaluated using the likelihood ratio test.\u003c/p\u003e\u003cp\u003eOur data analysis was conducted using R software (version 4.3.1). The LCA was performed using the poLCA package (Linzer \u0026amp; Lewis, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e); the regression models were estimated with the stats (R Core Team, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), lmtest (Zeileis \u0026amp; Hothorn, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) and fmsb (Nakasawa, 2024) packages; and data filtering was conducted through the haven (Wickham \u0026amp; Miller, 2023) and dplyr (Wickham et al., 2024) packages.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eDescriptive statistics and comparison between countries\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the descriptive statistics for the total sample and by country. As can be seen, various significant differences were found between respondents in Mexico and Spain. The mean age of the Spanish adolescents was slightly higher (t\u0026thinsp;=\u0026thinsp;2.8**) than that of their Mexican counterparts. They also scored higher on engagement in unstructured activities (t\u0026thinsp;=\u0026thinsp;13***) and revenge (t\u0026thinsp;=\u0026thinsp;3.4***), while also reporting lower self-control (t=-3.8***).\u003c/p\u003e\u003cp\u003eSubstantial differences were also observed in experience of crime. The Spanish adolescents reported higher lifetime involvement in offending, particularly in the following categories: graffiti (χ\u0026sup2;=86***), shoplifting(χ\u0026sup2;=135***), burglary (χ\u0026sup2;=8.5**), group fight (χ\u0026sup2;=4.5*), assault (χ\u0026sup2;=5.3*), and hacking (χ\u0026sup2;=11***). In contrast, the only lifetime offense with a higher prevalence among Mexican adolescents was weapon carrying (χ\u0026sup2;=9.3**). No statistically significant differences were found between the two samples in any of the lifetime victimization variables.\u003c/p\u003e\u003cp\u003eAs regards experience in the last year, the results were similar. The Spanish adolescents reported higher involvement in offending, although the differences were only significant in the cases of graffiti (χ\u0026sup2;=24***), shoplifting (χ\u0026sup2;=49***), and burglary (χ\u0026sup2;=3.9*), while the Mexican participants again reported carrying weapons with greater frequency (χ\u0026sup2;=6.2*). Victimization experience in the last year was similar in both countries, except in the case of cyber hate (χ\u0026sup2;=8.3**), which was more prevalent in Mexico\u003c/p\u003e\u003cp\u003eFinally, it is worth highlighting several interesting findings from this descriptive analysis. First, the most frequently reported form of victimization in both countries and across both periods (lifetime and last year) was cyber hate. Second, the most commonly reported lifetime and last-year offense in Spain was shoplifting (LTP\u0026thinsp;=\u0026thinsp;35% and LYP\u0026thinsp;=\u0026thinsp;11%), whereas in Mexico it was weapon carrying (LTP\u0026thinsp;=\u0026thinsp;13% and LYP\u0026thinsp;=\u0026thinsp;7.3%). Third, a consistent pattern was observed across all measures of experience with crime, in both countries and as both victim and as offender: the lifetime prevalence reported was substantially higher than that reported for the last year.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eDescriptive statistics for the total sample and by country\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;1797\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSpain\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;898\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMexico\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;899\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eComparison\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocioeconomic variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.51 (1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.58 (1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.44 (1.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.8**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e889 (49.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e445 (49.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e444 (49.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e873 (48.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e440 (49.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e433 (48.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-binary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLifestyle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEmotional well-being\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.42 (1.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.37 (1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.47 (1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSituational factors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnstructured activities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.82 (1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.14 (1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.49 (1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePsychosocial scales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelf-control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.04 (4.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.48 (4.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.68 (4.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-3.8***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMorality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.55 (2.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.46 (2.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22.65 (2.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eViolence perception sensitivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.36 (5.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.27 (5.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.44 (5.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRevenge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.46 (3.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.93 (3.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.03 (3.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.4***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVictimization LTP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRobbery with violence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e242 (14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e127 (14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e115 (13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAssault\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e135 (7.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73 (8.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e62 (7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNCSIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e129 (7.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65 (7.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64 (7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCyber hate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e300 (17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e140 (16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e160 (18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOffending LTP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGraffiti\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e189 (11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e156 (18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33 (3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e86***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShoplifting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e394 (23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e301 (35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e93 (11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e135***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBurglary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23 (2.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.5**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWeapon carrying\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e173 (10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68 (8.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e105 (13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.3**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGroup fight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e199 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e116 (14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e83 (10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.5*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAssault\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10 (1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.3*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNCSIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26 (3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHacking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e92 (5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63 (7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29 (3.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVictimization LTP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRobbery with violence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e124 (7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68 (7.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e56 (6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAssault\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64 (3.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28 (3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNCSIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56 (3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (3.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCyber hate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e203 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82 (9.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e121 (14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.3**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOffending LYP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGraffiti\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67 (3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54 (6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e24***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShoplifting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e114 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94 (11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20 (2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e49***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBurglary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.9*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWeapon carrying\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98 (5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e61 (7.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.2*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGroup fight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e109 (6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64 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colname=\"c3\"\u003e\u003cp\u003e24 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHacking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40 (2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26 (3.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eMean score (SD) for continuous variables and n (%) for categorical variables. \u003csup\u003e2\u003c/sup\u003eT-test for continuous variables, χ\u0026sup2;-test for continuous variables; *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eIdentification of latent profiles\u003c/h2\u003e\u003cp\u003eLatent class analysis (LCA) is an approach that focuses on the subjects of a study, in our case, the survey respondents, being designed to identify unobserved subgroups (latent classes) within a larger, heterogenous population. The technique consists of grouping participants into internally homogenous groups, based on their response patterns across a series of categorical variables (Collins \u0026amp; Lanza, 2010; Eshima, 2022). Hence, in contrast to methods that center on variables and the differences between them, LCA organizes individuals according to their common characteristics.\u003c/p\u003e\u003cp\u003eGiven that the overlap between victimization and offending is grounded in the idea that being a victim and being an offender are interconnected phenomena, LCA is an appropriate tool for identifying the patterns underlying this connection within a sample. It is intended to reveal individual profiles according to their combined experiences of victimization and delinquent behaviors.\u003c/p\u003e\u003cp\u003eWith the aim of exploring whether the overlap between victimization and offending is shaped differently across the two countries, the LCA models were independently estimated for Mexico and Spain, considering two periods of analysis: lifetime (LT) and the last year (LY). Table\u0026nbsp;2 presents the fit statistics for all the models estimated.\u003c/p\u003e\u003cp\u003eIn all four cases, the lowest BIC value was found in the two-class latent solution, indicating that it was the most parsimonious model and that with the best fit (BIC Spain LT\u0026thinsp;=\u0026thinsp;5,667.08; BIC Mexico LT\u0026thinsp;=\u0026thinsp;4,381.12; BIC Spain LY\u0026thinsp;=\u0026thinsp;3,268.88; BIC Mexico LY\u0026thinsp;=\u0026thinsp;2,658.17). The entropy value, which measures the clarity of classification, was consistently higher in the analyses of the last-year data (LYP) compared to those referring to lifetime prevalence (LTP). Following the criterion proposed by Weller et al. (2020), the entropy values obtained are considered acceptable, being moderate for LTP (Mexico\u0026thinsp;=\u0026thinsp;0.63; Spain\u0026thinsp;=\u0026thinsp;0.70) and high for LYP (Mexico\u0026thinsp;=\u0026thinsp;0.79; Spain\u0026thinsp;=\u0026thinsp;0.82). In light of these results, the two-class models were selected for all the subsequent analyses.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTable\u0026nbsp;2. Model Fit Statistics for Latent Class Analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"595\" height=\"370\"\u003e\u003c/p\u003e\u003c/div\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the profiles identified in each country and for each timeframe. As can be seen, two distinct profiles were identified across both countries and time periods. The first, labeled \u003cem\u003elow-risk\u003c/em\u003e (depicted in blue), is characterized by minimal experience with crime, either as victim or offender, suggesting low probabilities of affirmative responses across all items. The second profile, labeled \u003cem\u003evictim-offender\u003c/em\u003e (depicted in red), is characterized by higher probabilities of contact with crime in either or the two roles. This second profile, which, as reported in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, was a minority in all cases (Spain LTP\u0026thinsp;=\u0026thinsp;28.1%, Mexico LTP\u0026thinsp;=\u0026thinsp;28.4%, Spain LYP\u0026thinsp;=\u0026thinsp;14.9%, Mexico LYP\u0026thinsp;=\u0026thinsp;13.9%), exhibited notable differences between Spain and Mexico.\u003c/p\u003e\u003cp\u003eThe characteristics defining the victim-offender profile, detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, reveal a key distinction between the two countries. Although in both countries the three variables combine experiences of victimization and offending, in Spain, both over the lifetime and during the last year, the most salient variables were primarily offenses considered as minor, specifically shoplifting, graffiti (lifetime), and group fight (last year). In contrast, the victim-offender profile in Mexico was defined, in both periods, first by victimization (cyber hate and robbery) and subsequently by a high-risk offense, namely, weapon carrying.\u003c/p\u003e\u003cp\u003eA noteworthy finding common to all the four cases examined pertains to the role of cyber hate victimization. As mentioned in the previous section, not only was this the most prevalent form of victimization in both countries, but it was also consistently among the three key variables for defining the victim-offender profile in all the overlap subgroups analyzed.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eSummary of Victim-Offender Profiles\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCountry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTimeframe\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrevalence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTop 3 Characteristics\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLTP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eShoplifting (O) (66%) | Graffiti (O) (44%) | Cyber Hate (V) (38%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMexico\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLTP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCyber Hate (V) (47%) | Robbery (V) (34%) | Weapon Carrying (O) (33%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLYP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eShoplifting (O) (43%) | Group Fight (O) (42%) | Cyber Hate (V) (36%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMexico\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLYP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCyber Hate (V) (51%) | Robbery (V) (37%) | Weapon Carrying (O) (34%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003ePredictors of Membership in the Victim-Offender Profile\u003c/h2\u003e\u003cp\u003eTo identify the variables predicting membership in the victim-offender profile, a series of hierarchical binomial logistic regressions were conducted. Given the split-ballot design of the questionnaire, we were unable to jointly assess the influence of all the independent variables. Consequently, the analysis was performed using two independent blocks of models for each country and timeframe.\u003c/p\u003e\u003cp\u003eBlock A compared a baseline model, which included sex, age, and emotional well-being as control variables, and, as an explanatory variable, unstructured activities, with Model A, which further incorporated the scales of self-control and morality. Block B started from an identical baseline model, estimated on the corresponding subsample, and compared it with Model B, which included the violence perception sensitivity and revenge scales.\u003c/p\u003e\u003cp\u003eThe results of the estimation for the lifetime period are presented in Table\u0026nbsp;4, while those for the last year are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The results of the logistic regressions for both the periods analyzed reveal the predictors of the victim-offender profile differ between Spain and Mexico. Below, we describe some of the most salient findings for the lifetime period, as shown in Table\u0026nbsp;4.\u003c/p\u003e\u003cp\u003eIn Spain, being male was found to be a key predictor. It showed a significant positive association in the Block A baseline model (OR\u0026thinsp;=\u0026thinsp;2.20**) and in the Block B comparison (OR\u0026thinsp;=\u0026thinsp;2.02** and OR\u0026thinsp;=\u0026thinsp;2.01*). It is worth noting that the significance of gender disappeared once the variables of Model A were introduced (OR\u0026thinsp;=\u0026thinsp;1.49), with only low self-control being statistically significant (OR\u0026thinsp;=\u0026thinsp;0.83***). This suggests that the effect of gender may be mediated by differences in self-control between males and females. Additionally, in Block B, revenge (OR\u0026thinsp;=\u0026thinsp;1.09**) was also significantly associated with membership in the victim-offender profile.\u003c/p\u003e\u003cp\u003eIn Mexico, age (OR\u0026thinsp;=\u0026thinsp;1.34*) and engagement in unstructured activities (OR\u0026thinsp;=\u0026thinsp;1.38* and OR\u0026thinsp;=\u0026thinsp;1.39*) had a significant effect in the Block A comparison. In contrast to the Spanish case, neither gender nor self-control was key in this profile. An interesting finding in the Mexican sample was that age did not remain significant in the baseline model of Block B, despite being estimated on a very similar subgroup of participants. Additionally, the effect of unstructured activities lost its significance in Model B (OR\u0026thinsp;=\u0026thinsp;1.23) once violence perception sensitivity (OR\u0026thinsp;=\u0026thinsp;1.05*) was included, which was the only variable that exhibited statistical significance. This suggests that, within this adolescent profile, the effect of unstructured activities may be mediated by violence perception sensitivity.\u003c/p\u003e\u003cp\u003eFor this period, two findings were consistent across the countries. Emotional well-being showed a robust and significant negative effect in all the estimated models, which highlights its important role in understanding the victim-offender overlap. Meanwhile, the model comparisons conducted using the \u003cem\u003ep\u003c/em\u003e-value indicated that incorporating the scales significantly improved the fit of the model in almost all cases, with the only exception of Model A in Mexico (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.7), where self-control and morality provided no additional explanatory power beyond the baseline model.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eBinomial Logistic Regression Predicting Membership in the Victim-Offender Profile (LTP)\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eSpain\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c10\" namest=\"c6\"\u003e\u003cp\u003eMexico\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eBlock A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eBlock B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eBlock A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003eBlock B\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBaseline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBaseline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModel B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eModel A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eBaseline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eModel B\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (male)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.20 (1.37\u0026ndash;3.59)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.49 (0.87\u0026ndash;2.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.02 (1.21\u0026ndash;3.43)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.01 (1.16\u0026ndash;3.52)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.43 (0.85\u0026ndash;2.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.39 (0.82\u0026ndash;2.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1.31 (0.82\u0026ndash;2.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.42 (0.88\u0026ndash;2.32)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.19 (0.96\u0026ndash;1.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.19 (0.95\u0026ndash;1.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.11 (0.89\u0026ndash;1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.12 (0.89\u0026ndash;1.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.34 (1.07\u0026ndash;1.70)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.34 (1.07\u0026ndash;1.69)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1.21 (0.98\u0026ndash;1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.19 (0.96\u0026ndash;1.47)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmotional well-being\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.75 (0.61\u0026ndash;0.92)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.70 (0.55\u0026ndash;0.87)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.71 (0.57\u0026ndash;0.88)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.74 (0.59\u0026ndash;0.92)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.72 (0.56\u0026ndash;0.91)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.71 (0.56\u0026ndash;0.91)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e0.65 (0.53\u0026ndash;0.80)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.67 (0.54\u0026ndash;0.83)***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnstructured activities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.07 (0.86\u0026ndash;1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.07 (0.86\u0026ndash;1.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.10 (0.88\u0026ndash;1.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.07 (0.85\u0026ndash;1.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.38 (1.06\u0026ndash;1.81)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.39 (1.06\u0026ndash;1.82)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1.26 (1.01\u0026ndash;1.58)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.23 (0.98\u0026ndash;1.54)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.83 (0.78\u0026ndash;0.89)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.00 (0.94\u0026ndash;1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMorality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.90 (0.81-1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.95 (0.84\u0026ndash;1.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eViolence perception sensitivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.05 (0.99\u0026ndash;1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.05 (1.00-1.10)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRevenge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.09 (1.02\u0026ndash;1.17)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.04 (0.97\u0026ndash;1.10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN (cases)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e381\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePseudo R\u0026sup2; (Nagelkerke)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep-value (vs. Baseline)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003eNote. Odds ratios are shown (CI 95%). *p\u0026thinsp;\u0026lt;\u0026thinsp;.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the last year (LYP) analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e5\u003c/span\u003e), the predictors of the victim-offender profile also showed substantial differences between the two countries. In Spain, being male remained the most robust predictor in Block A (OR\u0026thinsp;=\u0026thinsp;3.45*** y OR\u0026thinsp;=\u0026thinsp;2.49*), although its effect was not significant in either of the two Block B models, which might point to a difference between the two subsamples used as baseline. Similarly, after incorporating the psychological variables, both low self-control (OR\u0026thinsp;=\u0026thinsp;0.90**) and low morality (OR\u0026thinsp;=\u0026thinsp;0.87*) emerged as significant risk factors. In Block B, revenge (OR\u0026thinsp;=\u0026thinsp;1.19***) and perception of violence (OR\u0026thinsp;=\u0026thinsp;1.07*) were identified as key predictors of membership in the victim-offender profile.\u003c/p\u003e\u003cp\u003eIn Mexico, in contrast to the LTP analysis, neither age nor unstructured activities were significant in the last year model. Instead, low morality (OR\u0026thinsp;=\u0026thinsp;0.84*) was the only significant predictor in Block A. For Block B, none of the psychological variables (violence perception sensitivity and revenge) was statistically significant, and the model comparison confirmed that their inclusion did not improve the fit of the baseline model (p\u0026thinsp;=\u0026thinsp;0.7).\u003c/p\u003e\u003cp\u003eCross-sectionally, for this period, emotional well-being remained a salient variable in almost all the models for both countries (except in Block A in Mexico).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eBinomial Logistic Regression Predicting Membership in the Victim-Offender Profile (LYP)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"17\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c2\"\u003e\u003cp\u003eSpain\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"8\" nameend=\"c17\" namest=\"c9\"\u003e\u003cp\u003eMexico\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eBlock A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u003cp\u003eBlock B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c13\" namest=\"c9\"\u003e\u003cp\u003eBlock A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e\u003cp\u003eBlock B\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" colname=\"c2\"\u003e\u003cp\u003eBaseline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eModel A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eBaseline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eModel B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003eBaseline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e\u003cp\u003eModel A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003eBaseline\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (male)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3.65 (1.91\u0026ndash;7.35)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e2.49 (1.24\u0026ndash;5.20)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1.31 (0.70\u0026ndash;2.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1.22 (0.61\u0026ndash;2.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e1.54 (0.76\u0026ndash;3.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c14\" namest=\"c12\"\u003e\u003cp\u003e1.26 (0.60\u0026ndash;2.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u003cp\u003e1.36 (0.75\u0026ndash;2.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e1.36 (0.74\u0026ndash;2.54)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.03 (0.78\u0026ndash;1.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.01 (0.76\u0026ndash;1.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1.11 (0.85\u0026ndash;1.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1.15 (0.86\u0026ndash;1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e1.24 (0.91\u0026ndash;1.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c14\" namest=\"c12\"\u003e\u003cp\u003e1.22 (0.89\u0026ndash;1.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u003cp\u003e1.23 (0.94\u0026ndash;1.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e1.22 (0.93\u0026ndash;1.61)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmotional well-being\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.73 (0.55\u0026ndash;0.95)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.70 (0.53\u0026ndash;0.92)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.68 (0.52\u0026ndash;0.87)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e0.72 (0.55\u0026ndash;0.94)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e0.76 (0.56\u0026ndash;1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c14\" namest=\"c12\"\u003e\u003cp\u003e0.72 (0.52\u0026ndash;1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u003cp\u003e0.75 (0.58\u0026ndash;0.96)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e0.76 (0.59\u0026ndash;0.99)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnstructured activities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.38 (1.04\u0026ndash;1.83)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.37 (1.04\u0026ndash;1.83)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1.32 (1.00-1.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1.26 (0.95\u0026ndash;1.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e1.44 (1.00-2.05)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c14\" namest=\"c12\"\u003e\u003cp\u003e1.43 (0.99\u0026ndash;2.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u003cp\u003e1.25 (0.94\u0026ndash;1.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e1.25 (0.94\u0026ndash;1.65)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.90 (0.83\u0026ndash;0.97)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c14\" namest=\"c12\"\u003e\u003cp\u003e0.94 (0.86\u0026ndash;1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMorality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.87 (0.76\u0026ndash;0.99)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c14\" namest=\"c12\"\u003e\u003cp\u003e0.84 (0.71\u0026ndash;0.97)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eViolence perception sensitivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1.07 (1.00-1.16)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c14\" namest=\"c12\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e1.01 (0.95\u0026ndash;1.07)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRevenge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1.19 (1.09\u0026ndash;1.30)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c14\" namest=\"c12\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e1.03 (0.96\u0026ndash;1.12)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN (cases)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e377\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e377\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c14\" namest=\"c12\"\u003e\u003cp\u003e311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u003cp\u003e378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e378\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePseudo R\u0026sup2; (Nagelkerke)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c14\" namest=\"c12\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep-value (vs. Baseline)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c14\" namest=\"c12\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"17\" nameend=\"c17\" namest=\"c1\"\u003e\u003cp\u003eNote. Odds ratios are shown (CI 95%). *p\u0026thinsp;\u0026lt;\u0026thinsp;.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA comparison of the results for both timeframes reveals three main findings. First, emotional well-being is the most consistent variable in both countries, being significant in the vast majority of the analyses performed, for both LTP and LYP. Second, the explanatory power of the variables differs by country. In Spain, being male, low self-control and revenge are robust predictors in both periods. In contrast, in Mexico, variables such as age and unstructured activities are key in predicting membership in the profile over the lifetime but lose their predictive power when the analysis is only for the last year, where low morality appears to play a more notable role. Finally, the models that include psychological scales tend to have a greater predictive power in Spain (maximum R\u0026sup2; of 0.22) compared to Mexico (maximum R\u0026sup2; of 0.12), suggesting that the variables included are particularly relevant to understanding the victim-offender profile in the Spanish sample.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results of the present study open several avenues for discussion on the phenomenon of the overlap among adolescents, particularly in relation to its nature and its implications for intervention and prevention in this profile of youth, which are addressed below.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eThe configuration of the overlap and the influence of context\u003c/h2\u003e\u003cp\u003eThe results of the LCA provide important findings on the nature of the overlap. The first and most compelling finding is the consistent identification of a two-class solution. The adolescents in all four subsamples are best grouped into two profiles that distinguish between those with minimal contact with crime and those who have experienced it in both roles. Pure profiles, that is, victims only or offenders only, could not be identified, which suggests strong empirical support for the existence of the victim-offender overlap, even across contexts with notably different levels of security and rule of law (Berg \u0026amp; Schreck, 2022; Jennings et al., 2012). This does not mean there are no individuals in the sample that are only victims or only offenders, but rather that these cases do not form a sufficiently numerous or homogeneous profile, with consistent response patterns, to constitute a distinct latent class. The two-class model was thus the most parsimonious solution for both Mexican and Spanish adolescents, and for both lifetime and last-year experience of crime.\u003c/p\u003e\u003cp\u003eSecond, as we used a diverse series of experiences of victimization and offending and not general indicators of delinquent behavior (Berg \u0026amp; Mulford, 2017), we were able to establish that some experiences have a greater impact on the overlap than others. Cyber hate is the most frequent victimization experience in the victim-offender profile across both periods and countries, even being more prevalent than robbery. This suggests that the discriminatory dynamics of aggression in digital environments are not an innocuous or isolated phenomenon, but rather are interconnected with crime and victimization in the physical world, arguably acting as an indicator of cross-sectional risk. In fact, hate crime victimization has been shown to have more serious effects on physical, mental and behavioral health than other offenses (Mellgren et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), leading to loss of social identification and mortification, although their impact upon each individual is different (Funnell, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurthermore, differences between Spain and Mexico were also found in the victim-offender profiles, revealing that their configuration is not universal but is instead shaped by contextual influences. In Spain, compared to Mexico, this profile is characterized primarily by the commission of minor offenses typical of youth populations, such as shoplifting, graffiti, or group fights. This is linked to the greater engagement of Spanish adolescents in unstructured activities in public spaces, as has been observed in various other countries. (Buil et al, 2025). Meanwhile, in Mexico, compared to Spain, the profile is defined by higher victimization and greater probability of carrying weapons. These results add strength to the idea that, in contexts of high insecurity and violence, as is the case of Mexico, young people\u0026rsquo;s experience of delinquency is more linked to their exposure to victimization and the adoption of protection and survival strategies, such as carrying weapons (Kopf \u0026amp; Gresham, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Kemal et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lizotte et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Moss et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Oliphant et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Simon et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), or to lower involvement in unstructured activities, as suggested by our results. According to the 2025 National Victimization and Public Security Perception Survey (ENVIPE), in 2024, 63.1% of respondents reported having stopped allowing minors in their households to go out alone for fear of their being victims of crime.\u003c/p\u003e\u003cp\u003eFinally, it is worth highlighting that the prevalences of the latent profiles are notably similar across both countries when comparing data for the last year with those for lifetime experiences, although the prevalence of the victim-offender profile is lower in the latter period. This finding supports the idea that the overlap between victimization and offending is linked to similar experiences that accumulate throughout adolescents\u0026rsquo; lives. Further research is thus needed to elucidate the temporal sequencing of these different experiences and the causal relationships among them. (Birbeck et al, 2023, Berg \u0026amp; Mulford, 2017).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003ePredictors of the victim-offender profile and implications for prevention\u003c/h2\u003e\u003cp\u003eThe results of the regression analyses reveal a series of variables that are capable of predicting the risk of developing a victim-offender profile. These variables are not the same in the two countries, which suggests that the context provides key insights into adolescents\u0026rsquo; experience with crime. This is discussed below.\u003c/p\u003e\u003cp\u003eThe results of comparing the lifetime and last-year models differ across the two countries. In the case of Spain, the variables associated with adolescents in the victim-offender profile in both models are being male, experiencing emotional distress, lower self-control, and displaying revenge-oriented attitudes. The last-year model reveals further factors, namely, greater involvement in unstructured activities, low morality, and violence perception sensitivity, suggesting that although the profile develops and consolidates over time around emotional distress, during adolescence, situational and attitudinal variables play a greater role in explaining these youths\u0026rsquo; experiences with crime.\u003c/p\u003e\u003cp\u003eIn the Spanish case, membership in the victim-offender profile appears to be linked to a greater individual propensity toward antisocial behavior and attitudes that favor such conduct. This is a highly salient finding, suggesting that Situational Action Theory (SAT) (Wikstr\u0026ouml;m, 2014; Wikstr\u0026ouml;m et al., 2013) might explain not only delinquent behavior but also the victim-offender phenomenon. Although childhood maltreatment has been associated with lower self-control, lower morality, and greater exposure to criminogenic contexts (Doelman et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), very few studies have drawn on SAT to explore the relationship between victimization and offending.\u003c/p\u003e\u003cp\u003eFurthermore, boys are at greater risk of belonging to this profile. In the lifetime model, this higher risk among boys can be explained by their greater individual propensity. In this sense, our results align with previous studies showing that the gender gap in adolescence can be explained, at least in part, by differences in individual propensity, in agreement with Situational Action Theory (SAT) (Hirtenlehner \u0026amp; Treiber, 2017). However, in the model for the last year, boys show a higher risk of belonging to this profile independently of opportunity and individual propensity (low self-control and low morality), as measured in the ISRD. This finding suggests there remains a need for further exploration of the gender gap in relation to both the victim-offender overlap and SAT, which has hitherto paid scant attention to questions of gender (see, for example, Hardie \u0026amp; Rose, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eIn Spain, then, adolescents that have been both victims and offenders in the last year are typically boys that exhibit high individual propensity and high participation in unstructured activities, but also greater sensitivity to violence and pro-revenge attitudes. Although further research is needed, these findings are consistent with previous studies showing that different experiences of victimization in youth contribute not only to moral disengagement (Luo \u0026amp; Bussey, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), but also to a greater sensitivity to violence.\u003c/p\u003e\u003cp\u003eIn this regard, the combination of a high level of pro-revenge attitudes and high sensitivity to violence suggest that the more recent profile might be explained by a more expressive-affective than instrumental pattern of victimization and offending (Anderson \u0026amp; Huesmann, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). This is consistent with the type of less serious offenses that characterize this profile and the type of crimes of which they have been victim, that is, cyber hate. Hate speech causes multiple forms of harm; however, as argued by D\u0026iacute;az-Faes and Pereda (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), online hate crimes are not simply common offenses with a moral aggravating factor, but rather complex manifestations of identity, power, and social structure, with the impact going beyond the individual to group identity. That this profile is predominantly observed among boys may suggest that the acts of harm they experience and perpetrate are closely linked to the social construct of masculinity. In this regard, several studies have pointed out that online violence is a practice that reinforces traditional gender roles (Cosma et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similarly, research on cyberbullying has highlighted the importance of identity aspects such as sexual orientation and gender expression for understanding and preventing different forms of harassment on social networks (Navarro, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Ojeda et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the case of Mexico, albeit with a lower predictive capacity, older age, emotional distress, engagement in unstructured activities, and violence perception sensitivity appear to play a role in the lifetime model. This suggests that, in Mexico, the phenomenon depends on experiences accumulated during adolescence that do not occur only as a result of greater individual propensity or higher participation in unstructured activities, although these remain risk factors. Instead, it may be the reflection of a highly insecure social environment that facilitates such experiences over the lifetime, even among individuals that present low individual propensity. In this sense, it seems likely that such adolescents are exposed to direct or indirect victimization, which heightens their sensitivity to violence and leads them to adopt self-protective behaviors that are typically identified as antisocial (such as carrying weapons).\u003c/p\u003e\u003cp\u003eThese variables do not remain significant, however, in the last-year model, with the exception of emotional distress, which, together with low morality, are the only distinguishing factors at this stage. The model thus combines both situational and individual variables, which, however, change over the course of life.\u003c/p\u003e\u003cp\u003eIn short, these findings help broaden understanding of the victim-offender overlap. First, the variables employed in this study more effectively predict the victim-offender profile among Spanish adolescents than among their Mexican counterparts. This confirms the need to delve deeper into the nature of this profile in countries of the Global South, given the bias of theoretical frameworks developed primarily in the Global North (Carrington et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In our view, these frameworks fail to properly account for the contextual and cultural factors that arguably play a significant role in this phenomenon, and are thus insufficiently robust to enhance understanding of this criminological issue.\u003c/p\u003e\u003cp\u003eIn addition, it is worth noting that the variables associated with the most prominent theoretical explanations of the victim-offender overlap fail to generate models capable of explaining the variability between the low-risk and overlap groups, with R\u0026sup2; values ranging between 0.6% and 22%. Further research is therefore needed to introduce novel explanatory hypotheses that help advance our understanding of this profile in young people, about whom there is still much to be understood. Indeed, as has been suggested, it is arguably time to expand criminological theory and embrace alternative perspectives (Berg \u0026amp; Schreck, 2022; Birbeck et al., 2023). In this vein, some authors have interpreted the victim-offender overlap as the result of the moral harm brought about by adverse experiences. This moral harm might successfully explain the trajectory from victimization to offending not through trauma itself and its emotional impact, but instead through the ethical and relational crisis that disrupts cognitions, emotions, and social bonds. Antisocial and violent behavior would thus be the consequence of morally injured young people that have lost their compass (Ava \u0026amp; Kerig, 2025).\u003c/p\u003e\u003cp\u003eIn this regard, an important finding is that emotional distress is the variable most consistently associated with the victim-offender profile, in both countries under study and across both lifetime and last-year models. We cannot know whether the emotional distress is prior these experiences, and is thus a key factor that might explain this profile as adolescents whose vulnerability and risk situation expose them to victimization or offending (McLachlan, 2025) or, conversely, whether it is a consequence of having experienced such situations (Houbre et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; McLoughlin et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In any event, emotional distress should be addressed in the prevention of antisocial behavior and in interventions with these young people, regardless of whether intervention is a response to victimization or offending. In this sense, both child protection systems and the juvenile justice system should attend to the harm endured by adolescents beyond their specific experience with crime.\u003c/p\u003e\u003cp\u003eOur results also suggest that prevention strategies should be contextualized. In both Spain and Mexico, attention should clearly be given to the interconnection between adolescents\u0026rsquo; online and offline lives. However, in Spain, interventions could focus on the identity-related harm that young people perceive and inflict, particularly as regards experiences they interpret as grievances that justify revenge or diminish their sensitivity to violence, especially among boys. In this sense, harnessing a gender perspective may be particularly useful, as it allows us to understand the identity-related harm both experienced and inflicted during a crucial stage of development, and how differential socialization influences self-control and morality. In Mexico, although the results appear to confirm that reducing exposure to risk is both necessary and effective, coinciding with the arguments of Mulford et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), this presents a paradox: limiting exposure to public spaces to protect adolescents, may, at the same time, undermine their right to development and participation in the community. This constitutes a complex challenge for public policy and for the adults involved in adolescents\u0026rsquo; development.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eConsiderations on the participant recruitment method\u003c/h2\u003e\u003cp\u003eFinally, some considerations should be made on the method used to recruit our participants that might have affected the scope of the results. First, the analyses were conducted using two convenience samples; therefore, it is important to clarify that no conclusions can be drawn regarding the prevalence of the experiences reported in either country. The aim of this study is purely exploratory; we sought to broaden the understanding of the victim-offender overlap by means of a comparative analysis of two cultural contexts with markedly different levels of exposure to violence.\u003c/p\u003e\u003cp\u003eFurthermore, it is necessary to consider the specific biases associated with the method used, namely panelist recruitment. Beyond the fatigue bias that may participants might experience after participation in multiple surveys, the literature has identified that the use of panelists introduces a bias related to socioeconomic status, as participation tends to be more prevalent among young people with greater leisure time and economic resources (Murray \u0026amp; Xie, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Nonetheless, this type of non-probabilistic sampling is preferable to the use of river sampling, which entails an even greater coverage bias and offers less control over who participates (Lehdonvirta et al., 2020).\u003c/p\u003e\u003cp\u003eAs discussed at the outset of this study, we employed two samples of panelists from the same company, \u003cem\u003eMetroscopia\u003c/em\u003e, which operates in both countries, using the same sample stratification method. Nevertheless, it is important to consider that although the socioeconomic bias of online panels is structural in nature, it is also influenced by national context, particularly among younger participants, since their availability is more strongly impacted by economic (Lehdonvirta et al., 2020) and digital factors (Blom et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In this sense, the digital divide is larger in Mexico\u003csup\u003e4\u003c/sup\u003e, and thus the Mexican sample may be affected by an overrepresentation of adolescents of a socioeconomic status with greater access to Internet, thereby neglecting young people from more vulnerable environments with more extensive exposure to crime.\u003c/p\u003e\u003cp\u003eFinally, it is worth noting that the literature has identified an overrepresentation of online victimization experiences in such samples (Oksanen et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Nonetheless, given that the focus of this study is not on examining the prevalence of the behaviors experienced, the impact of this bias on the analysis is reduced and may even be considered a \u0026ldquo;useful bias,\u0026rdquo; which allows us to explore how victims and offenders interact within groups of young people with greater exposure to the Internet (Lehdonvirta et al., 2020).\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA.E.G.E. was responsible for data collection in Mexico, data curation, and the statistical analysis for this study. E.F.M. was responsible for data collection in Spain. E.F.M. wrote the theoretical framework; R.B.G. and A.E.G.E. expanded it. All three authors made joint decisions on variables and proposed models, discussed the results, and participated in writing and revising the discussion.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAnderson C. A., \u0026amp; Huesmann L. R. (2003). Human aggression: A social-cognitive view. In Hogg M.A., Cooper J. (Eds.), \u003cem\u003eThe Sage Handbook of Social Psychology\u003c/em\u003e (pp. 259\u0026ndash;287). Sage.\u003c/li\u003e\n\u003cli\u003eBaek, H., Han, S., \u0026amp; Gordon, Q. (2021). Factors that influence trust in the police in Mexico. \u003cem\u003eInternational Journal of Comparative and Applied Criminal Justice\u003c/em\u003e, 46(4), 407\u0026ndash;422. https://doi.org/10.1080/01924036.2021.1998917\u003c/li\u003e\n\u003cli\u003eBenson, M. L. (2013). \u003cem\u003eCrime and the Life Course. An Introduction\u003c/em\u003e. Routledge.\u003c/li\u003e\n\u003cli\u003eBlom, A. G., Herzing, J. M. E., Cornesse, C., Sakshaug, J. 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Miller (Ed.), \u003cem\u003eThe Encyclopedia of Theoretical Criminology. \u003c/em\u003eWiley-Blackwell.\u003c/li\u003e\n\u003cli\u003eWickham H, Miller E, Smith D (2023). \u003cem\u003ehaven: Import and Export \u0026apos;SPSS\u0026apos;, \u0026apos;Stata\u0026apos; and \u0026apos;SAS\u0026apos; Files\u003c/em\u003e. https://CRAN.R-project.org/package=haven\u003c/li\u003e\n\u003cli\u003eWickham H., Fran\u0026ccedil;ois, R., Henry L., M\u0026uuml;ller K., Vaughan, D. (2023). \u003cem\u003edplyr: A Grammar of Data Manipulation\u003c/em\u003e. https://CRAN.R-project.org/package=dplyr.\u003c/li\u003e\n\u003cli\u003eWorld Justice Project. (2024). WJP Rule of Law Index 2024. Recuperado de https://worldjusticeproject.org/rule-of-law-index/\u003c/li\u003e\n\u003cli\u003eZeileis, A. \u0026amp; Hothorn, T. (2002). Diagnostic Checking in Regression Relationships. \u003cem\u003eR News\u003c/em\u003e, 2(3), 7-10. https://journal.r-project.org/articles/RN-2002-018/ \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e In total, 33 cases (1.8% of the original sample) were excluded from the analysis, 10 from Spain and 23 from Mexico. The exclusion criterion was a control variable identifying the number of implausible responses provided by each participant. Of the cases excluded, 29 presented one implausible response, three presented two, and one case showed three.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e A novel aspect of the present study is the individual use of the items that comprise the measures of victimization and offending, rather than grouping them, as has typically been done in previous research. This approach responds not only to the catalogue of offenses included in this survey being relatively limited, but also to our consideration that the integration of items should not be assumed without prior testing, particularly when evaluating the overlap between victimization and offending, rather than the diversification of the behaviors experienced.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e In the regression analyses, only comparisons between male and female participants were conducted; therefore, the cases in which participants identified as non-binary were not included in these analyses.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e According to 2024 data from INEGI, in Mexico, 83,1% of the population aged six years and older are frequent internet users, whereas in Spain, according to data from the National Statistics Institute, 95,8% of individuals aged sixteen years and older report frequent use. In Mexico, only 41,8% of households have a computer (16,3% in rural areas), compared with 83% in Spain (70,9% in rural areas, according to the National Observatory for Technology and Society).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Victim-Offender overlap, Latent Class Analysis, Adolescence, Victimization, Delinquency, Comparative analysis","lastPublishedDoi":"10.21203/rs.3.rs-8002022/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8002022/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the configuration and contextual correlates of the victim-offender overlap in adolescents. Using data from the International Self-Report Delinquency Study (ISRD-4), we compare this phenomenon across two countries with markedly contrasting socio-legal and security environments: Mexico and Spain, in two periods of reference: lifetime and last year. We apply Latent Class Analysis (LCA) to identify adolescent risk profiles, consistently finding two distinct classes across both nations and periods: a low-risk group and a victim-offender class. Subsequent regression analyses reveal that emotional well-being is a consistent universal predictor of membership to this profile, along with country-specific variables that exhibit differential effects. These findings highlight the need for culturally and contextually sensitive prevention programs to address the complex nature of this overlap in high-violence versus low-violence settings.\u003c/p\u003e","manuscriptTitle":"The Intersection of Victimization and Delinquency in Adolescents: Comparative Evidence from Mexico and Spain","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-14 06:45:36","doi":"10.21203/rs.3.rs-8002022/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"01b4f86b-cd6c-4227-98a9-5bffce1abb21","owner":[],"postedDate":"November 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T16:06:55+00:00","versionOfRecord":{"articleIdentity":"rs-8002022","link":"https://doi.org/10.1007/s43576-026-00213-8","journal":{"identity":"international-criminology","isVorOnly":false,"title":"International Criminology"},"publishedOn":"2026-04-10 15:58:11","publishedOnDateReadable":"April 10th, 2026"},"versionCreatedAt":"2025-11-14 06:45:36","video":"","vorDoi":"10.1007/s43576-026-00213-8","vorDoiUrl":"https://doi.org/10.1007/s43576-026-00213-8","workflowStages":[]},"version":"v1","identity":"rs-8002022","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8002022","identity":"rs-8002022","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}