Next Article in Journal
Self-Compassion as a Mediator of the Longitudinal Link Between Parent and Adolescent Depressive Symptoms
Previous Article in Journal
Examining the Associations Between Parental Distress in Caregivers of Children Accessing Outpatient Psychiatry with Caregiver Ratings of Child Symptom Severity, Family Functioning, Financial Hardship, Social Support and Self-Care
Previous Article in Special Issue
Risk Factors for Teen Dating Violence Among Sexual and Gender Minority Youths: A Systematic Review
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Adolescents
Volume 5
Issue 3
10.3390/adolescents5030044
adolescents-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Family Conflict and Gun Carrying in Adolescence: Multilevel Analysis of Household and Neighborhood Effects in Los Angeles County

by
Kathryn M. Barker
1,*,
Devin Gregoire
2,
Naomi Wilcox
3,
Maryam Izadshenas
3 and
Anita Raj
4,5
1
Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
2
Division of General Internal Medicine and Geriatrics, Oregon Health & Science University, Portland, OR 97239, USA
3
Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA 92093, USA
4
Newcomb Institute, Tulane University, New Orleans, LA 70118, USA
5
Celia Scott Weatherhead School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA
*
Author to whom correspondence should be addressed.
Adolescents 2025, 5(3), 44; https://doi.org/10.3390/adolescents5030044
Submission received: 31 May 2025 / Revised: 29 July 2025 / Accepted: 1 August 2025 / Published: 22 August 2025
(This article belongs to the Special Issue Understanding and Addressing Adolescent Aggression and Interpersonal Violence)
Versions Notes

Abstract

Background: Firearm-related injuries are the leading cause of death among children and adolescents (ages 1 to 19 years) in the United States. Access to and carrying firearms are key risk factors for violence and adolescent firearm use. This study examines the association between family conflict and adolescent gun carrying in Los Angeles County, and the extent to which household and neighborhood contexts contribute to adolescent gun carrying. Methods: We use cross-sectional multilevel data from adolescents ages 12–17 years in the Los Angeles Family and Neighborhood Study, conducted in 2002, to fit a series of generalized linear mixed models to examine the association between family conflict (scale range: 0–2) and adolescent gun carrying. Models include random effects to examine the contributions of household and neighborhood contexts on the outcome measure. Results: After controlling for demographic characteristics, gang involvement, substance use, and household and neighborhood contexts, adolescent experiences of family conflict remain positively associated with adolescent gun carrying behavior (OR = 3.45, p = 0.043). Random effects estimates indicate that a relatively large amount of variation in adolescent gun carrying is explained by household and neighborhood contexts: 23% and 24%, respectively. Conclusions: Multilevel family and community-level interventions, with an emphasis on family violence, are necessary components of prevention strategies to reduce high rates of firearm-related mortality among US adolescents.

1. Introduction

Firearm-related injuries are the leading cause of death among children and adolescents (ages 1 to 19 years) in the United States (US) [1], with younger children disproportionately affected by firearm homicides and suicides relative to the general US population [1,2]. Data from the US Centers on Disease Control and Prevention indicate that from 2019 to 2023, the firearm death rate among children and adolescents in the United States increased by 46% (from 2.4 to 3.5 per 100,000) [3]. Although data on exposure to gun violence among the youth is generally limited, prior work indicates that nonfatal firearm injuries occur at rates two to four times more often than firearm mortalities [4,5]. Access to and carrying firearms are key risk factors for adolescent firearm violence use [6,7]. Although US federal law (18 USC § 922 (b)) generally prohibits the possession of handguns by any person under the age of 18, the most recent evidence from the U.S. Centers for Disease Control and Prevention (CDC) indicates that in 2021, 3.5% of high school students carried firearms [8].
Despite firearm-related morbidity and mortality being a major public health concern in the US and for children and adolescents specifically, there remains a limited understanding of why adolescents carry firearms [9]. A small study (n = 141) from the Southeastern US (conducted 2018–2019) indicated that the most frequently endorsed factors associated with why young people (ages 18–22) carried a gun were to protect friends (65% of sample) and family (60%) and to feel safe (55%) [10]. A 2019 scoping review examining the motives for adolescent firearm carriage indicates that the majority of reviewed articles (11 of 13) cited a perceived need for protection/self-defense as the primary motive underlying gun carrying (though none of the quantitative studies characterized the reasons underlying this perceived need) [11]. A study using data from the nationally representative Youth Risk Behavior Survey (YRBS) highlights the importance of the vulnerability/self-protection theory in adolescent gun carrying behavior [9]. This theory asserts that individuals who have been victimized may turn to weapon carrying as a means of self-protection [12,13]. This theory has been applied in work examining the role of neighborhood violence on gun carrying behavior [9]. Studies examining the role of neighborhood characteristics—including aspects of social disorganization [14]—have highlighted a link between feeling unsafe, social and physical disorder in a neighborhood, and an increased likelihood of firearm carrying [15]. Sociocultural theories of violence take an ecological approach to understanding the ways in which these macro-level forces interact with exo- and micro-level forces to explain the causes of violence [16,17]. Studies that have examined contextual factors are however older and limited to less generalizable samples of youth, such as those involved in the criminal justice system or from racial/ethnic minority communities [18,19].
Conflict and violence within families is a risk factor for victimization from firearm violence and for community violence, concerns linked to gun carrying for adolescents [20]. This is supported by a number of sociocultural theories: social learning theory, social exchange theory, and the social norms theory and the related subculture-of-violence theory. Bandura’s social learning theory posits that violence is a learned behavior, with adolescents learning acceptable or appropriate behavior from within their family unit [21,22]. Social exchange theory asserts that an individual’s behavior is influenced by the amount and types of rewards and punishments one anticipates [23]. If there is a perceived gain to using violence, an individual will use violence to achieve that gain. Finally, social norms theory posits that social norms—informal rules of behavior that dictate what is acceptable within a given social context—influence individual behavior [24]. This theory has been increasingly applied in health promotion strategies [25]. The related subculture-of-violence theory posits that individuals within societal subcultures learn and rely on the norms and values of that subculture through socialization [26]. If violence is normative within a subculture, individuals and social systems within that subculture will be socialized to use violence [26]. Despite the theoretical importance linking family violence and the cultures of violence within families to adolescent violence behaviors, empirical evidence examining the link between family violence and gun carrying is relatively limited, though the studies that do exist indicate an association between family conflict/violence and gun carrying among adolescents. Meeker et al. (2021) found in their study with a sample of high school students that a history of adverse childhood events, including verbal/physical abuse and altercations and the absence of family support, is associated with adolescent gun carrying [27]. A study with adolescents in Minnesota, similarly found that experiencing or witnessing physical violence in the family was significantly associated with gun-carrying in both boys and girls [28]. A longitudinal study with 426 high-risk youth in Flint, Michigan found that family violence and family gun violence predicted gun carrying among children ages 7 to 18 years of age, but not at older ages (18–24 years) [29]. One study with 141 young adults from economically marginalized communities in the Southeastern US found that a lack of family conflict was a significant protective factor against likelihood of gun carriage [10]. None of the studies use a generalizable sample. Few of these studies account for household and neighborhood effects in their examination, despite recognition of the importance of these contexts in both theoretical and empirical work [20,30].
To respond to these gaps, we examine family conflict and firearm carrying with a population representative sample of adolescents in Los Angeles County, which includes a racially/ethnically as well as economically diverse sample drawn from densely populated urban areas to more rural areas. The data come from a multilevel longitudinal study designed to examine how families and neighborhoods jointly influence child wellbeing in Los Angeles County [31]. The data have a hierarchical design, which is ideal for analyzing contextual effects. We can thus consider in our analysis the association between family conflict and firearm carrying for adolescents, as well as the degree to which household and neighborhood contexts contribute to variation in our outcome. This approach allows for an ecological analysis of family and community contributors to adolescent firearm carrying behavior, aligned with theoretical understanding of the multilevel risks that may exist for adolescent firearm use which remain understudied in firearm research [20,30].

2. Methods

2.1. Data

Data were obtained from Wave 1 of the Los Angeles Family and Neighborhood Survey (LAFANS), a multi-stage stratified cluster sample of data from 3200 households in 65 neighborhoods across diverse Los Angeles County [31]. Wave 1 was collected from April 2000 to January 2002 and Wave 2 was collected from August 2006 to December 2008 [31]. Although Wave 2 data are more recent, the sample size (n = 1900 households) was too small to support analyses of the relatively rare outcome measure. LAFANS was designed to answer how neighborhoods affect a variety of outcomes, including children’s development and wellbeing, and stress and health among children and adults [32]. Fundamental to the design of LAFANS was the stratified sampling approach that allowed for an oversampling of poor and very poor census tracts, with poor census tracts defined as being in the 60–89th percentiles of the poverty distribution, and very poor tracts defined as being in the top 10% of the poverty distribution. The remaining non-poor stratum corresponded with tracts in the bottom 60% of the poverty distribution. Estimates for poverty distributions were derived from the Los Angeles County’s Urban Research Division with state and county data from 1997 [32]. The 65 tracts were approximately balanced across these 3 strata, with 20 tracts allocated to the poor and very poor strata, respectively, and 25 tracts being designated to the non-poor stratum [32].
Given the multilevel design of LAFANS, sampling was conducted at the tract level, followed by the household/family level, and then the individual level. In each tract, 50 households were randomly chosen from lists of all possible dwelling units per tract. One randomly sampled adult (RSA) was selected from each household, and in households with children, one randomly sampled child (RSC) was selected. If the RSC had one or more siblings under the age of 18 from the same biological/adoptive mother, one of them was randomly selected for interview (designated as SIB) [32]. Given that statistical models incorporate household as a random effect, and therefore explain clustering or similarities in outcome measures at the household level, the present analyses combine the two adolescent respondent types. As such, no further distinction is made in analyses, results, or discussion between RSC and SIB.
In households with children, the mother of the RSC was included for interview and designated as the primary caregiver (PCG). Often, the RSA and PCG were the same individual in each household. In-person interviews were conducted with respondents using computer-assisted interviews in English and Spanish, depending on the language preferred by the respondent [32]. LAFANS interviewers explained the privacy protocols established to protect respondent identities and the confidentiality of their responses, which likely encouraged respondents to complete the survey and to provide honest answers to sensitive questions such as those about nativity, citizenship, and legal status. The research design of LAFANS made these data well-suited for the study of family violence and gun carrying within a neighborhood context [32].

2.2. Sample

Complete case analysis was conducted. The first wave of LAFANS data contained responses from 1454 children. Only older adolescents (ages 12–17) in the study were asked more sensitive questions regarding gun carrying and family conflict, among other sensitive material. As such, adolescents ages 8–11 years (n = 563) are not included in the present analysis, resulting in a possible sample of 891. One adolescent (n = 1) refused to answer the question used to assess the outcome measure of gun carrying, bringing the sample to 890. Five adolescents (n = 5) gave only responses of “don’t know” or “refused” across all six questions regarding family conflict and were removed, reducing the analytic sample size to 885. Two adolescents (n = 2) were missing responses for one of the additional covariates in the model, reducing the complete case sample to 883. Two adolescents (n = 2) were missing responses to all additional questions asked of those children ages 12–17, further reducing the sample to 881. There were 12 eligible adolescents with missing observations on the family income variable. To address missingness on this variable, we used multivariate imputation by chained equations algorithm in R via the mice package (version 3.15.0) [33]. The number of imputations performed was five and we utilized the predictive mean matching method as it is robust to non-normality and preserves the original distribution of the income variable by imputing only observed values. After accounting for missingness and imputation, the final analytic sample size was 881 adolescents.

2.3. Measures

The outcome measure is firearm carrying behavior. This behavior was assessed in LAFANS by asking adolescents aged 12–17 years the following question: “In the past 30 days, did you ever carry a hand gun?” Binary responses are coded as yes (1) and no (0).
The primary exposure variable is Family Conflict (FC). This measure was created using responses to six items regarding how the adolescent and their family get along and settle arguments: 1. People in my family fight a lot; 2. People in my family hardly ever lose their tempers [R]; 3. People in my family sometimes get so angry they throw things; 4. People in my family always calmly discuss problems [R]; 5. People in my family often say mean things to each other; 6. People in my family sometimes hit each other. Adolescent respondents used a three-point Likert scale (True (2); Sometimes True (1); Not True (0)) to respond to the items. Items noted with “[R]” were reverse-coded. Factor analysis on responses to the six questions revealed acceptable internal consistency (Cronbach’s alpha = 0.65); removal of items resulted in lower values of the Cronbach’s alpha. As carried out in previous research with LAFANS data on family stress [34], the final FC exposure variable was generated by averaging responses across all six survey items to create a mean FC score, ranging from 0 to 2, with a score of 0 representing an absence of self-reported family conflict, and a score of 2 representing self-reported frequent experience of family violence and unhealthy family conflict resolution. Respondents who answered at least one of the six questions had a mean FC score calculated. Questions that received answers of “refused” or “don’t know” were omitted from the mean FC score calculation, with the score then being calculated over the remaining responses.
To incorporate contextual effects above that of the individual level for gun carrying, a multilevel analytic approach was taken by employing random intercepts. Generalized linear mixed models (GLMMs) were constructed and included random intercepts for both household and neighborhood in the form of household ID (HHID) and census tract ID (TRACTX).
Final adjusted models include the following demographic characteristics: age in years as an integer, self-reported biological sex (male/female), and race/ethnicity (Latinx, non-Hispanic White, American Indian, Asian, Black, and Pacific Islander with the latter four categories collapsed into ‘Other’ for regression analyses due to small cell sizes), and socioeconomic status, measured as family income (thousands of dollars). Family income functions as a measure for family SES, and is an imputed variable developed by Marianne Bitler, Fuan-Yue Kung, and Christine Peterson at the RAND Corporation [35]. Family income is a sum of all wage/salary earnings, transfer income, and assets in the family unit. Wage/salary earnings apply to the RSA, spouse, and children in the household when applicable. Transfer income is defined as any income from the following: child support, unemployment, worker’s compensation, social security, supplemental security, food stamps, public assistance, house/energy assistance, foster care, Veterans Affairs, pension/trust, and alimony. Evaluations of assets include income from properties, businesses, retirement, stocks and bonds, interest from checking/savings/money market accounts, and any other miscellaneous income. The full family income imputation process is described under RAND version 1 restricted data access file in the income and assets imputation documentation [35].
Two known covariates are also included in final models: history of substance use [36] and gang involvement [37]. The substance use measure assesses ever-use of cigarettes, alcohol, marijuana, and/or hard drugs as assessed via the following survey items: “have you ever smoked a cigarette?”, “have you ever had a drink of alcohol?”, “have you ever used marijuana?” and “ever used drugs like crack, cocaine, etc?” (coded as a binary variable—yes to any substance use (1) or no history of substance use (0); and direct or indirect gang involvement (either the adolescents themselves are in a gang and/or a friend or family member is in a gang), assessed via responses to “have you ever belonged to a gang?” and “family or friends belong to a gang?” (coded as a binary variable).

2.4. Statistical Approach

A series of generalized linear mixed models (GLMM) with a logit link were used to examine the extent to which household and neighborhood contexts contribute to adolescent gun carrying behavior, and the association between exposure to family violence and firearm carrying in adolescence. Models including household and neighborhood levels together as nested random effects failed to converge, resulting in a need to generate separate models for household and neighborhood level random effects. Three sets of models are generated, with the first set (Models 1 and 2) being null intercept-only models with single random effects of household and neighborhood, respectively. Models 3 and 4 retain their respective random effects structure and include the family conflict score as an individual-level predictor. In order to determine whether sex should be best understood as a covariate or a moderator variable, we also include an interaction term in the model along with family conflict. The final set of models (Models 5 and 6) are fully adjusted models, accounting for demographic characteristics (age, sex, race, family socioeconomic status) and behavioral covariates (substance use and gang involvement). Equations for the fully adjusted household and neighborhood GLMM are provided in Appendix A.
In addition to generating odds ratios and corresponding 95% confidence intervals, there is also substantive interest in the intraclass correlation coefficient (ICC) to understand the proportion of the total variance in adolescent gun carrying can that can be attributed to variation within-clusters [38]. Specifically, the ICC illustrates the amount of variation in gun carrying which may be explained by the contextual levels of households and neighborhoods, or, conversely, the individual-level factors.
All analyses were conducted in R (v4.3.0) [39] via RStudio (v2023.3.1.446) [40] using the GLMMadaptive package (v0.8-8) [41].

3. Results

Descriptive statistics for the final sample are displayed in Table 1. The median age of adolescents was 14 years, and sex was evenly distributed as 51.0% male and 49.0% female. The sample was predominantly Latinx (54.9%), followed by White (21.3%), Black (9.8%), Asian (6.4%), Multiracial-Identifying (6.2%), Pacific Islander (0.7%), and American Indian (0.7%). A small proportion (2.2%) of adolescents reported carrying a firearm in the last 30 days. Overall, the mean Family Conflict score was 0.641 (maximum possible score of 2). A small proportion (2.5%) of adolescents reported gang involvement while nearly half of the adolescents (44.9%) reported a history of ever substance use. The median family income was USD 32,000. The adolescents in the sample were situated within 736 households and 65 neighborhoods.
Table 2 displays results from null multilevel models for household and neighborhood level contexts. As shown, household and neighborhood contexts accounted for 23% and 24% of the total variation in gun carrying, respectively (ICC HHID = 0.23; ICC TRACTX = 0.24). Models 3 and 4 add the primary exposure variable, family conflict. As shown in Table 3, after accounting for household context, a one-point increase in family conflict scale score is associated with nearly 5 times the odds of gun carrying in adolescence (OR = 4.75, 95% CI 1.74–12.94). These odds are roughly the same after accounting for neighborhood context (OR = 4.62, 95% CI 1.69–12.59). Despite the addition of the individual-level predictor (family conflict), the amount of variance explained by household and neighborhood contexts remains the same from Models 1 and 2 (ICC HHID = 0.23; ICC TRACTX = 0.24). Results from the models that include sex as an interaction term indicate that sex (male/female) does not modify the association between family conflict and gun carrying (p-values of 0.944 and 0.977, respectively—see Supplemental Table S1). As such, sex treated as a covariate in subsequent models.
To examine whether this association between gun carrying and family conflict holds after adjusting for demographic characteristics and known covariates, as shown in Table 4, Models 5 and 6 add age, sex, race, family SES, substance use, and gang involvement to the GLMM.
As expected, gang membership is highly correlated with gun carrying in both the household and neighborhood models (OR = 14.84, 95% CI 3.76–58.60 and OR = 14.93, 95% CI 3.78–59.05), respectively. Adolescent self-report of ever using alcohol or drugs is also highly correlated with gun carrying in household and neighborhood models (OR = 6.72, 95% CI 1.38–32.66 and OR = 6.59, 95% CI 1.36–31.97), respectively. However, after accounting for these variables and the demographic characteristics, the association between family conflict and adolescent gun carrying remains statistically significant in the household model and has marginal statistical significance in the neighborhood model: OR = 3.45 (p = 0.043) and OR = 3.23 (p = 0.053), respectively. Thus, after accounting for demographic characteristics, gang involvement, and substance use, adolescent report of exposure to family conflict is associated with roughly three times the odds of self-reported gun carrying behavior. The variance explained by household and neighborhood contexts remains at 23% and 24%, respectively.

4. Discussion

This study contributes to the evidence base examining adolescent gun carriage, using data from adolescents living in Southern California via the Los Angeles Family and Neighborhood Survey. This dataset permits simultaneous analysis of individual-level behaviors and family and neighborhood effects. We find that youth exposed to family conflict have more than three times the odds of carrying a gun relative to those unexposed to such conflict, even after accounting for individual-level risk behaviors known to be highly correlated with gun carriage (e.g., gang involvement and substance use [37]). These findings reinforce and extend prior evidence indicating that contexts of violence at the family level increase risk for gun carrying among adolescents [8,20].

4.1. Theoretical Perspectives and Interpretation

Applying both social learning theory and social exchange theory suggests that adolescents exposed to family conflict may learn that aggression is a way to resolve conflict, and—with this perceived gain from using violence and their imitation of learned aggressive behaviors—adolescents may be more inclined to use violence in other domains of their lives. These theories help explain how exposure to interpersonal conflict within the household may shape behavioral expectations and coping strategies in social environments outside the home. Additional research is needed to empirically apply these theoretical frameworks to examine the causal mechanisms linking exposure to family conflict and gun carriage.
At the same time, vulnerability/self-protection theory posits that individuals may carry firearms as a means of perceived self-defense in response to threats. Although our outcome measure does not capture motivations for gun carrying, we recognize that youth experiencing family conflict may also feel heightened vulnerability, potentially increasing their inclination to carry a gun for protection. Future research should empirically examine motivations for carriage to test this theory more directly.
Our findings also align with social disorganization theory, which links neighborhood disorder and weakened social cohesion to increased violence and weapon carrying. Although our dataset did not include direct measures of neighborhood disorder, we used random intercepts at the neighborhood level to account for unmeasured contextual factors. This approach, while limited, acknowledges that the neighborhood environment plays a meaningful role in shaping adolescent behavior, consistent with the theoretical premise. Taken together, these theories offer complementary perspectives on adolescent firearm carriage—emphasizing individual, familial, and contextual pathways. Future work would benefit from studies that incorporate multiple levels of measurement to empirically assess how these frameworks interact in shaping youth behavior.

4.2. Contextual Effects

To our knowledge, this is the first study that uses a sample representative of a large US city (as opposed to populations more disproportionately impacted by firearm-related harms) to examine the effects of family conflict, gang violence, and substance use on adolescent gun carrying alongside random effects of family and neighborhood in joint statistical models. In the joint fully adjusted GLMM, we find significant household and neighborhood effects on firearm carrying among adolescents with ICCs of 0.23 and 0.24 for households and neighborhoods, respectively, across all model iterations. These results indicate that variation in adolescent gun carrying explained by household and neighborhood contexts is 23% and 24%, respectively. Compared to ICCs for other adolescent health outcomes, these ICCs are relatively high, with empirical findings on neighborhood ICC for BMI at 1.5% [42] and for coitarche 4.1% [43]. The significant household and neighborhood effects we see for gun carrying behavior may suggest the salience of context-specific norms related to firearms, violence, and firearm-related violence within families and neighborhoods. Future research is needed to examine the ways in which clustering of violence occurs within geographically- and culturally identified communities, and may contribute to increased risk of adolescent bully victims and associated weapon carrying [13]. This working hypothesis is informed by American sociologists Sampson and Wilson’s highly influential work on inequalities in violence resulting from varied social circumstances available to individuals within America’s often racially and economically segregated community contexts [44]. Racial and ethnic disparities in firearm-related harm are well documented; in Los Angeles County, for example, Black Americans are killed by firearms at disproportionately high rates compared to their share of the population [45]. Although small representation of certain racial groups in the LAFANS data impeded our ability to analyze data by all racial/ethnic subgroups, we did not see significant differences in our outcome among White and Hispanic youth. This finding should be interpreted with caution until it can be replicated in other studies, however it is consistent with previous research among adolescent boys that contradicts “racist prejudice with regard to weapon carrying in schools” showing no significant differences in weapon carrying (4–5%) by race and/or ethnicity in 2017 and 2019 [46]. We also note that the prevalence of gun carrying in our analytic sample was low (2.2%), which may reduce statistical power and lead to underestimation of effect sizes.

4.3. Limitations

Other considerations should be made when interpreting findings. The outcome of gun carrying is assessed using a single item measure. While single-item measures are sometimes used for simplicity or due to survey constraints, they risk under-representing the complexity and nuances of youth firearm carrying behavior, such as the frequency of gun carrying and the reasons for carriage. This limited conceptualization of the construct prevents our study from contributing empirical evidence in support of theoretical models such as vulnerability/self-protection theory which indicate that individuals may carry guns in response to perceived threats as a form of self-defense. These data limitations may also impact the utility of findings for policy or intervention design. Further, given the small number of participants reporting the outcome, there is a risk of overfitting and model instability in the fully adjusted GLMMs. While diagnostic checks supported model convergence, the low event rate may limit the reliability of estimated associations. Future research with larger event counts is needed to confirm these findings and allow for more robust modeling. Our exposure variable—family conflict—was constructed by averaging responses across six possible items, including respondents who answered at least one item. This approach helped to preserve the sample size however it also means that some observations may not fully capture the intended construct. Future research with a larger sample reporting on these items could consider setting a threshold for the minimum number of responses to improve the reliability of the exposure variable.
All measures relied on self-report, which may be subject to recall bias and social desirability bias. While the 30-day reference window for the outcome likely limits recall error, this limited timeframe may under-represent the pervasiveness of adolescent gun carrying given that less frequent gun carrying is possible. Social desirability bias is another key concern, particularly given the sensitive and potentially stigmatized nature of both the outcome (firearm carrying) and the exposure (family conflict). Respondents may under-report handgun carrying due to fear of legal consequences or concerns about confidentiality. Similarly, individuals may minimize reports of family conflict to conform to perceived social norms or avoid disclosing negative family dynamics. These concerns may potentially bias associations toward the null. As such, the results here may show a weaker association than truly exists. In addition, although a continuous measure of family conflict may arguably capture violence in a more meaningful way than a binary measure, findings should not be interpreted as having a linear or “dose–response” relationship between gun carrying behaviors and family conflict. Consideration is also required in the way in which the construct of family conflict is operationalized here and in other studies. For example, another study examining factors related to gun carriage for young adults used a three-item scale from the Communities That Care Youth Survey instrument [47], and framed the construct as, “Lack of Family Conflict” [10]. This suggests an important avenue for intervention with families and highlights the need for intervention research to examine whether the promotion of improved conflict resolution skills in families may link to reductions in adolescent firearm carriage, especially in communities with high rates of violence.
While this study includes measures of family conflict and accounts for neighborhood-level clustering, we acknowledge that our ability to capture the full range of household and neighborhood influences was limited by the available data. Specifically, the dataset did not include validated measures of family emotional support, quality of parent–child communication, or specific neighborhood characteristics such as physical disorder and residential mobility. These are important dimensions that are theorized shape adolescent behavior and firearm carriage (e.g., social disorganization theory). Future research would benefit from incorporating a more comprehensive set of household and neighborhood indicators to better understand the pathways influencing youth firearm involvement.
Calls for longitudinal multilevel data on adolescent firearm involvement exist but these data continue to be largely unavailable or nonexistent [20]. We relied on the LAFANS data as they are the only available dataset that allows us to examine multilevel effects on gun carrying alongside our primary exposure variable, family conflict. Limitations with the data largely center on issues of model non-convergence, in part due to the rare outcome. It was not possible to examine the school context in models due to non-convergence. Further, we could not model household and neighborhood effects simultaneously, so we cannot assess what the variance parameters (i.e., the proportion of variance in the outcome) would be when modeled jointly. This represents what is known as omitted context bias, or the misattribution of the variance not included in models to the context included in a multilevel model [48]. Previous research examining the impact of omitted context bias on random effects parameter estimates highlights that—relative to single context models—variance parameters for the same contexts are attenuated—but remain statistically significant—when modeled jointly [43]. Thus, the multilevel findings in the present analysis can be interpreted as households and neighborhoods being contributing factors to variation in gun carrying behavior, but the extent to which these contexts matter should be interpreted with caution.
Finally, the LAFANS data were collected in the early 2000s. While we hypothesize that the salience of our exposure variables (family dynamics and household and neighborhood contexts) on adolescent gun carrying behaviors are unlikely to have shifted in major ways over the past two decades in American society, our findings must be replicated in more recent data to confirm this. The lack of more recent data allowing such analyses speaks to the need for updating and expansion of survey research focused on this issue. In addition, focus on the Los Angeles metropolitan area also impedes generalizability of findings to wider U.S. contexts. Nonetheless, these data allow us to examine multilevel effects using a representative sample of racially, economically, and urban/rural-based individuals rather than reliance on a sample of adolescents from special populations such as incarcerated youth. The Los Angeles metropolitan area also demonstrates one of the strongest and most sustained declines in firearm homicide rates, and this is in part attributed to stronger gun control policies in the state [49]. Hence, these data from Los Angeles provide a unique opportunity to examine the contributing determinants of gun violence beyond those of gun control policies, which often receive more attention relative to other determinants of firearm violence in research, policy, practice, and media coverage.

5. Conclusions

This study examines the role of family and neighborhood-level factors on adolescents’ carrying of firearms in Los Angeles County, a diverse geographic area. We find that family conflict is a key risk factor for adolescent carrying of firearms, and that neighborhood and family contexts are contributors to risk for gun carrying. California has implemented some of the strictest and most comprehensive gun control laws in the United States. These include the prohibition of firearm possession by individuals under a Domestic Violence Restraining Order and Gun Violence Restraining Orders, which allow courts to temporarily remove firearms from individuals deemed at risk to themselves or others [50]. The present findings provide evidence demonstrating that policies such as these which prevent those perpetrating extreme violence are not sufficient in addressing firearm violence in the U.S.; we also need normative change approaches that seek to understand and address the beliefs, behaviors, and expectations that promote gun carriage within certain American subcultures [51]. We call for multilevel primary prevention approaches for adolescent gun carrying that target family and neighborhood contexts in addition to working with individual adolescents to prevent gun carrying. Investments in families—especially those within historically disadvantaged communities—must be made to comprehensively address this public health crisis and to prevent firearm-related violence, morbidity, and mortality in the United States.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/adolescents5030044/s1, Table S1: Bivariate Models with Family Conflict x Sex Interaction; Table S2: nAGQ Simulation Results.

Author Contributions

K.M.B. made substantial contributions to the conception and design of the work and acquired the data. D.G. and N.W. performed data analysis. K.M.B., D.G., N.W., M.I. contributed to the interpretation of data. K.M.B., M.I., D.G. and A.R. drafted the manuscript, which was reviewed by N.W. and substantively revised by K.M.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported in part by the California Firearm Violence Research Center (CA FVRC) with funds from the State of California [PI: Raj] and the National Institute on Alcoholism and Alcohol Abuse (NIAAA) [K01AA028557, PI: Barker]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the California FVRC or NIAAA. The funders had no role in the design, analysis or interpretation of this research.

Institutional Review Board Statement

This study was conducted in accordance with the principles outlined in the Declaration of Helsinki (1975, revised in 2013). The research was certified by the University of California San Diego (UCSD) Institutional Review Board as not qualifying as human subjects research according to the Code of US Federal Regulations, Title 45, part 46 and University of California San Diego Standard Operating Policies and Procedures (#191982XX, approval 29 January 2020).

Informed Consent Statement

Not applicable.

Data Availability Statement

The data used for this study are publicly available via application to the University of Michigan’s Data Sharing for Demographic Research program. https://www.icpsr.umich.edu/web/DSDR/series/846 (accessed on 15 May 2025).

Acknowledgments

We thank the investigators of LAFANS—Narayan Sastry, Bonnie Ghosh-Dastidar, John Adams, and Anne R. Pebley—for their collection and sharing of these data without which this research would not be possible. We also thank the survey participants for sharing their experiences with the survey team.

Conflicts of Interest

The authors declare that they have no competing interests.

Appendix A. Generalized Linear Mixed Models (GLMMs): Equations and Modeling Decisions

The equations for the fully adjusted household (j) and neighborhood (k) GLMMs are
l o g i t π i j = l o g π i j 1 − π i j = β 0 X 0 i j + β 1 F a m V i o l i j + γ C o v i j + u 0 j + e 0 i j
l o g i t π i k = l o g π i k 1 − π i k = β 0 X 0 i k + β 1 F a m V i o l i k + γ C o v i k + v 0 k + e 0 i k
where l o g i t ( π i j ) and l o g i t π i k are the log-odds of gun carrying of individual i nested in household ( j ) or neighborhood ( k ). β 0 X 0 i j and β 0 X 0 i k are the average log-odds of gun carrying across all households or neighborhoods, holding all other covariates constant. β 1 F a m V i o l i j and β 1 F a m V i o l i k are the parameter values and variables of the individual-level family conflict measures. γ C o v i j and γ C o v i k are the vectors of individual-level covariates (age, sex, race/ethnicity, drug use, gang involvement, and family income) and corresponding parameter values. The u and v terms are the random effect parameters for household and neighborhood level variances, and are assumed to be normally distributed with a mean of 0 and a variance of σ u 0 2 u 0 j ∼ N 0 , σ u 0 2 or σ v 0 2 v 0 k ∼ N 0 , σ v 0 2 . Lastly, e 0 i j e 0 i j ∼ N 0 , σ e 0 2 and e 0 i k e 0 i k ∼ N 0 , σ e 0 2 represent the random effect of the individual under household and neighborhood contexts.
For constructing the GLMM, there are two primary approaches for estimating parameters via maximum likelihood estimation—approximation of the integrand and approximation of the integral. Approximation of the integrand can be achieved using the Laplace approximation [52]. Laplace approximates the integrals of the random effects in the marginal log-likelihood by applying a normal approximation to the integrand. While this is often sufficient, the accuracy of this approach can be more limited in settings involving binary data [53]. As a result, a more reliable approach in the modeling of binary outcomes with GLMM is via approximation of the integral using adaptive Gaussian quadrature [54]. Although more computationally demanding than other approaches, adaptive Gaussian quadrature can produce more accurate estimates by approximating the aforementioned marginal log-likelihood using finite weighted sums [52]. Given the rare event nature of our outcome, we employ adaptive Gaussian quadrature in GLMM. The accuracy of this approach can be modified by increasing the number of quadrature points (nAGQ) used in approximation of integrals in likelihood functions until stable estimates are produced [53]. We selected nAGQ = 2 as a balance between computational efficiency and precision of estimation. Two statistical packages capable of manipulating nAGQ values were considered to compute these models, namely the lme4 and GLMMadaptive packages in R. Bivariate models containing the gun carrying outcome and FC score exposure—along with random effects of household or neighborhood—were created using varying values for nAGQ. Both the lme4 and GLMMadaptive packages produced stable estimates under the neighborhood context, but under the household context, the lme4 approach produced more inconsistent estimates alongside errors in model convergence. Consequently, the GLMMadaptive package was chosen as the computational tool for this analysis. Using AIC and successful model convergence as our criteria for choosing the optimal number of points, our analyses use two quadrature points. See nAGQ Simulation results in Supplemental Table S2.

References

  1. Goldstick, J.E.; Cunningham, R.M.; Carter, P.M. Current Causes of Death in Children and Adolescents in the United States. N. Engl. J. Med. 2022, 386, 1955–1956. [Google Scholar] [CrossRef]
  2. Goldstick, J.E.; Carter, P.M.; Cunningham, R.M. Current epidemiological trends in firearm mortality in the United States. JAMA Psychiatry 2021, 78, 241–242. [Google Scholar] [CrossRef]
  3. Panchal, N.; Zitter, S. The Impact of Gun Violence on Children and Adolescents. 2025. Available online: https://www.kff.org/mental-health/issue-brief/the-impact-of-gun-violence-on-children-and-adolescents/# (accessed on 15 May 2025).
  4. Centers for Disease Control and Prevention. All Intents All Causes Nonfatal Emergency Department Visits and Rates per 100,000. WISQARS Fatal and Nonfatal Injury Reports. 2021. Available online: https://wisqars.cdc.gov/ (accessed on 8 July 2025).
  5. Naik-Mathuria, B.J.; Cain, C.M.; Alore, E.A.; Chen, L.; Pompeii, L.A. Defining the Full Spectrum of Pediatric Firearm Injury and Death in the United States: It is Even Worse Than We Think. Ann. Surg. 2023, 278, 10–16. [Google Scholar] [CrossRef]
  6. Sigel, E.J.; Mattson, S.A.; Mercado, M.C. Increased Violence Involvement and Other Behavioral and Mental Health Factors Among Youth with Firearm Access. J. Adolesc. Health 2019, 65, 63–71. [Google Scholar] [CrossRef] [PubMed]
  7. Teplin, L.A.; Meyerson, N.S.; Jakubowski, J.A.; Aaby, D.A.; Zheng, N.; Abram, K.M.; Welty, L.J. Association of Firearm Access, Use, and Victimization During Adolescence with Firearm Perpetration During Adulthood in a 16-Year Longitudinal Study of Youth Involved in the Juvenile Justice System. JAMA Netw. Open 2021, 4, e2034208. [Google Scholar] [CrossRef] [PubMed]
  8. Harper, C.R.; Li, J.; Sheats, K.; Hertz, M.F.; Merrill-Francis, M.; Friar, N.W.; Ashley, C.L.; Shanklin, S.; Barbero, C.; Gaylor, E.M.; et al. Witnessing Community Violence, Gun Carrying, and Associations with Substance Use and Suicide Risk Among High School Students—Youth Risk Behavior Survey, United States, 2021. MMWR Suppl. 2023, 72, 22–28. [Google Scholar] [CrossRef]
  9. Baiden, P.; Park, Y.; LaBrenz, C.A.; Childress, S. Exposure to Neighborhood Violence and Gun Carrying Among Adolescents in the United States: Findings From A Population-Based Study. J. Interpers. Violence 2024, 39, 3396–3423. [Google Scholar] [CrossRef]
  10. Ross, K.M.; Walsh, C.S.; O’Connor, K.E.; Sullivan, T.N. Ecological promotive and protective factors deterring gun carriage for young adults living in communities with high rates of community violence. J. Community Psychol. 2023, 51, 1164–1180. [Google Scholar] [CrossRef] [PubMed]
  11. Oliphant, S.N.; Mouch, C.A.; Rowhani-Rahbar, A.; Hargarten, S.; Jay, J.; Hemenway, D.; Zimmerman, M.; Carter, P.M.; Consortium, F. A scoping review of patterns, motives, and risk and protective factors for adolescent firearm carriage. J. Behav. Med. 2019, 42, 763–810. [Google Scholar] [CrossRef]
  12. Valdebenito, S.; Ttofi, M.M.; Eisner, M.; Gaffney, H. Weapon carrying in and out of school among pure bullies, pure victims and bully-victims: A systematic review and meta-analysis of cross-sectional and longitudinal studies. Aggress. Violent Behav. 2017, 33, 62–77. [Google Scholar] [CrossRef]
  13. van Geel, M.; Vedder, P.; Tanilon, J. Bullying and Weapon Carrying: A Meta-analysis. JAMA Pediatr. 2014, 168, 714–720. [Google Scholar] [CrossRef] [PubMed]
  14. Sampson, R.J.; Groves, W.B. Community Structure and Crime: Testing Social-Disorganization Theory. Am. J. Sociol. 1989, 94, 774–802. [Google Scholar] [CrossRef]
  15. Molnar, B.E.; Miller, M.J.; Azrael, D.; Buka, S.L. Neighborhood predictors of concealed firearm carrying among children and adolescents: Results from the project on human development in Chicago neighborhoods. Arch. Pediatr. Adolesc. Med. 2004, 158, 657–664. [Google Scholar] [CrossRef] [PubMed]
  16. Cavanaugh, M.M. Theories of Violence: Social Science Perspectives. J. Hum. Behav. Soc. Environ. 2012, 22, 607–618. [Google Scholar] [CrossRef]
  17. Bronfenbrenner, U. The Ecology of Human Development: Experiments by Nature and Design; Harvard University Press: Cambridge, MA, USA, 1979. [Google Scholar]
  18. Sheley, J.F.; Brewer, V.E. Possession and carrying of firearms among suburban youth. Public Health Rep. 1995, 110, 18. [Google Scholar]
  19. Kingery, P.M.; Pruitt, B.E.; Heuberger, G. A profile of rural Texas adolescents who carry handguns to school. J. Sch. Health 1996, 66, 18–22. [Google Scholar] [CrossRef]
  20. Carter, P.M.; Zimmerman, M.A.; Cunningham, R.M. Addressing Key Gaps in Existing Longitudinal Research and Establishing a Pathway Forward for Firearm Violence Prevention Research. J. Clin. Child. Adolesc. Psychol. 2021, 50, 367–384. [Google Scholar] [CrossRef]
  21. Bandura, A. Aggression: A Social Learning Analysis; Prentice-Hall: Englewood Cliffs, NJ, USA, 1973. [Google Scholar]
  22. Bandura, A. The social learning perspective: Mechanisms of aggression. In Psychology of Crime and Criminal Justice; Toch, H., Ed.; Waveland Press: Prospect Heights, IL, USA, 1986; pp. 198–236. [Google Scholar]
  23. Gelles, R.J. An exchange/social control theory. In The Dark Side of Families: Current Family Violence Research; Finkelhor, D., Gelles, R.J., Hotaling, G.T., Straus, M.A., Eds.; Sage: Beverly Hills, CA, USA, 1983; pp. 151–165. [Google Scholar]
  24. Bicchieri, C. The Grammar of Society: The Nature and Dynamics of Social Norms; Cambridge University Press: New York, NY, USA, 2006. [Google Scholar]
  25. Cislaghi, B.; Heise, L. Using social norms theory for health promotion in low-income countries. Health Promot. Int. 2018, 34, 616–623. [Google Scholar] [CrossRef]
  26. Wolfgang, M.E.; Ferracuti, F.; Mannheim, H. The Subculture of Violence: Towards an Integrated Theory in Criminology; Tavistock Publications London: London, UK, 1967; Volume 16. [Google Scholar]
  27. Meeker, E.C.; O’Connor, B.C.; Kelly, L.M.; Hodgeman, D.D.; Scheel-Jones, A.H.; Berbary, C. The impact of adverse childhood experiences on adolescent health risk indicators in a community sample. Psychol. Trauma: Theory Res. Pract. Policy 2021, 13, 302–312. [Google Scholar] [CrossRef]
  28. Yexley, M.; Borowsky, I.; Ireland, M. Correlation Between Different Experiences of Intrafamilial Physical Violence and Violent Adolescent Behavior. J. Interpers. Violence 2002, 17, 707–720. [Google Scholar] [CrossRef]
  29. Huesmann, L.R.; Dubow, E.F.; Boxer, P.; Bushman, B.J.; Smith, C.; Docherty, M.; O’Brien, M. Longitudinal predictions of young adults’ weapons use and criminal behavior from their childhood exposure to violence. Aggress. Behav. 2021, 47, 621–634. [Google Scholar] [CrossRef]
  30. Susser, M.; Susser, E. Choosing a future for epidemiology: From black box to Chinese boxes and eco-epidemiology. Am. J. Public Health 1996, 86, 674–677. [Google Scholar] [CrossRef]
  31. Sastry, N.; Ghosh-Dastidar, B.; Adams, J.; Pebley, A. The Design of a Multilevel Longitudinal Survey of Children, Families, and Communities: The Los Angeles Family and Neighborhood Survey; DRU-2400/1-LAFANS; RAND Working Paper Series; RAND: Santa Monica, CA, USA, 2000. [Google Scholar]
  32. Sastry, N.; Ghosh-Dastidar, B.; Adams, J.L.; Pebley, A.R. The Design of a Multilevel Survey of Children, Families, and Communities: The Los Angeles Family and Neighborhood Survey. Soc. Sci. Res. 2006, 35, 1000–1024. [Google Scholar] [CrossRef]
  33. van Buuren, S.; Groothuis-Oudshoorn, K. mice: Multivariate Imputation by Chained Equations in R. J. Stat. Softw. 2011, 45, 1–67. [Google Scholar] [CrossRef]
  34. Wing, S.E.; Bandoli, G.; Telesca, D.; Su, J.G.; Ritz, B. Chronic exposure to inhaled, traffic-related nitrogen dioxide and a blunted cortisol response in adolescents. Env. Res. 2018, 163, 201–207. [Google Scholar] [CrossRef] [PubMed]
  35. Pebley, A.R.; Sastry, N. Los Angeles Family and Neighborhood Survey (L.A.FANS), Wave 1, Restricted Data Version 1, 2000-2001, ICPSR Codebook for Household Income and Assets Module Version 1 Restricted Variables; Inter-University Consortium for Political and Social Research (ICPSR), University of Michigan: Ann Arbor, MI, USA, 2019. [Google Scholar]
  36. Dong, B. Developmental Comorbidity of Substance Use and Handgun Carrying Among U.S. Youth. Am. J. Prev. Med. 2021, 61, 209–216. [Google Scholar] [CrossRef]
  37. Watkins, A.M.; Huebner, B.M.; Decker, S.H. Patterns of gun acquisition, carrying, and use among juvenile and adult arrestees: Evidence from a high-crime city. Justice Q. 2008, 25, 674–700. [Google Scholar] [CrossRef]
  38. Merlo, J.; Bengtsson-Boström, K.; Lindblad, U.; Råstam, L.; Melander, O. Multilevel analysis of systolic blood pressure and ACE gene I/D polymorphism in 438 Swedish families—a public health perspective. BMC Med. Genet. 2006, 7, 14. [Google Scholar] [CrossRef]
  39. Team, R.C. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2023. [Google Scholar]
  40. Posit, T. RStudio: Integrated Development Environment for R; Posit Software, PBC: Boston, MA, USA, 2023. [Google Scholar]
  41. Rizopoulos, D. GLMMadaptive: Generalized Linear Mixed Models Using Adaptive Gaussian Quadrature. 2023. Available online: https://drizopoulos.github.io/GLMMadaptive/ (accessed on 15 May 2025).
  42. Evans, C.R.; Onnela, J.P.; Williams, D.R.; Subramanian, S.V. Multiple contexts and adolescent body mass index: Schools, neighborhoods, and social networks. Soc. Sci. Med. 2016, 162, 21–31. [Google Scholar] [CrossRef]
  43. Barker, K.M.; Subramanian, S.V.; Berkman, L.; Austin, S.B.; Evans, C.R. Adolescent sexual initiation: A cross-classified multilevel analysis of peer group, school, and neighborhood-level influences. J. Adolesc. Health 2019, 65, 390–396. [Google Scholar] [CrossRef]
  44. Sampson, R.J.; Wilson, W.J. Toward a Theory of Race, Crime, and Urban Inequality. In Crime and Inequality; Hagan, J., Peterson, R.D., Eds.; Stanford University Press: Stanford, CA, USA, 1995; pp. 37–56. [Google Scholar]
  45. Jan, M.; Sternfeld, I.; Welsing, A.; Shetgiri, R. Promoting Firearm Safety and Preventing Firearm Violence: What Clinicians Can Do. Rx Prev. 2019, 9, 2. Available online: https://rx.ph.lacounty.gov/Portals/0/docs/RxViolencePrevention0719.pdf (accessed on 15 May 2025).
  46. Jewett, P.I.; Gangnon, R.E.; Kafka, J.; Areba, E.M.; Malecki, K.; Borowsky, I.W. Weapon Carrying Among Boys in US Schools by Race and/or Ethnicity: 1993–2019. Pediatrics 2021, 148, e2020049623. [Google Scholar] [CrossRef]
  47. Arthur, M.W.; Hawkins, J.D.; Pollard, J.A.; Catalano, R.F.; Baglioni, A.J. Measuring Risk and Protective Factors for Use, Delinquency, and Other Adolescent Problem Behaviors:The Communities That Care Youth Survey. Eval. Rev. 2002, 26, 575–601. [Google Scholar] [CrossRef]
  48. Meyers, J.L.; Beretvas, S.N. The Impact of Inappropriate Modeling of Cross-Classified Data Structures. Multivar. Behav. Res. 2006, 41, 473–497. [Google Scholar] [CrossRef] [PubMed]
  49. Kegler, S.R.; Dahlberg, L.L.; Vivolo-Kantor, A.M. A descriptive exploration of the geographic and sociodemographic concentration of firearm homicide in the United States, 2004–2018. Prev. Med. 2021, 153, 106767. [Google Scholar] [CrossRef]
  50. Office of the Attorney General. Domestic Violence Restraining Orders and Gun Violence Restraining Orders. Available online: https://oag.ca.gov/ogvp/gvro-dvro (accessed on 15 May 2025).
  51. Lemieux, F. Effect of Gun Culture and Firearm Laws on Gun Violence and Mass Shootings in the United States: A Multi-Level Quantitative Analysis. Int. J. Crim. Justice Sci. 2014, 9, 1. [Google Scholar]
  52. Fitzmaurice, G.M.; Laird, N.M.; Ware, J.H. Applied Longitudinal Analysis, 2nd ed.; Wiley Series in Probability and Statistics; John Wiley & Sons: Hoboken, NJ, USA, 2011; pp. 395–433. [Google Scholar]
  53. Pinheiro, J.C.; Chao, E.C. Efficient Laplacian and Adaptive Gaussian Quadrature Algorithms for Multilevel Generalized Linear Mixed Models. J. Comput. Graph. Stat. 2006, 15, 58–81. [Google Scholar] [CrossRef]
  54. Bolker, B.M.; Brooks, M.E.; Clark, C.J.; Geange, S.W.; Poulsen, J.R.; Stevens, M.H.H.; White, J.-S.S. Generalized linear mixed models: A practical guide for ecology and evolution. Trends Ecol. Evol. 2009, 24, 127–135. [Google Scholar] [CrossRef]
Table 1. Sociodemographic characteristics of participants (n = 881).
Table 1. Sociodemographic characteristics of participants (n = 881).
Variablen (%)
Gun Carrying
No862 (97.8%)
Yes19 (2.2%)
Family Conflict Score
Mean (SD) [Min, Max]0.64 (0.405)
[0.00, 2.00]
Age (years)
Median [Min, Max]14.0 [12.0, 17.0]
Sex
Male449 (51.0%)
Female432 (49.0%)
Any Gang Membership
No859 (97.5%)
Yes22 (2.5%)
Any Drug Use
No485 (55.1%)
Yes396 (44.9%)
Race
Latino484 (54.9%)
Other (American Indian, Asian, Black, and Pacific Islander)209 (23.8%)
White188 (21.3%)
Imputed Family Income (in Thousands) *32.0 [0, 981]
Median [Min, Max]
Number of Unique Households
n (SD)736 (0)
Number of Unique Tracts
n (SD)65.0 (0)
Individuals per Household
Median [Min, Max]1.0 [1.0, 2.0]
Individuals per Tract
Median [Min, Max]4.0 [3.0, 7.0]
* n = 86.912 missing values imputed via MICE.
Table 2. Household and neighborhood effects on adolescent gun carrying (null models).
Table 2. Household and neighborhood effects on adolescent gun carrying (null models).
Model 1. Household, Null ModelModel 2. Neighborhood, Null Model
Fixed Effects Fixed Effects
PredictorsOdds RatioCIp-valuePredictorsOdds RatioCIp-value
Intercept0.010.01–0.02<0.001Intercept0.010.01–0.02<0.001
Random EffectsRandom Effects
σ23.29σ23.29
τ00 HHID1.01τ00 TRACTX1.05
ICC0.23ICC0.24
N HHID736N TRACTX65
Observations881Observations881
Note: both models employ nAGQ = 2.
Table 3. Violent family conflict and adolescent gun carrying, accounting for household and neighborhood effects.
Table 3. Violent family conflict and adolescent gun carrying, accounting for household and neighborhood effects.
Model 3. Bivariate Model, HouseholdModel 4. Bivariate Model, Neighborhood
Fixed Effects Fixed Effects
PredictorsOdds RatioCIp-valuePredictorsOdds RatioCIp-value
Intercept00.00–0.01<0.001Intercept00.00–0.01<0.001
Violent Family Conflict Score4.751.74–12.940.002Violent Family Conflict Score4.621.69–12.590.003
Random EffectsRandom Effects
σ23.29σ23.29
τ00 HHID1.01τ00 TRACTX1.05
ICC0.23ICC0.24
N HHID736N TRACTX65
Observations881Observations881
Note: both models employ nAGQ = 2.
Table 4. Violent family conflict and adolescent gun carrying (bivariate models—accounting for demographic characteristics and household and neighborhood effects).
Table 4. Violent family conflict and adolescent gun carrying (bivariate models—accounting for demographic characteristics and household and neighborhood effects).
Model 5. Full Model, Household, nAGQ = 2Model 6. Full Model, Neighborhood, nAGQ = 2
Fixed Effects Fixed Effects
PredictorsOdds RatioCIpPredictorsOdds RatioCIp
Intercept0.000.00–0.440.023Intercept0.000.00–0.440.023
Violent Family Conflict Score3.451.04–11.490.043Violent Family Conflict Score3.230.98–10.570.053
Age0.990.70–1.390.936Age0.980.70–1.380.908
Sex: Female0.390.13–1.210.102Sex: Female0.410.13–1.250.117
Any gang involvement: Yes14.843.76–58.60<0.001Any gang involvement: Yes14.933.78–59.05<0.001
Ever use any substance: Yes6.721.38–32.660.018Ever use any substance: Yes6.591.36–31.970.019
Race: Other
(ref: Lantix)		0.470.10–2.170.335Race: Other0.50.11–2.370.383
Race: Non-Hispanic White (ref: Lantix)0.780.20–3.070.719Race: Non-Hispanic White0.920.22–3.840.913
Imputed Family Income10.98–1.010.569Imputed Family Income10.99–1.010.637
Random EffectsRandom Effects
σ23.30σ23.32
τ00 HHID1.01τ00 TRACTX1.02
ICC0.23ICC0.24
N HHID736N TRACTX65
Observations881Observations881
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Barker, K.M.; Gregoire, D.; Wilcox, N.; Izadshenas, M.; Raj, A. Family Conflict and Gun Carrying in Adolescence: Multilevel Analysis of Household and Neighborhood Effects in Los Angeles County. Adolescents 2025, 5, 44. https://doi.org/10.3390/adolescents5030044

AMA Style

Barker KM, Gregoire D, Wilcox N, Izadshenas M, Raj A. Family Conflict and Gun Carrying in Adolescence: Multilevel Analysis of Household and Neighborhood Effects in Los Angeles County. Adolescents. 2025; 5(3):44. https://doi.org/10.3390/adolescents5030044

Chicago/Turabian Style

Barker, Kathryn M., Devin Gregoire, Naomi Wilcox, Maryam Izadshenas, and Anita Raj. 2025. "Family Conflict and Gun Carrying in Adolescence: Multilevel Analysis of Household and Neighborhood Effects in Los Angeles County" Adolescents 5, no. 3: 44. https://doi.org/10.3390/adolescents5030044

APA Style

Barker, K. M., Gregoire, D., Wilcox, N., Izadshenas, M., & Raj, A. (2025). Family Conflict and Gun Carrying in Adolescence: Multilevel Analysis of Household and Neighborhood Effects in Los Angeles County. Adolescents, 5(3), 44. https://doi.org/10.3390/adolescents5030044

Article Metrics

Back to TopTop