title: Cumulative sociodemographic risk, adolescent antisocial behaviour, and the explanatory value of General Strain Theory
Creators: name: Thomsen, Lisa
Publisher: name: Griffith University
Year: 2020
Subjects: subject: sociodemographic contexts
subject: adolescent
subject: antisocial behaviour

description: Antisocial behaviour during adolescence can have grave consequences for young people, particularly when such behaviour leads to official contact with the criminal justice system. Numerous risk and promotive/protective factors have been identified by researchers. Often these are proximal influences such as individual characteristics or relationships with parents which have a strong, direct, and mostly immediate effect on outcomes. While the ways in which proximal factors influence antisocial activity are quite well established, a much smaller body of literature within Criminology (as well as associated fields such as Health), demonstrates that broader sociodemographic contexts also impact adolescent lives. These distal factors include contextual risks such as neighbourhood of residence, family SES, and cultural background. It has been difficult to establish in empirical research that the strong statistical relationship between structural characteristics such as social class and delinquency/crime can also be a direct relationship. Instead, the literature suggests that sociodemographic factors have an interactive and/or mediated effect on adolescent experiences, especially through effects on family functioning and parenting capabilities, the positive effects of which tend to be undermined by poverty and social exclusion. Testable theory-based models of the moderated mediation pathways of environmental and structural contexts are not particularly common in the literature on adolescent antisocial behaviours. The aim of this research is to explore how broad sociodemographic contexts affect adolescent involvement in antisocial behaviours. In line with bioecological theory, I posit that distal or macro variables related to environmental and social structural contexts affect proximal processes in a number of ways that lead to increases in the likelihood of antisocial activities. Because General Strain Theory (GST) proposes that strains increase the likelihood of crime due to the negative emotions which they evoke, I have adopted GST as an explanatory framework which is suitable for constructing and testing a moderated-mediation model for youth antisocial behaviours. Central to this thesis is the notion of cumulative sociodemographic risk. Risk factors tend to co-occur, and many adolescents are subjected to an accumulation of risks experienced simultaneously. Cumulative risk research has demonstrated that the likelihood of poor outcomes increases markedly as more and more risks co-occur. The literature on this phenomenon most often uses indices that count the number of risks experienced, with risks that are drawn from different domains, including individual, family, and social-structural grouped together. While this is useful to examine how increased risk exposure is related to outcomes, such an approach makes it difficult to explore the specific processes through which structural or sociodemographic stressors operate. I addressed this gap in the literature by constructing a cumulative index of risks related only to structural or social-environmental factors. I sourced data from 524 Australian youths aged 13 to 18 who responded to social media advertisements for participation, or who were registered with organisations providing rewards for survey completion. In order to ‘oversample’ adolescents likely to be exhibiting antisocial involvement, a subset of the study participants (n = 62) was made up of adolescents already exhibiting problematic behaviours and/or experiencing difficulties of some kind, to a level requiring assistance, resulting in them either being in contact with a Youth Service Agency, or attending an alternate schooling program. All participants completed a self-report survey that collected information on individual attributes (self-control, prosocial beliefs, empathy), emotional wellbeing (self-esteem, self-worth, life satisfaction, depressed feelings), social ties (social acceptance, school membership) and parenting relationships (parental attachment, knowledge, monitoring and availability). Antisocial behaviour of both self and friends during the previous 12-months was assessed using the 24-item scale utilised in 2006 in the Australian Healthy Neighbourhoods Study. This scale was adapted for Australian participants from the Communities That Care Youth Survey. Items in this scale assess antisocial behaviours, engagement in risk taking behaviours (such as drug and alcohol use) and deviant/criminal behaviours (such as truancy, theft, physical violence). With the influence of structural background characteristics being a key question in this study, information was also collected on several demographic and contextual variables. From this information participants were assigned cumulative sociodemographic risk scores ranging from 0 to 12, based on reported family size, family mobility, family structure, socioeconomic status, neighbourhood of residence, and Indigeneity. These scores were used to categorise youths as vulnerable or not vulnerable due to cumulative sociodemographic risk. As is frequently the case with data on antisocial behaviours, particularly where it consists of a count of antisocial acts, issues arose in relation to the modelling of a highly skewed outcome variable with an excess of zero values. I undertook an extensive investigation of suitable statistical approaches for highly skewed count data, including an in-depth exploration of model fit as a means of determining the optimal statistical distribution for my measure of antisocial behaviour. Consistent with much of the criminological literature in this area, a Negative Binomial Regression model (NBRM) was identified as the ‘least-worst’ solution, based simultaneously on statistical, theoretical, and practical considerations. As the notion of cumulative sociodemographic risk is relatively novel in the criminological literature, this aspect of the research was largely exploratory in nature. I aimed to present a picture of what constitutes cumulative sociodemographic vulnerability, how this phenomenon affects the lives of adolescents, and whether, why, and how it is related to an increased likelihood of antisocial behaviour. In exploring the ‘how’ I was particularly interested in the explanatory value of GST. I structured the overall investigation of these issues through three research questions: 1. How does cumulative sociodemographic risk influence adolescent lives? In responding to this question, I identified risk, promotive, and protective factors for poor youth outcomes according to cumulative sociodemographic vulnerability, thus providing insight into how cumulative sociodemographic risk affects individual attributes as well as proximal processes for male and female adolescents. Socio-demographically vulnerable youths exhibited lower levels of factors which research suggests should act protectively, including the individual characteristics of self-control, empathy, and prosocial beliefs, as well as lower levels of life satisfaction and school membership. In addition, sociodemographic vulnerability increased the likelihood of interaction with other adolescents who were also behaving antisocially, a known risk factor for antisocial involvement. Cumulative sociodemographic risk was associated with higher likelihoods of engaging in antisocial activities for both male and female adolescents. Socio-demographically vulnerable males (but not females) were also significantly more likely to use illicit substances and alcohol than their not-vulnerable counterparts. Vulnerable adolescents were more likely than non-vulnerable adolescents to engage in all categories of antisocial activity. The greatest differences in participation rates were observed for more serious offending, namely automotive crime, property crime, assault, and substance use, in contrast to acts of minor delinquency including truancy, shoplifting and stealing money. 2. Do predictors of adolescent antisocial behaviour differ according to cumulative sociodemographic risk status? Model fit analyses suggested that predictors of adolescent antisocial involvement during the previous year were best investigated utilising a hurdle model. My hurdle model first involved a logistic regression to determine predictors of absence/presence of antisocial behaviour, and then a truncated negative binomial regression to determine predictors of the extent of antisocial behaviour amongst those youths who reported some antisocial activity. The hurdle model analyses showed that cumulative sociodemographic vulnerability was related to differences in predictors of the absence/presence of antisocial behaviour. Specifically, while several individual and parenting characteristics predicted antisocial involvement for not-vulnerable adolescents, exposure to peer antisocial behaviour and the extent of sociodemographic risk overwhelmed other predictors of antisocial involvement for vulnerable youths. I found greater commonalities between vulnerability groups for predictors of the extent of antisocial behaviour for youths reporting some antisocial activity, with peer antisocial behaviour and parental monitoring being the strongest predictors. These findings suggest that while antisocial activity (particularly its extent) might be reduced for all youths through increased monitoring and reduced contact with antisocial peers, participation (vs non-participation) in antisocial behaviour amongst vulnerable adolescents is most strongly influenced by the extent of their vulnerability — something which is beyond the immediate control of these youths and their families, or prevention scientists. 3. Can the relationship between cumulative sociodemographic risk and adolescent antisocial behaviour be explained with a General Strain Theory (GST) model? Given the lack of theoretically based explanations for the impact of cumulative socioeconomic disadvantage on adolescent antisocial behaviour, I tested the applicability of a General Strain Theory (GST) model. This model included mediation of cumulative sociodemographic risk on adolescent antisocial behaviour through affective responses to sociodemographic strain. Two affective mediators of strain were tested. The first, depressed feelings, has shown mixed success as a mediator in previous strain research. The second, life satisfaction, has not previously been explored in the strain literature, but was considered to be an appropriate affective mediator due to its ability to capture a wide range of affective responses, thus reducing the likelihood of gender effects. Of these two mediators, only life satisfaction was found to be significant, partially mediating the relationship between cumulative risk and antisocial behaviours. In an extended moderated mediation model, I found several interactions that were consistent with predictions based on GST. Strong parenting relationships, better emotional wellbeing, and bonds with school acted protectively to reduce the effects of strain (cumulative sociodemographic risk) as well as the affective consequences of experiencing strain (life satisfaction). However other significant moderators did not fit with GST, with some findings suggesting that the possibility of reciprocal relationships over time should be tested using longitudinal data. Despite these inconsistencies, the pattern overwhelmingly revealed through the modelling was that the magnitude of the positive effects of factors that should be protective for adolescents reduced as levels of cumulative risk increased. Conclusion While society emphasises personal accountability in relation to youth offending, it is clear that environmental and structural contexts, which are beyond immediate control, have a powerful effect on adolescent lives. Cumulative sociodemographic risk increases the likelihood of experiencing more risk factors and fewer protective factors. Adolescents who are socio-demographically vulnerable not only engage in more antisocial acts, they participate in more serious delinquent behaviours. The extent of sociodemographic disadvantage is the strongest predictor of this antisocial involvement. Factors which act protectively in reducing the impact of the strain of disadvantage on the extent and seriousness of antisocial behaviour have reduced effects as cumulative sociodemographic risk increases. The influence of cumulative sociodemographic risk is pervasive and all-encompassing, suggesting that it should be more carefully measured and modelled in future criminological research. Some limitations are acknowledged in this research, most notably the use of cross-sectional data which has prevented the investigation of time-order in relationships between variables. Statistical issues in regard to the problematic nature of a highly skewed dataset have been addressed to the greatest extent possible within the bounds of a PhD study that is not primarily statistical in nature, but it is clear that further investigation using advanced analytic procedures is possible and desirable. Despite these limitations, the findings reported in this thesis have important implications for preventing and responding to youth antisocial activity. While there is much that can be done using data-driven, evidence-based strategies to tackle antisocial behaviour during the adolescent period, without some attention to the effects of structural and contextual factors these approaches are likely to fall short of their full potential. Further research is necessary to better understand the processes through which cumulative sociodemographic disadvantage affects adolescents, and the factors that are most influential in reducing its impact. Such understanding will facilitate the development of realistic and effective countermeasures for youths at the ‘sharp end’ of structural social inequality.