A model-based cluster analysis approach to adolescent problem behaviors and young adult outcomes

Abstract

Data from a community-based sample of 1,126 10th-and 11th-grade adolescents were analyzed using a model-based cluster analysis approach to empirically identify heterogeneous adolescent subpopulations from the person-oriented and pattern-oriented perspectives. The model-based cluster analysis is a new clustering procedure to investigate population heterogeneity utilizing finite mixture multivariate normal densities and accordingly to classify subpopulations using more rigorous statistical procedures for the comparison of alternative models. Four cluster groups were identified and labeled multiproblem high-risk, smoking high-risk, normative, and low-risk groups. The multiproblem high risk exhibited a constellation of high levels of problem behaviors, including delinquent and sexual behaviors, multiple illicit substance use, and depressive symptoms at age 16. They had risky temperamental attributes and lower academic functioning and educational expectations at age 15.5 and, subsequently, at age 24 completed fewer years of education, and reported lower levels of physical health and higher levels of continued involvement in substance use and abuse. The smoking high-risk group was also found to be at risk for poorer functioning in young adulthood, compared to the low-risk group. The normative and the low risk groups were, by and large, similar in their adolescent and young adult functioning. The continuity and comorbidity path from middle adolescence to young adulthood may be aided and abetted by chronic as well as episodic substance use by adolescents.Adolescence is a developmental period characterized by increases in risk behaviors, mood fluctuations, and conflict with parents (Arnett, 1992(Arnett, , 1999, as well as when significant brain development occurs in the frontal lobes, influencing the development of better reasoning and decision-making capabilities (Dahl, 2004;Reyna & Farley, 2006). Recent national data from the 2005 Youth Risk Behavior Survey (Eaton et al., 2006) highlight that a substantial percentage of adolescents in 9th through 12th grades in the United States are involved in a number of risk behaviors, including current cigarette smoking (23.0%), alcohol use (43.3%), heavy drinking (25.5%), marijuana use (20.2%), aggression and violence (e.g., physical fighting; 35.9%), and extreme feelings of sadness (28.5%) and suicidal ideas (16.9%). The alarmingly high rates of substance use among adolescents and college students are echoed in the most recent report from the Monitoring the Future National Survey (Johnston, O'Malley, Bachman, & Schulenberg, 2006). For most adolescents, engagement in some level of risk behavior is part of a statistically normative process that is intertwined with age-appropriate developmental tasks associated with increases in autonomy and self-regulation.Address correspondence and reprint requests to: Eun Young Mun, Rutgers Center of Alcohol Studies, Rutgers University, 607 Allison Road, Piscataway, NJ 08854; E-mail: eymun@rci.rutgers.edu.. NIH Public Access Author Manus...

Trajetórias escolares de adolescentes em conflito com a lei

“…A apresentação de comportamentos de risco, incluindo o cometimento de algum ato infracional, na adolescência, é parte de um processo estatisticamente normativo (MUN;WINDLE;SCHAINKER, 2008). Há, entretanto, adolescentes que se envolvem com a prática de atos infracionais de modo mais sério, seja pela frequência e/ou pela gravidade das ações.…”

Associações entre Bullying Escolar e Conduta Infracional: Revisão Sistemática de Estudos Longitudinais

“…Como na adolescência a probabilidade de emissão de comportamentos de risco, tais como os inerentes ao bullying e os relativos às infrações, é maior do que em outras fases de desenvolvimento (Mun, Windle, & Schainker, 2008), considera-se que esse período da vida refere-se a um momento de maior vulnerabilidade às experiências de violência entre pares na escola em função, dentre outros aspectos, do tempo que passam nessa instituição e do maior contato com outros adolescentes. No mais, a prática de bullying sinaliza, em muitos casos, o início de um processo desenvolvimental problemático, que conduz ao cometimento de infrações à lei (Piquero, Connell, Piquero, Farrington, & Jennings, 2012;, sendo que alguns estudos têm demonstrado que o comportamento agressivo e o bullying na escola são preditores de participação em gangues, bem como de criminalidade na idade adulta (Hinduja & Patchin, 2007;Sourander et al, 2011).…”

Finding groups using model-based cluster analysis: Heterogeneous emotional self-regulatory processes and heavy alcohol use risk.

“…Therefore, it is critical to have a statistical fit measure that penalizes for complexity and rewards for parsimony when comparing different models that vary in the number of extracted clusters (e.g., Bayesian Information Criterion). This would eliminate the ambiguity associated with relying on subjective criteria (Fraley & Raftery, 1998;Raftery & Dean, 2006).Model-based cluster analysis is one such alternative that is an inferentially based, statistically principled procedure that allows comparison of non-nested models (Raftery & Dean, 2006;Mun et al, 2008, and Appendices A and B for details). Model-based cluster analysis is based on the assumption that the observed data come from a population consisting of several subpopulations.…”

“…The use of model-based cluster analysis has emerged in recent years in psychology-related empirical research (e.g., Hicks, Markon, Patrick, Krueger, & Newman, 2004;Mun et al, 2008;Skeem, Johansson, Andershed, Kerr, & Louden, 2007). The current study, however, is one of the first papers to characterize this method in the context of more traditional cluster analysis approaches, structural equation models, and emerging finite mixture models, and to detail its use as a new alternative method for the person-oriented and pattern-oriented approaches used by developmental researchers (see also Mun et al, 2008).To illustrate this method, we use objective indices of autonomic nervous system regulation from a controlled experiment to explore how individual differences in one's ability to regulate emotional arousal may be related to development of risky alcohol use patterns. By doing so, we aim to demonstrate that model-based cluster analysis can be used to identify individuals of distinctive profiles in an experimental design with a small sample, to estimate the proportion of the identified subpopulations, and to relate the probability of membership in the cluster groups to other psychosocial characteristics.…”