Population heterogeneity in the salience of multiple risk factors for adolescent delinquency

Time-Varying Effect Models for Examining Age-Dynamic Associations in Gerontological Research

OBJECTIVES

Dynamic processes unfolding over later adulthood are of prime interest to gerontological researchers. Time-varying effect modeling (TVEM) accommodates dynamic change trajectories, but its use in gerontological research is limited. We introduce and demonstrate TVEM with an empirical example based on the National Health and Aging Trends Study (NHATS).

METHODS

We examined (a) age-varying prevalence of past month elevated symptoms of depression and anxiety and (b) age-varying associations between older adults' elevated symptoms of depression and anxiety and needing help with basic activities of daily living and educational attainment.

RESULTS

The proportion of participants reporting elevated symptoms of depression and anxiety in the past month increased gradually from 23-29% across the ages 70-92. Individuals needing help with ADLs had higher odds of reporting elevated symptoms of depression and anxiety, however the association was strongest for those in their 60s versus 80s. Across all ages, adults with lower education levels had higher odds of reporting elevated symptoms of depression and anxiety, an association that also varied by age.

CONCLUSION

We demonstrated TVEM's value for studying dynamic associations that vary across chronological age. With the recent availability of free, user-friendly software for implementing TVEM, gerontological researchers have a new tool for exploring complex change processes that characterize older adults' development.

Heterogeneous Association of Chinese Adolescents' Engaged Living With Problematic Internet Use: A Mixture Regression Analysis

The present study explored heterogeneity in the association between engaged living (i.e., social integration and absorption) and problematic Internet use (PIU). This study included 641 adolescents from four junior-senior high schools of Guangzhou, China. Besides the standard linear regression analysis, mixture regression analysis was conducted to detect certain subgroups of adolescents, based on their divergent association between engaged living and PIU. Sex, age, and psychological need were further compared among the latent subgroups. The results showed that a mixture regression model could account for more variance of PIU than a traditional linear regression model, and identified three subgroups based on their class-specific regression of PIU to engaged living. For the High-PIU class, lower social integration and higher absorption were associated with increased PIU; for the Medium-PIU class, only high social integration was linked with the increase of PIU. For the Low-PIU class, no relation between engaged living and PIU were found. Additionally, being male or having a lower level of satisfied psychological needs increased the link between engaged living and PIU. The results indicated a heterogeneous relationship between engaged living and PIU among adolescents, and prevention or intervention programs should be tailored specifically to subgroups with moderate or high levels of PIU and to those with lower levels of psychological needs’ satisfaction, as identified by the mixture regression model.

Dark Triad Traits and Risky Behaviours: Identifying Risk Profiles from a Person-Centred Approach

The relationship between Dark Triad traits and risky behaviours has been shown in recent years. However, few studies have attempted to disentangle this relationship using a person-centred approach. The goal of the current study was to identify subgroups of individuals on the basis of their scores on Machiavellianism, psychopathy, and narcissism and analyse the differences between them in a set of risky behaviours (i.e., frequency of substance use, reactive and proactive aggression, risk perception and risk engagement, and problematic internet use). The sample consisted of 317 undergraduates aged 18–34 (46% males). The results of the latent profile analysis showed five subgroups of individuals that were identified based on their scores on the Dark Triad traits: low-Dark Triad, narcissistic, Machiavellian/narcissistic, psychopathic, and Machiavellian/psychopathic. Overall, the Machiavellian/narcissistic and Machiavellian/psychopathic subgroups showed higher scores for most risky behaviours. The low-Dark Triad scored higher for risk perception. No significant differences between subgroups were found as regards frequency of alcohol, tobacco, and cannabis use. These findings suggest that the combination of the Dark Triad traits lead to more negative outcomes as regards risky behaviour than individual components. Moreover, they highlight the relevance of using a person-centred approach in the study of dark personalities.

Identifying Risk Profiles for Antisocial Behavior in a Spanish Sample of Young Offenders

One of the main purposes of juvenile risk assessment is to distinguish different risk profiles, which may lead to referring youths into specific intervention programs tailored to their specific needs. This study is devoted to identifying main typologies of risk in a sample of 286 Spanish young offenders aged 14 to 22 (M = 17.36; SD = 1.61) years. Participants were classified into different profiles, representing different levels of risk in terms of individual and psychosocial dynamic variables. A three-class (low-, middle-, and high-risk profiles) and a four-class (low-, middle-, high-risk family problems/callous-unemotional (CU) traits, and high-risk impulsive/undercontrolled) solutions were identified. These profiles showed their distinctiveness and meaningfulness in a set of comparisons on antisocial behavior and prior offenses measures. These findings highlight the presence of diverse patterns of risk and suggest that a limited number of specialized interventions may respond to the main needs of most institutionalized youths.

Repeated measures regression mixture models

Regression mixture models are one increasingly utilized approach for developing theories about and exploring the heterogeneity of effects. In this study we aimed to extend the current use of regression mixtures to a repeated regression mixture method when repeated measures, such as diary-type and experience-sampling method, data are available. We hypothesized that additional information borrowed from the repeated measures would improve the model performance, in terms of class enumeration and accuracy of the parameter estimates. We specifically compared three types of model specifications in regression mixtures: (a) traditional single-outcome model; (b) repeated measures models with three, five, and seven measures; and (c) a single-outcome model with the average of seven repeated measures. The results showed that the repeated measures regression mixture models substantially outperformed the traditional and average single-outcome models in class enumeration, with less bias in the parameter estimates. For sample size, whereas prior recommendations have suggested that regression mixtures require samples of well over 1,000 participants, even for classes at a large distance from each other (classes with regression weights of .20 vs. .70), the present repeated measures regression mixture models allow for samples as low as 200 participants with an increased number (i.e., seven) of repeated measures. We also demonstrate an application of the proposed repeated measures approach using data from the Sleep Research Project. Implications and limitations of the study are discussed.

A systematic review of the Second Step program

This article presents findings from a meta-analysis of 24 primary research studies on the Second Step social emotional learning program. Program content knowledge, outcomes related to prosociality, and outcomes related to antisociality are examined as outcome categories in randomized controlled trials, quasi-experimental studies and, separately, single-group repeated measures design studies. Students participating in Second Step demonstrated increased knowledge of program content and increased prosocial outcomes. Participation in the program was not associated with significant changes in antisocial outcomes. Evidence suggests the possibility of publication bias in studies reporting knowledge outcomes but no evidence of publication bias in studies reporting prosocial and antisocial outcomes. This study complements broader meta-analyses on SEL programs and aims to inform local decision making by providing an estimate of the overall effects of the Second Step program on a variety of student outcomes.

Cumulative risk over the early life course and its relation to academic achievement in childhood and early adolescence

Early-life risk factors, such as family disruption, maltreatment, and poverty, can negatively impact children's scholastic abilities; however, most previous studies have relied on cross-sectional designs and retrospective measurement. This study investigated the relation between cumulative risk factors during the early life course and subsequent academic achievement in a cohort of children and adolescents. Data for this study were based on registry-data material from the LIFECOURSE study of 1151 children from the 2000 birth cohort in Reykjavik, Iceland, assembled in 2014-2016. Multiple lifetime risk factors, including maternal smoking during pregnancy, parent's disability status, being born to a young mother, number of children in the household, family income, number of visits to school nurses, and reports of maltreatment, were assessed. Latent class analysis and Analysis of Covariance (ANCOVA) were used to predict academic achievement in the 4th and 7th grades. Individuals with no risk factors reported the highest average academic achievement in the 4th (M=66 points, SD=17) and 7th grades (M=67 points, SD=15). There was a significant main effect for 4th-grade risk factors and academic achievement (F [7, 1146]=12.06, p<0.001) and a similar relationship between the risk factor profile and achievement scores in 7th grade (F [7, 1146]=15.08, p<0.001). Each additional risk factor was associated with a drop in academic achievement at both grade levels. We conclude that academic achievement declines in proportion to the number of risk factors in early life.

Influence of inter-parental conflict on adolescent delinquency via school connectedness: Is impulsivity a vulnerability or plasticity factor?

This study examined the mediating effect of school connectedness on the relationship between inter-parental conflict and adolescent delinquency, and impulsivity's moderation of the conditional effect of school connectedness. In total, 1407 Chinese students (mean age = 12.74 years, SD = 0.57) from 4 middle schools completed anonymous questionnaires regarding inter-parental conflict, impulsivity, school connectedness, and delinquency. Path analysis revealed school connectedness was a mediator in the relationship between inter-parental conflict and adolescent delinquency. Furthermore, impulsivity moderated the indirect effect of school connectedness in the relationship between inter-parental conflict and adolescent delinquency. A simple slope plot, together with a proportion affected (PA) index, indicated that impulsivity functioned more as a plasticity, rather than a vulnerability, factor in school connectedness. When school connectedness was low, high impulsivity was associated with more delinquent behaviors; however, when it was high, high impulsivity was associated with fewer delinquent behaviors.

Impact of an equality constraint on the class-specific residual variances in regression mixtures: A Monte Carlo simulation study

Regression mixture models are a novel approach to modeling the heterogeneous effects of predictors on an outcome. In the model-building process, often residual variances are disregarded and simplifying assumptions are made without thorough examination of the consequences. In this simulation study, we investigated the impact of an equality constraint on the residual variances across latent classes. We examined the consequences of constraining the residual variances on class enumeration (finding the true number of latent classes) and on the parameter estimates, under a number of different simulation conditions meant to reflect the types of heterogeneity likely to exist in applied analyses. The results showed that bias in class enumeration increased as the difference in residual variances between the classes increased. Also, an inappropriate equality constraint on the residual variances greatly impacted on the estimated class sizes and showed the potential to greatly affect the parameter estimates in each class. These results suggest that it is important to make assumptions about residual variances with care and to carefully report what assumptions are made.