Ang, Rebecca P.

"This report presents the findings of the evaluation of the CARE Powercharged Program delivered to Secondary 1 Normal Technical students in three schools in 2005. ... In Singapore, there are few evaluations conducted on school-based intervention programs. It is necessary to evaluate these programs to ascertain if there are any impacts and what they are. At the same time, there is limited research on students in the Normal Technical stream. Evaluation and research are particularly important as 1) we need to understand students' learning needs, and 2) there is strong interest in understanding, strengthening and improving the learning experience of students in Normal Technical classes in the current policy climate."-- [p. 1] of executive summary.

Cyberbullying victimization and mental health symptoms are major concerns for children and adolescents worldwide. Despite the increasing number of longitudinal studies of cyberbullying and mental health among this demographic, the robustness of the causal associations between cyberbullying victimization and the magnitude of mental health symptoms remains unclear. This meta-analysis investigated the longitudinal impact of cyberbullying victimization on mental health symptoms among children and adolescents. A systematic search identified primary studies published in English between January 2010 and June 2021, yielding a sample of 27 studies encompassing 13,497 children and adolescents aged 8 to 19 years old. The longitudinal association between cyberbullying victimization and mental health symptoms among children and adolescents was found to be weakly positive and consistent across time and age. Three significant moderators were identified: the effect of cyberbullying victimization on mental health was larger among older children, groups with a higher proportion of males, and in more recent publications. No evidence of publication bias was detected. This study adds to the existing body of research by providing a new perspective on the long-term effects of cyberbullying victimization on the mental health of children and adolescents’ mental health. Furthermore, it underscores the necessity of developing effective cyberbullying prevention programs, interventions, and legal regulations to comprehensively address this issue.

Youth violence is a growing concern in Singapore. To address this complex social issue, we surveyed the psychology, social science, and criminology literature to identify a total of 11 intrinsic (familial, individual, school) and 2 extrinsic (peer) factors linked to youth violence, and also their interdependencies. We then developed a complex agent network model where each complex agent is represented by a complex factor network of the 13 factors along with youth violence, coupled to each other through the extrinsic factors to form a complex social network. We simulated the model using as initial conditions the results from a large-scale school-based survey of the factors and random social ties. We find factors in each complex agent evolving with time under the influences from other factors, and the social ties between agents evolving with time as a result of behavioral imitation between agents. We ran a sensitivity analysis on the model, to find that the model is most sensitive to the parameters linking (1) non-intact family, (2) delinquency in general, (3) school disengagement, (4) peer delinquency, and (5) friends in gang to gang involvement. We also ran a series of intervention scenario simulations, and our results show that it is critical to intervene early, and successful interventions work by tipping the balance between competing intrinsic and extrinsic factors. Mental health professionals and school counsellors can then apply this unique insight from the model to design more effective interventions.

The Dark Triad (i.e., narcissism, psychopathy, Machiavellianism) has garnered intense attention over the past 15 years. We examined the structure of these traits’ measure—the Dark Triad Dirty Dozen (DTDD)—in a sample of 11,488 participants from three W.E.I.R.D. (i.e., North America, Oceania, Western Europe) and five non-W.E.I.R.D. (i.e., Asia, Middle East, non-Western Europe, South America, sub-Saharan Africa) world regions. The results confirmed the measurement invariance of the DTDD across participants’ sex in all world regions, with men scoring higher than women on all traits (except for psychopathy in Asia, where the difference was not significant). We found evidence for metric (and partial scalar) measurement invariance within and between W.E.I.R.D. and non-W.E.I.R.D. world regions. The results generally support the structure of the DTDD.

How well adolescents get along with others such as peers and teachers is an important aspect of adolescent development. Current research on adolescent relationship with peers and teachers is limited by classical methods that lack explicit test of predictive performance and cannot efficiently discover complex associations with potential non-linearity and higher-order interactions among a large set of predictors. Here, a transparently reported machine learning approach is utilized to overcome these limitations in concurrently predicting how well adolescents perceive themselves to get along with peers and teachers. The predictors were 99 items from four instruments examining internalizing and externalizing psychopathology, sensation-seeking, peer pressure, and parent-child conflict. The sample consisted of 3232 adolescents (M = 14.0 years, SD = 1.0 year, 49% female). Nonlinear machine learning classifiers predicted with high performance adolescent relationship with peers and teachers unlike classical methods. Using model explainability analyses at the item level, results identified influential predictors related to somatic complaints and attention problems that interacted in nonlinear ways with internalizing behaviors. In many cases, these intrapersonal predictors outcompeted in predictive power many interpersonal predictors. Overall, the results suggest the need to cast a much wider net of variables for understanding and predicting adolescent relationships, and highlight the power of a data-driven machine learning approach with implications on a predictive science of adolescence research.

This study explored the association between parental arrest and two functions of aggression (i.e., reactive and proactive aggression) among 797 Singaporean adolescents. The moderating effects of psychopathic traits and gender were also investigated in the parental arrest-reactive/proactive aggression link. Parental arrest was found to be significantly and positively related to both reactive and proactive aggression, especially proactive aggression. Psychopathic traits strengthened the link between parental arrest and proactive aggression in adolescents with stronger effects for girls in this Singaporean sample. These findings point to the importance of early intervention with respect to girls who display high levels of psychopathic traits, especially when they also have parents with a prior arrest history, so as to reduce their levels of proactive aggression.

This study examines the influence of both student and teacher perception of the student-teacher relationship on student's attitude towards teachers and school. It also seeks to explore any gender differences in the perception of teacher-student relationship between male and female adolescents. A sample of 1,266 students (541 girls and 725 boys) from six different middle schools in Singapore participated in this study. Findings indicated that gender differences were observed for certain dimensions in the teacher-student relationship predicting their attitude towards teachers and school. Possible explanations for the obtained results were suggested and implications of the findings were also discussed.