Impact of a School-Based Multicomponent Positive Psychology Intervention on Adolescents’ Time Attitudes: A Latent Transition Analysis

Time attitudes, which refer to positive and negative feelings towards the past, present, and future, are a salient phenomenon in the developmental stage of adolescence and have been related to better well-being. Positive feelings towards time can be promoted in the school setting through empirically validated positive psychology interventions. However, the extent to which these interventions impact the time attitudes of adolescents remains unknown. The current study investigated the influence of a multicomponent positive psychology intervention on adolescents’ transitions between time attitude profiles and how these transitions are related to their emotional, social, and psychological well-being. Participants consisted of 220 (M = 14.98; 47.3% female) adolescents from two Spanish high schools who participated in the six-week Get to Know Me+ program. Adolescents’ time attitudes and well-being were measured via the Adolescents and Adult Time Inventory–Time Attitudes and the Mental Health Continuum–Short Form, respectively, at pre- and postintervention. Participants were clustered in different profiles through a latent profile analysis, and the transitions were analyzed using a latent transition analysis. Five profiles were identified (negative, present/future negative, past negative, optimistic, and positive), and results indicated that adolescents who participated in the intervention were more likely to transition to positive profiles (optimistic and positive) and generally reported higher well-being, especially those in the negative, present/future negative, and optimistic profiles. Preliminary evidence showed that school-based multicomponent positive psychology interventions can have a positive impact on adolescents’ feelings towards time and well-being.

Dual-Factor Mental Health from Childhood to Early Adolescence and Associated Factors: A Latent Transition Analysis

The dual-factor model of mental health indicates the importance of simultaneously assessing symptoms and subjective wellbeing, but there is limited understanding of how dual-factor mental health changes during the transition from childhood to early adolescence and factors associated with change. The current study investigated dual-factor mental health over a 2-year period from when children were 8–9 years old to 10–11 years old (N = 2402; 48% female), using latent transition analysis. Further analyses determined whether sex and peer support were associated with initial mental health status or specific transitions during this period. Following class enumeration procedures, a 5-class model was selected at both timepoints. Classes were: (1) complete mental health, (2) vulnerable, (3) emotional symptoms but content, (4) conduct problems but content, and (5) troubled. Half of the sample changed mental health status during the study period. Sex and peer support were associated with specific mental health statuses and subsequent transitions. The findings have implications for mental health screening practice and identifying those in need of targeted interventions.

Changes in Consumption Expenditures among Baby-boomers and Young-olds: Latent Transition Analysis

The aim of this study was to examine the longitudinal transition of consumption expenditures among both baby-boomers and young-olds in South Korea. We used data from the 6th (2016) and the 7th (2018) waves of the Korean Longitudinal Study of Ageing (KLoSA). The final sample comprised 1,806 baby-boomers (age range=53-61 in 2016) and 1,483 young-olds (age range=65-74 in 2016). Consumption expenditures were observed with nine types of expenses: food, eating out, public education, private education, housing, health-care, clothing, cultural entertainment, and savings. According to the results from latent transition analysis (LTA), three consumption subgroups were identified among baby-boomers: “non-expenditure for education (NE, 69.7%)” group, “high-public education expenditures (PE, 10.7%)” group, and “high-public and private education expenditures (PPE, 19.6%)” group. For baby-boomers, NE and PE were more likely to remain the same type throughout the two waves, and PPE was most likely to move to NE two years later. Meanwhile, the consumption expenditures of young-olds were divided into “low-saving (LS, 63.7%)” group, “high-saving (HS, 40%)” group, and “education cost-centered (EC, 5.3%)” group. In the case of young-olds, the transition between the groups was unlikely to occur across the two waves which can be interpreted as having fewer life cycle changes than baby-boomers. This study suggests that it is necessary to take into account the difference between the generations when understanding longitudinal transition of consumption expenditures.

A Simulation Study on Latent Transition Analysis for Examining Profiles and Trajectories in Education: Recommendations for Fit Statistics

Latent transition analysis is an informative statistical tool for depicting heterogeneity in learning as latent profiles. We present a Monte Carlo simulation study to guide researchers in selecting fit indices for identifying the correct number of profiles. We simulated data representing profiles of learners within a typical pre- post- follow up-design with continuous indicators, varying sample size (N from 50 to 1000), attrition rate (none/10% per wave), and profile separation (entropy; from .73 to .87). Results indicate that the most commonly used fit index, the Bayesian information criterion (BIC), and the consistent Akaike information criterion (CAIC) consistently underestimate the real number of profiles. A combination of the AIC or the AIC3 with the adjusted Bayesian Information Criterion (aBIC) provides the most precise choice for selecting the number of profiles and is accurate with sample sizes of at least N = 200. The AIC3 excels starting from N = 500. Results were mostly robust towards differing numbers of time points, profiles, indicator variables, and alternative profiles. We provide an online tool for computing these fit indices and discuss implications for research.