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Teachers frequently express stress associated with teaching in large classrooms. Despite the timehonored tradition in teacher stress research of treating class size as a job-related stressor, the underlying premise that class size directly impacts teachers' stress reactions remains untested. In this randomized controlled experiment targeted at preservice teachers, we utilized a standardized virtual reality (VR) classroom to examine whether class size (number of student avatars) directly affected physiological (heart rate) or psychological (subjective rating) stress reactions among 65 preservice teachers. Results from linear mixed-effects modeling (LMM) showed that class size significantly predicted both their physiological and psychological stress reactions in the simulated environment: Average heart rate and subjective stress ratings were both significantly higher in the large class size condition. Further investigations into the causes of this association has been proposed. These findings may contribute to a better understanding of the effects of classroom features on preservice teachers' emotional experiences and well-being.
Video is a widely used medium in teacher training for situating student teachers in classroom scenarios. Although the emerging technology of virtual reality (VR) provides similar, and arguably more powerful, capabilities for immersing teachers in lifelike situations, its benefits and risks relative to video formats have received little attention in the research to date. The current study used a randomized pretest-posttest experimental design to examine the influence of a video- versus VR-based task on changing situational interest and self-efficacy in classroom management. Results from 49 student teachers revealed that the VR simulation led to higher increments in self-reported triggered interest and self-efficacy in classroom management, but also invoked higher extraneous cognitive load than a video viewing task. We discussed the implications of these results for pre-service teacher education and the design of VR environments for professional training purposes. Practitioner notes What is already known about this topic Video is a popular teacher training medium given its ability to display classroom situations. Virtual reality (VR) also immerses users in lifelike situations and has gained popularity in recent years. Situational interest and self-efficacy in classroom management is vital for student teachers' professional development. What this paper adds VR outperforms video in promoting student teachers' triggered interest in classroom management. Student teachers felt more efficacious in classroom management after participating in VR. VR also invoked higher extraneous cognitive load than the video. Implications for practice and/or policy VR provides an authentic teacher training environment for classroom management. The design of the VR training environment needs to ensure a low extraneous cognitive load.
The present study proposes and tests pathways by which racial discrimination might be positively related to bullying victimization among Black and White adolescents. Data were derived from the 2016 National Survey of Children's Health, a national survey that provides data on children's physical and mental health and their families. Data were collected from households with one or more children between June 2016 to February 2017.
A letter was sent to randomly selected households, who were invited to participate in the survey. The caregivers consisted of 66.9% females and 33.1% males for the White sample, whose mean age was 47.51 (SD = 7.26), and 76.8% females and 23.2% males for the Black sample, whose mean age was 47.61 (SD = 9.71).
In terms of the adolescents, 49.0% were females among the White sample, whose mean age was 14.73 (SD = 1.69). For Black adolescents, 47.9% were females and the mean age was 14.67(SD = 1.66).
Measures for the study included bullying perpetration, racial discrimination, academic disengagement, and socio-demographic variables of the parent and child.
Analyses included descriptive statistics, bivariate correlations, and structural path analyses.
For adolescents in both racial groups, racial discrimination appears to be positively associated with depression, which was positively associated with bullying perpetration. For White adolescents, racial discrimination was positively associated with academic disengagement, which was also positively associated with bullying perpetration. For Black adolescents, although racial discrimination was not significantly associated with academic disengagement, academic disengagement was positively associated with bullying perpetration.
Wages and wage dynamics directly affect individuals' and families' daily lives. In this article, we show how major theoretical branches of research on wages and inequality-that is, cumulative advantage (CA), human capital theory, and the lifespan perspective-can be integrated into a coherent statistical framework and analyzed with multilevel dynamic structural equation modeling (DSEM). This opens up a new way to empirically investigate the mechanisms that drive growing inequality over time. We demonstrate the new approach by making use of longitudinal, representative U.S. data (NLSY-79). Analyses revealed fundamental between-person differences in both initial wages and autoregressive wage growth rates across the lifespan. Only 0.5% of the sample experienced a "strict" CA and unbounded wage growth, whereas most individuals revealed logarithmic wage growth over time. Adolescent intelligence and adult educational levels explained substantial heterogeneity in both parameters. We discuss how DSEM may help researchers study CA processes and related developmental dynamics, and we highlight the extensions and limitations of the DSEM framework.
Background:
Using the internet to search for information or share images about self-harm is an emerging risk among young people. The aims of this study were (a) to analyze the prevalence of different types of self-harm on the internet and differences by sex and age, and (b) to examine the relationship of self-harm on the internet with intrapersonal factors (i.e., depression and anxiety) and interpersonal factors (i.e., family cohesion and social resources).
Method:
The sample consisted of 1,877 adolescents (946 girls) between 12 and 17 years old (Mage = 13.41, SD = 1.25) who completed self-report measures.
Results:
Approximately 11% of the participants had been involved in some type of self-harm on the internet. The prevalence was significantly higher among girls than boys and among adolescents older than 15 years old. Depression and anxiety increased the risk of self-harm on the internet, whereas family cohesion decreased the probability of self-harm on the internet.
Conclusions:
Self-harm on the internet is a relatively widespread phenomenon among Spanish adolescents. Prevention programs should include emotional regulation, coping skills, and resilience to reduce in this behavior.
Descriptive analyses of socially important or theoretically interesting phenomena and trends are a vital component of research in the behavioral, social, economic, and health sciences.
Such analyses yield reliable results when using representative individual participant data (IPD) from studies with complex survey designs, including educational large-scale assessments (ELSAs) or social, health, and economic survey and panel studies. The meta-analytic integration of these results offers unique and novel research opportunities to provide strong empirical evidence of the consistency and generalizability of important phenomena and trends.
Using ELSAs as an example, this tutorial offers methodological guidance on how to use the two-stage approach to IPD meta-analysis to account for the statistical challenges of complex survey designs (e.g., sampling weights, clustered and missing IPD), first, to conduct descriptive analyses (Stage 1), and second, to integrate results with three-level meta-analytic and meta-regression models to take into account dependencies among effect sizes (Stage 2).
The two-stage approach is illustrated with IPD on reading achievement from the Programme for International Student Assessment (PISA). We demonstrate how to analyze and integrate standardized mean differences (e.g., gender differences), correlations (e.g., with students' socioeconomic status [SES]), and interactions between individual characteristics at the participant level (e.g., the interaction between gender and SES) across several PISA cycles.
All the datafiles and R scripts we used are available online. Because complex social, health, or economic survey and panel studies share many methodological features with ELSAs, the guidance offered in this tutorial is also helpful for synthesizing research evidence from these studies.