Search

Cluster-randomized trials have been widely used to evaluate the treatment effects of interventions on student outcomes. When interventions are implemented by teachers, researchers need to account for the nested structure in schools (i.e., students are nested within teachers nested within schools). Schools usually have a very limited number of teachers in each grade. This limited teacher-level sample size may pose potential challenges for study design, analysis, and reporting. The current study aims to evaluate the impacts of small teacher-level sample sizes on study design, analysis, and reporting in cluster-randomized trials. Through a simulation study, we show that the intraclass correlation coefficients at the teacher and school levels are prone to biased estimates in many cases, especially when the student-level sample size is also small. One design strategy we recommend is to sample a large number of students per teacher as this will alleviate the impacts of small teacher-level sample sizes. We recommend that researchers use the total variance, if available, rather than school-level variance to report standardized treatment effects for a meta-analysis. When the total variance is not available for all studies, researchers should use a school-level variance in two-level models as the base for standardization to reduce biases. [ABSTRACT FROM AUTHOR]

Due to the adverse health effects of PM2.5 (particles with a diameter less than 2.5 μm), the national ambient air quality standards (NAAQS) for PM2.5 have been established in many countries. Ambient PM2.5 concentrations are monitored to determine compliance with the NAAQS, where beta attenuation monitors (BAMs) are widely used as a reference method. However, the high costs of installation and maintenance of BAMs result in difficulties in enhancing the spatial resolution of PM2.5 monitoring. In this context, low-cost PM2.5 sensors (LCSs) have been widely deployed for PM2.5 measurements. Despite their low cost and ease of installation, concerns regarding the data reliability of LCSs remain unresolved, necessitating further field research under various environmental conditions. In this study, we conducted a data comparability assessment between 125 LCSs and two BAMs over 12 months. Daily average PM2.5 concentrations measured by LCSs generally agreed with those from BAMs. However, when comparing hourly PM2.5 data, we found that the data comparability of LCSs against BAMs was lower than that of the daily average data, primarily influenced by ambient PM2.5 levels. Additionally, we found statistical differences in PM2.5 concentrations among six regions within the study area. This indicates that ambient PM2.5 levels can vary within a small area, supporting the necessity of monitoring PM2.5 on a small scale. Overall, the results of this study suggest that the sensors demonstrate both potential and limitations in PM2.5 monitoring. Therefore, to use LCSs for PM2.5 monitoring, it is necessary to appropriately select the averaging time and PM2.5 concentration ranges, considering local environmental conditions and the inherent characteristics of LCSs. [ABSTRACT FROM AUTHOR]