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[http://journals.sagepub.com/home/ero](http://journals.sagepub.com/home/ero)  \nExamining Design and Statistical Power for Planning Cluster Randomized Trials Aimed at Improving Student Science Achievement  \nand Science Teacher Outcomes  \nQi Zhang  \nJessaca Spybrook  \nWestern Michigan University  \nFatih Unlu  \nRAND  \nWith the increasing demand for evidence-based research on teacher effectiveness and improving student achievement, more impact studies are being conducted to examine the effectiveness of professional development (PD) interventions. Cluster randomized trials (CRTs) are often carried out to assess PD interventions that aim to improve both teacher and student outcomes. Due to the different design parameters (i.e., intraclass correlation and R2) and benchmark effect sizes associated with the student and teacher outcomes, two power analyses are necessary for planning CRTs that aim to detect both teacher and student effects in one study. These two power analyses are often conducted separately without considering how design choices to power the study to detect student effects may affect design choices to power the study to detect teacher effects and viceversa. In this study, we consider strategies to maximize the efficiency of the study design when both student and teacher effects are of primary interest.  \nKeywords: study design, power analysis, cluster randomized trial, science education  \nIntroduction  \nThere has been a strong push for research on interventions aimed at improving student science outcomes in the United States in the past decade. However, the number of studies conducted to examine the efficacy of math and literacy interventions on student outcomes still greatly exceeds those that evaluated science interventions. For instance, What Works Clearinghouse (WWC) reviewed 1,437 studies under the mathematics topic and 4,587 studies under the literacy topic since 2002. In contrast, WWC reviewed only 73 studies under the science topic in the same time frame. This strongly indicates that more studies of science interventions are needed.  \nNot only are more studies evaluating science interventions needed but they should be of high quality. In a metaanalysis of science intervention effect sizes, Taylor et al.(2018) found fewer than 2% of the reviewed studies, which included randomized controlled trials (RCTs) and quasiexperimental designs (QEDs), had adequate sample sizesand used well-designed and well-implemented rigorous research methods to detect causal effects. This indicates that research grounded in strong methodology is also much needed in science education.  \nImpact Studies to Improve Student Outcomes  \nIn the past decade, important federal policies were established to support the growing demand to improve science education in K–12 settings and the increasing need for rigorous research on science education. The National Research Council, the National Science Teacher Association, and the American Association for the Advancement of Science introduced the Next Generation Science Standards (NGSS) in 2011, which is a multistate effort to standardize science curriculum and science teaching in the United States (National Research Council, 2012) . NGSS has gained a great amount of traction since its debut. As of 2019, 20 states adopted NGSS into their Common Core State Standards, further providing clear guidelines for the knowledge students need to be prepared for success in college. More states are expected to implement NGSS in the upcoming years.  \nAround the same time as NGSS launch, the Institute of Educational Sciences (IES), the research branch of the U.S. Department of Education, and the National Science Foundation, joined forces to create the Common Guidelines for Educati","cbCaiak4Kqup0HwY","https://ap.wps.com/l/cbCaiak4Kqup0HwY","pdf",529419,12,"English","# Impact Studies to Improve Student Outcomes\n## Evidence-based demands for science education interventions\n## Power analysis considerations for cluster randomized trials","[{\"question\":\"Why are two power analyses needed for cluster randomized trials in this article?\",\"answer\":\"Because student and teacher outcomes have different design parameters, including intraclass correlation and R2, and different benchmark effect sizes. Detecting both effects in one study therefore requires considering each outcome separately.\"},{\"question\":\"What problem does the article identify with how power analyses are commonly conducted?\",\"answer\":\"Common practice often runs the student-effect and teacher-effect power analyses separately, without accounting for how design choices made for one target may affect the design choices needed for the other.\"},{\"question\":\"What strategies does the article propose for improving study efficiency?\",\"answer\":\"The article considers approaches to maximize the efficiency of the study design when both student and teacher effects are primary interests.\"}]","AERA Open - Vol. 6, No. 3 - Examining Design and Statistical Power for Planning Cluster Randomized Trials | PDF",1792068593]