Design and Statistical Power for Planning Cluster Randomized Trials Aimed at Improving Student Science Achievement and Science Teacher Outcomes (Data)
Principal Investigator(s): View help for Principal Investigator(s) Qi Zhang, Western Michigan University; Jessaca Spybrook, Western Michigan University; Fatih Unlu, RAND Corporation
Version: View help for Version V1
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Project Citation:
Zhang, Qi, Spybrook, Jessaca , and Unlu, Fatih. Design and Statistical Power for Planning Cluster Randomized Trials Aimed at Improving Student Science Achievement and Science Teacher Outcomes (Data). Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2020-05-19. https://doi.org/10.3886/E119523V1
Project Description
Summary:
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Abstract:With 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 impact design choices to power the study to detect teacher effects and vice-versa. In this study, we consider strategies to maximize the efficiency of study design when both student and teacher effects are of primary interest.
Funding Sources:
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National Science Foundation (1544236)
Scope of Project
Subject Terms:
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study design;
power analysis;
cluster randomized trial ;
science education
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