Experimental Design and Statistical Power for Cluster Randomized Cost-Effectiveness Trials

Li, Wei; Dong, Nianbo; Maynard, Rebecca; Spybrook, Jessaca; Kelcey, Ben

doi:10.1080/19345747.2022.2142177

Journal of Research on Educational Effectiveness

2022

DOI: 10.1080/19345747.2022.2142177

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Experimental Design and Statistical Power for Cluster Randomized Cost-Effectiveness Trials

Wei Li

Nianbo Dong

Rebecca Maynard

et al.

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2024

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Cited by 4 publications

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“…Finally, the last three articles cover some selected but important topics in causal inference. Li et al (2024) discuss aspects of cluster randomized trials in education research, Qin (2024) provides an introduction to causal mediation analysis, and Shear and Briggs (2024) address measurement issues in causal inference.…”

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confidence: 99%

Causal research designs and analysis in education

Steiner,

Kim

2024

Asia Pacific Educ. Rev.

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Causal inference in education research is crucial for assessing the effectiveness of educational policies, programs, and interventions. Establishing causal relations, that is, identifying interventions that actually work in practice, helps policymakers, educators, and researchers implement strategies that are genuinely beneficial, ensuring resources are allocated efficiently to enhance educational experiences and outcomes. Without rigorous causal inference based on experimental or quasi-experimental designs, efforts to improve education might rely on practices that appear effective but fail to produce actual benefits when scaled or applied in different contexts.The main challenge with causal inference is that data alone are insufficient to reliably assess whether a causal relation between two variables of interest-the treatment conditions and the outcome-exists. In other words, data are uninformative about the causal relation of observed variables (Cunningham, 2021;Pearl & Mackenzie, 2018). Causal inference needs more than data. It also needs reliable * Peter M. Steiner

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confidence: 99%

Causal research designs and analysis in education

Steiner,

Kim

2024

Asia Pacific Educ. Rev.

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Design and analysis of cluster randomized trials

Li,

Xie,

Pham

et al. 2024

Asia Pacific Educ. Rev.

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Advantages of Monte Carlo Confidence Intervals for Incremental Cost-Effectiveness Ratios: A Comparison of Five Methods

Dong,

Maynard,

Kelcey

et al. 2024

Journal of Research on Educational Effectiveness

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Experimental Design and Statistical Power for Cluster Randomized Cost-Effectiveness Trials

Cited by 4 publications

References 56 publications

Causal research designs and analysis in education

Causal research designs and analysis in education

Design and analysis of cluster randomized trials

Advantages of Monte Carlo Confidence Intervals for Incremental Cost-Effectiveness Ratios: A Comparison of Five Methods

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