Optimal sample allocation in multisite randomized trials

Shen, Zuchao; Kelcey, Benjamin

doi:10.1080/00220973.2020.1830361

Cited by 8 publications

(6 citation statements)

References 25 publications

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“…To facilitate end-user calculations, we have developed a freely available R package odr (Shen & Kelcey, 2020) that implements the proposed framework. The package also can perform power analysis accommodating costs by default (e.g., required budget/sample size calculation, power calculation under a given budget, MDES calculation under a given budget) and conventional power analysis (e.g., sample size, power, and MDES calculation).…”

Section: Discussionmentioning

confidence: 99%

“…The resulting converged values of p and n in the final iteration capture the sampling plan that jointly optimizes over these parameters. We implement these solutions in the R package odr (Shen & Kelcey, 2020).…”

Section: Methodsmentioning

confidence: 99%

“…However, we can solve the multivariate partial derivatives numerically. We implement these solutions in the R package odr (Shen & Kelcey, 2020).…”

Section: Optimal Sample Allocation In Three-level Cluster-randomized mentioning

confidence: 99%

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Optimal Sample Allocation Under Unequal Costs in Cluster-Randomized Trials

Shen

Kelcey

2020

Journal of Educational and Behavioral Statistics

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Conventional optimal design frameworks consider a narrow range of sampling cost structures that thereby constrict their capacity to identify the most powerful and efficient designs. We relax several constraints of previous optimal design frameworks by allowing for variable sampling costs in cluster-randomized trials. The proposed framework introduces additional design considerations and has the potential to identify designs with more statistical power, even when some parameters are constrained due to immutable practical concerns. The results also suggest that the gains in efficiency introduced through the expanded framework are fairly robust to misspecifications of the expanded cost structure and concomitant design parameters (e.g., intraclass correlation coefficient). The proposed framework is implemented in the R package odr.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Optimal Sample Allocation Under Unequal Costs in Cluster-Randomized Trials

Shen

Kelcey

2020

Journal of Educational and Behavioral Statistics

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“…Ninth-grade students (14 to 15 years old) from a public middle school in Spain participated in the study. The school was selected by cluster sampling [31] from among the population of public middle schools in the Valencian community. Of the total of 112 students who participated, 50.47% were female and 49.53% were male.…”

Section: Samplementioning

confidence: 99%

Peer Tutoring and Math Digital Tools: A Promising Combination in Middle School

2022

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Peer tutoring in combination with math digital tools was employed with middle school students learning mathematics. A total of 112 students in 9th grade (14 to 15 years old) participated in the study. A pretest–posttest with control group design was used. Students worked with systems of linear equations during the experience. The effects of the intervention of peer tutoring in combination with math digital tools on students’ mathematics achievement were examined using quantitative methods. The way students in the experimental group learned and their motivation towards using digital tools compared with students in the control group were analysed qualitatively. Statistically significant improvements and a large effect size were reported for students’ mathematics achievement in the experimental group. No statistically significant differences were reported between the pretest and the posttest for the control group. The qualitative analyses revealed that students in the experimental group achieved a higher level of autonomous learning, showed a greater association of mathematical concepts, helped their peers more, did more exercises and problems than students in the control group, and enjoyed the experience.

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“…Besides covariate adjustment, another strategy to improve design precision and efficiency is to identify the most efficient sampling plan in an optimal design framework by additionally leveraging the information about the costs of sampling a unit (Hedges & Borenstein, 2014; Konstantopoulos, 2009, 2011; Liu, 2003; Raudenbush, 1997; Shen & Kelcey, 2020, 2022b, 2022c). Optimal design frameworks simultaneously address statistical power and design efficiency.…”

Section: Introductionmentioning

confidence: 99%

Intraclass Correlations for Evaluating the Effects of Teacher Empowerment Programs on Student Educational Outcomes

Shen

Curran

You

et al. 2022

Educational Evaluation and Policy Analysis

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Programs that improve teaching effectiveness represent a core strategy to improve student educational outcomes and close student achievement gaps. This article compiles empirical values of intraclass correlations for designing effective and efficient experimental studies evaluating the effects of these programs. The Early Childhood Longitudinal Study, Kindergarten Class of 1998–1999 (ECLS-K) data are used to estimate empirical design parameter values in the setting of students nested within teachers nested within schools. The results show that intraclass correlations at the school level are relatively stable, and those at the teacher level increase along with grade level. We illustrate the utility of the results by demonstrating how to leverage this information to design more efficient and effective experimental studies.

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Optimal sample allocation in multisite randomized trials

Cited by 8 publications

References 25 publications

Optimal Sample Allocation Under Unequal Costs in Cluster-Randomized Trials

Optimal Sample Allocation Under Unequal Costs in Cluster-Randomized Trials

Peer Tutoring and Math Digital Tools: A Promising Combination in Middle School

Intraclass Correlations for Evaluating the Effects of Teacher Empowerment Programs on Student Educational Outcomes

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