Optimal Sample Allocation in Group-Randomized Mediation Studies with a Group-Level Mediator

Cox, Kyle; Kelcey, Benjamin

doi:10.1080/00220973.2018.1496060

Cited by 7 publications

(15 citation statements)

References 36 publications

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“…Relative power did vary across models and tests when the path coefficients were misspecified with the exception of the MC interval test for which power demonstrated consistent robustness. The MC interval test result can be traced back to stable n opt 1 values when using the test even with different a and b path coefficient values (Cox & Kelcey, 2019, Kelcey & Shen, 2019. Conversely, power under the optimal sampling framework for the joint and Sobel test varied in robustness to misspecified a and b path coefficient values across models reflecting the conditions in which these parameters influenced n opt 1 values for that specific test.…”

Section: Influence Of Mediation Test and Analytic Modelmentioning

confidence: 97%

“…Additionally, the total funds (T) do not play a role in determining the optimal individual sample size so T was set as a function of group cost (T = 100c 2 ; see Cox & Kelcey, 2019).…”

Section: Power Of 2-2-1 Mediation Studiesmentioning

confidence: 99%

“…Performance of these tests in terms of power converges with large sample sizes but under sample sizes typical for studies in the social sciences there are substantial differences (e.g., Kelcey, Dong, Spybrook, & Shen, 2017). Additionally, the optimal sample allocation for a study can differ based solely on the selected mediation test (Cox & Kelcey, 2019). These factors necessitate the inclusion of multiple mediation tests representing various approaches.…”

Section: Group-level Mediatormentioning

confidence: 99%

“…Like group-randomized studies of main effects, optimal sample allocation under the Sobel test is derived by minimizing the error variance (Raudenbush, 1997). With a standardized mediator and outcome, the optimal individual sample size for the Sobel test in terms of path coefficients is (Cox & Kelcey, 2019) with the optimal sample of groups determined by substituting in .…”

Section: Group-level Mediatormentioning

confidence: 99%

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Robustness of Statistical Power in Group-Randomized Studies of Mediation Under an Optimal Sampling Framework

Cox

Kelcey

2019

Methodology

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Abstract. When planning group-randomized studies probing mediation, effective and efficient sample allocation is governed by several parameters including treatment-mediator and mediator-outcome path coefficients and the mediator and outcome intraclass correlation coefficients. In the design stage, these parameters are typically approximated using information from prior research and these approximations are likely to deviate from the true values eventually realized in the study. This study investigates the robustness of statistical power under an optimal sampling framework to misspecified parameter values in group-randomized designs with group- or individual-level mediators. The results suggest that estimates of statistical power are robust to misspecified parameter values across a variety of conditions and tests. Relative power remained above 90% in most conditions when the incorrect parameter value ranged between 50% and 150% of the true parameter.

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Section: Influence Of Mediation Test and Analytic Modelmentioning

confidence: 97%

“…Additionally, the total funds (T) do not play a role in determining the optimal individual sample size so T was set as a function of group cost (T = 100c 2 ; see Cox & Kelcey, 2019).…”

Section: Power Of 2-2-1 Mediation Studiesmentioning

confidence: 99%

Section: Group-level Mediatormentioning

confidence: 99%

Section: Group-level Mediatormentioning

confidence: 99%

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Robustness of Statistical Power in Group-Randomized Studies of Mediation Under an Optimal Sampling Framework

Cox

Kelcey

2019

Methodology

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“…Many more groups need to be randomly assigned to treatment and control conditions to have adequate power, also known as cluster-randomization in the literature. There are many scholars spearheading this line of research, and guide practitioners to design rigorous clusterrandomized trials (e.g., Bloom, 1995Bloom, , 2006Bloom et al, 1999;Bulus & Dong, 2021;Bulus & Şahin, 2019;Cox & Kelcey, 2019a, 2019bDong, Kelcey, & Spybrook, 2017;Dong & Maynard, 2013;Dong, Kelcey, & Spybrook, 2017;Kelcey, Dong, Spybrook, & Cox, 2017;Kelcey, Dong, Spybrook, & Shen, 2017;Konstantopoulos, 2009Konstantopoulos, , 2011Konstantopoulos, , 2013Raudenbush, 1997;Raudenbush & Liu, 2000;and many others). There are publicly available software tools that implement results from these studies to assist with the design of cluster-randomized trials (e.g., PowerUp!, Dong & Maynard, 2013;PowerUpR, Bulus et al, 2019;OD+, Spybrook et al, 2011).…”

Section: (Block) Randomize and Adjust For Baseline Differencesmentioning

confidence: 99%

Statistical power and precision of experimental studies originated in the Republic of Turkey from 2010 to 2020: Current practices and some recommendations

Buluş

Koyuncu

2021

Participatory Educational Research

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This study systematically reviews randomly selected 155 experimental studies in education field originated in the Republic of Turkey between 2010 and 2020. Indiscriminate choice of sample size in recent publications prompted us to evaluate their statistical power and precision. First, above and beyond our review, we could not identify any large-scale experiments such as cluster-randomized or multisite randomized trials, which overcome shortcomings of small-scale experiments, better suit to the organizational structure of the education field, nevertheless require far greater effort and financial resources. Second, none of the small-scale experiments has reported or conducted ex-ante power analysis. Third, results indicate that studies are sufficiently powered to detect medium effects and above (Cohen's d ≥ 0.50), however they are underpowered to detect small effects (Cohen's d ≤ 0.20). Trends in the past ten years indicate precision remained unchanged. We made several recommendations to increase the precision of experimental designs and improve their evidential values: Determine sample size prior to an experiment with power analysis routine, randomize subjects / clusters to obtain unbiased estimates, collect pre-test information and other relevant covariates, adjust for baseline differences beyond covariate control, document attrition, report standardized treatment effect and standardized variance parameters. Findings should be interpreted considering minimum effects in education that are relevant to education policy and practice.

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Using Ant Colony Optimization to Identify Optimal Sample Allocations in Cluster-Randomized Trials

Shen,

Leite,

Zhang

et al. 2024

The Journal of Experimental Education

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Optimal Sample Allocation in Group-Randomized Mediation Studies with a Group-Level Mediator

Cited by 7 publications

References 36 publications

Robustness of Statistical Power in Group-Randomized Studies of Mediation Under an Optimal Sampling Framework

Robustness of Statistical Power in Group-Randomized Studies of Mediation Under an Optimal Sampling Framework

Statistical power and precision of experimental studies originated in the Republic of Turkey from 2010 to 2020: Current practices and some recommendations

Using Ant Colony Optimization to Identify Optimal Sample Allocations in Cluster-Randomized Trials

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