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Background:Evaluation studies frequently draw on fallible outcomes that contain significant measurement error. Ignoring outcome measurement error in the planning stages can undermine the sufficiency and efficiency of an otherwise well-designed study and can further constrain the evidence studies bring to bear on the effectiveness of programs. Objectives:We develop simple formulas to adjust statistical power, minimum detectable effect (MDE), and optimal sample allocation formulas for two-level cluster- and multisite-randomized designs when the outcome is subject to measurement error. Results:The resulting adjusted formulas suggest that outcome measurement error typically amplifies treatment effect uncertainty, reduces power, increases the MDE, and undermines the efficiency of conventional optimal sampling schemes. Therefore, achieving adequate power for a given effect size will typically demand increased sample sizes when considering fallible outcomes, while maintaining design efficiency will require increasing portions of a budget be applied toward sampling a larger number of individuals within clusters. We illustrate evaluation planning with the new formulas while comparing them to conventional formulas using hypothetical examples based on recent empirical studies. To encourage adoption of the new formulas, we implement them in the R package PowerUpR and in the PowerUp software.
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Cluster sampling for Morris method made easy
Abstract In this paper we provide a thorough investigation of the cluster sampling scheme for Morris' elementary effects method (MM), a popular model‐free factor screening method originated in the setting of design and analysis of computational experiments. We first study the sampling mechanism underpinning the two sampling schemes of MM (i.e., cluster sampling and noncluster sampling) and unveil its nature as a two‐level nested sampling process. This in‐depth understanding sets up a foundation for tackling two important aspects of cluster sampling: budget allocation and sampling plan. On the one hand, we study the budget allocation problem for cluster sampling under the analysis of variance framework and derive optimal budget allocations for efficient estimation of the importance measures. On the other hand, we devise an efficient cluster sampling algorithm with two variants to achieve enhanced statistical properties. The numerical evaluations demonstrate the superiority of the proposed cluster sampling algorithm and the budget allocations derived (when used both separately and in conjunction) to existing cluster and noncluster sampling schemes.
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- PAR ID:
- 10452944
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Naval Research Logistics (NRL)
- Volume:
- 68
- Issue:
- 4
- ISSN:
- 0894-069X
- Page Range / eLocation ID:
- p. 412-433
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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