每个集群有多少个案例?具有线性结果的两级聚类随机评估中相对于最小可检测效应的每簇单位数的可操作性

IF 1.1 3区 社会学 Q2 SOCIAL SCIENCES, INTERDISCIPLINARY
E. Hedberg
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引用次数: 0

摘要

在聚类随机评估中,一种治疗或干预措施被随机分配到一组聚类中,每组聚类都有组成的单独观察单位(例如,上学的学生单位被分配到治疗中)。这些设计的一个考虑因素是每个集群需要多少个单元才能达到足够的统计能力。通常,研究人员表示,“每个集群大约30个单元”是最能提高统计精度的。为了避免不基于统计理论和实际考虑的经验法则,而是为这个问题提供指导,最小可检测效应大小(MDES)与每簇少一个单位的较大MDES的比率与聚类随机设计的关键参数相关。给出了在给定单位数下的后续差异效应大小比(SDESR)的公式,以及寻找假设的SDESR的单位数的公式。一般来说,收益递减点发生在类内相关性值较大的单位数量较少的情况下。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
How Many Cases per Cluster? Operationalizing the Number of Units per Cluster Relative to Minimum Detectable Effects in Two-Level Cluster Randomized Evaluations with Linear Outcomes
In cluster randomized evaluations, a treatment or intervention is randomly assigned to a set of clusters each with constituent individual units of observations (e.g., student units that attend schools, which are assigned to treatment). One consideration of these designs is how many units are needed per cluster to achieve adequate statistical power. Typically, researchers state that “about 30 units per cluster” is the most that will yield benefit towards statistical precision. To avoid rules of thumb not grounded in statistical theory and practical considerations, and instead provide guidance for this question, the ratio of the minimum detectable effect size (MDES) to the larger MDES with one less unit per cluster is related to the key parameters of the cluster randomized design. Formulas for this subsequent difference effect size ratio (SDESR) at a given number of units are provided, as are formulas for finding the number of units for an assumed SDESR. In general, the point of diminishing returns occurs with smaller numbers of units for larger values of the intraclass correlation.
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来源期刊
American Journal of Evaluation
American Journal of Evaluation SOCIAL SCIENCES, INTERDISCIPLINARY-
CiteScore
4.40
自引率
11.80%
发文量
39
期刊介绍: The American Journal of Evaluation (AJE) publishes original papers about the methods, theory, practice, and findings of evaluation. The general goal of AJE is to present the best work in and about evaluation, in order to improve the knowledge base and practice of its readers. Because the field of evaluation is diverse, with different intellectual traditions, approaches to practice, and domains of application, the papers published in AJE will reflect this diversity. Nevertheless, preference is given to papers that are likely to be of interest to a wide range of evaluators and that are written to be accessible to most readers.
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