| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w29843 |
| 来源ID | Working Paper 29843 |
| Highly Powered Analysis Plans | |
| Michael L. Anderson; Jeremy Magruder | |
| 发表日期 | 2022-03-14 |
| 出版年 | 2022 |
| 语种 | 英语 |
| 摘要 | Formal analysis plans limit false discoveries by registering and multiplicity adjusting statistical tests. As each registered test reduces power on other tests, researchers prune hypotheses based on prior knowledge, often by combining related indicators into evenly-weighted indices. We propose two improvements to maximize learning within these types of analysis plans. First, we develop data-driven optimized indices that can yield more powerful tests than evenly-weighted indices. Second, we discuss organizing the logical structure of an analysis plan into a gated tree that directs type I error towards these high-powered tests. In simulations we show that researchers may prefer these "optimus gates" across a wide range of data-generating processes. We then assess our strategy using the community-driven development (CDD) application from Casey et al. (2012) and the Oregon Health Insurance Experiment from Finkelstein et al. (2012). We find substantial power gains in both applications, meaningfully changing the conclusions of Casey et al. (2012). |
| 主题 | Econometrics ; Estimation Methods ; Data Collection ; Experimental Design ; Development and Growth ; Development |
| URL | https://www.nber.org/papers/w29843 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/587515 |
| 推荐引用方式 GB/T 7714 | Michael L. Anderson,Jeremy Magruder. Highly Powered Analysis Plans. 2022. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w29843.pdf（611KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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