| 来源类型 | Publication
|
| Analyzing Grouped Administrative Data for RCTs Using Design-Based Methods |
| Peter Z. Schochet
|
| 发表日期 | 2019-06-12
|
| 出版者 | Journal of Educational and Behavioral Statistics (online ahead of print, subscription required)
|
| 出版年 | 2019
|
| 语种 | 英语
|
| 概述 | This article discusses estimation of average treatment effects for randomized controlled trials (RCTs) using grouped administrative data to help improve data access.", |
| 摘要 | This article discusses estimation of average treatment effects for randomized controlled trials (RCTs) using grouped administrative data to help improve data access. The focus is on design-based estimators, derived using the building blocks of experiments, that are conducive to grouped data for a wide range of RCT designs, including clustered and blocked designs, and models with weights and covariates. Because of the linearity of the regression model underlying RCTs, the asymptotic properties of design-based estimators using group-level averages—formed randomly or by covariates for nonclustered designs and as cluster-level averages for clustered designs—match those using individual data. Furthermore, design effects from aggregation are tolerable with moderate numbers of groups and few covariates, suggesting little information is lost in these cases. Ecological inference methods for subgroup analyses, however, yield large design effects. Several empirical examples using real-world education RCT data demonstrate the theory. |
| URL | https://www.mathematica.org/our-publications-and-findings/publications/analyzing-grouped-administrative-data-for-rcts-using-design-based-methods
|
| 来源智库 | Mathematica Policy Research (United States)
|
| 资源类型 | 智库出版物
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| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/489613
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推荐引用方式
GB/T 7714 |
Peter Z. Schochet. Analyzing Grouped Administrative Data for RCTs Using Design-Based Methods. 2019.
|
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