Was There a Riverside Miracle? A Hierarchical Framework for Evaluating Programs With Grouped Data
是否存在 Riverside 奇迹？利用分组数据评估项目的层级框架AI 解读 ↓
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是否存在 Riverside 奇迹？利用分组数据评估项目的层级框架
- 研究问题
- 如何在项目地点效应存在差异时评估处理效果，并把预测不确定性纳入对新地点实施效果与异常地点表现的判断？
- 方法与思路
- 作者提出介于完全合并和地点固定效应之间的层级模型，使用 GAIN 示范项目数据分析地点差异及基于地点特征的外推。
- 材料陈述的贡献
- 摘要称模型捕捉较多地点效应差异且比逐地估计更精确；计入预测不确定性后，Riverside 地点效应不再显著。
- 阅读关注点
- 应核对处理效应的识别、层级结构和预测分布，以及新地点与已有地点的相似性要求和 Riverside 效应被低估的原因。
- 阅读边界
- 所给 RePEc 摘要开头不完整，不推断刊本变化。识别条件与不确定性计算未说明；显著性变化不能解释为项目没有效果，外推有相似性限制。
原文摘要
sites. Two frequently used methods are pooling the data or using fixed effects (an extreme version of which estimates separate models for each site). The former approach ignores site effects. The latter incorporates site effects but lacks a framework for predicting the impact of subsequent implementations of the program (e.g., would a new implementation resemble Riverside?). I present a hierarchical model that lies between these two extremes. Using data from the Greater Avenues for Independence demonstration, I demonstrate that the model captures much of the site-to-site variation of the treatment effects but has less uncertainty than estimating the treatment effect separately for each site. I also show that when predictive uncertainty is ignored, the treatment impact for the Riverside sites is significant, but when predictive uncertainty is considered, the impact for these sites is insignificant. Finally, I demonstrate that the model extrapolates site effects with reasonable accuracy when the site being predicted does not differ substantially from the sites already observed. For example, the San Diego treatment effects could have been predicted based on their site characteristics, but the Riverside effects are consistently underpredicted.