Do dropouts suffer from dropping out? Estimation and prediction of outcome gains in generalized selection models
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辍学者会因辍学受损吗？广义选择模型中的结果增益估计与预测
- 研究问题
- 高中辍学如何影响毕业年级数学成绩，平均影响与实际辍学者的影响是否不同，结果增益分布如何预测？
- 方法与思路
- 作者在潜变量选择框架中使用 Student-t 与高斯有限混合模型，预测结果增益分布，并讨论未识别跨状态相关参数的学习。
- 材料陈述的贡献
- 摘要称广义模型优于传统高斯设定；平均辍学影响较大且为负，但实际辍学者的成绩影响并不显著为负。
- 阅读关注点
- 核对模型比较标准、反事实成绩分布的识别与先验设定，尤其检查未识别相关参数为何能够获得数据更新。
- 阅读边界
- 仅据所给 Crossref 摘要，先验敏感性与比较证据未说明；未识别参数的更新不等于点识别，不显著也不等于无影响。
原文摘要
Abstract In this paper we describe methods for predicting distributions of outcome gains in the framework of a latent variable selection model. We describe such procedures for Student‐ t selection models and a finite mixture of Gaussian selection models. Importantly, our algorithms for fitting these models are simple to implement in practice, and also permit learning to take place about the non‐identified cross‐regime correlation parameter. Using data from High School and Beyond, we apply our methods to determine the impact of dropping out of high school on a math test score taken at the senior year of high school. Our results show that selection bias is an important feature of this data, that our beliefs about this non‐identified correlation are updated from the data, and that generalized models of selectivity offer an improvement over the ‘textbook’ Gaussian model. Further, our results indicate that on average dropping out of high school has a large negative impact on senior‐year test scores. However, for those individuals who actually drop out of high school, the act of dropping out of high school does not have a significantly negative impact on test scores. This suggests that policies aimed at keeping students in school may not be as beneficial as first thought, since those individuals who must be induced to stay in school are not the ones who benefit significantly (in terms of test scores) from staying in school. Copyright © 2004 John Wiley & Sons, Ltd.