利用差中之差和具有治疗时间变化的比较中断时间序列估计器估计治疗效果的统计能力

IF 1.9 3区 心理学 Q2 EDUCATION & EDUCATIONAL RESEARCH Journal of Educational and Behavioral Statistics Pub Date : 2021-02-12 DOI:10.3102/10769986211070625
Peter Z. Schochet
{"title":"利用差中之差和具有治疗时间变化的比较中断时间序列估计器估计治疗效果的统计能力","authors":"Peter Z. Schochet","doi":"10.3102/10769986211070625","DOIUrl":null,"url":null,"abstract":"This article develops new closed-form variance expressions for power analyses for commonly used difference-in-differences (DID) and comparative interrupted time series (CITS) panel data estimators. The main contribution is to incorporate variation in treatment timing into the analysis. The power formulas also account for other key design features that arise in practice: autocorrelated errors, unequal measurement intervals, and clustering due to the unit of treatment assignment. We consider power formulas for both cross-sectional and longitudinal models and allow for covariates. An illustrative power analysis provides guidance on appropriate sample sizes. The key finding is that accounting for treatment timing increases required sample sizes. Further, DID estimators have considerably more power than standard CITS and ITS estimators. An available Shiny R dashboard performs the sample size calculations for the considered estimators.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":"47 1","pages":"367 - 405"},"PeriodicalIF":1.9000,"publicationDate":"2021-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Statistical Power for Estimating Treatment Effects Using Difference-in-Differences and Comparative Interrupted Time Series Estimators With Variation in Treatment Timing\",\"authors\":\"Peter Z. Schochet\",\"doi\":\"10.3102/10769986211070625\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article develops new closed-form variance expressions for power analyses for commonly used difference-in-differences (DID) and comparative interrupted time series (CITS) panel data estimators. The main contribution is to incorporate variation in treatment timing into the analysis. The power formulas also account for other key design features that arise in practice: autocorrelated errors, unequal measurement intervals, and clustering due to the unit of treatment assignment. We consider power formulas for both cross-sectional and longitudinal models and allow for covariates. An illustrative power analysis provides guidance on appropriate sample sizes. The key finding is that accounting for treatment timing increases required sample sizes. Further, DID estimators have considerably more power than standard CITS and ITS estimators. An available Shiny R dashboard performs the sample size calculations for the considered estimators.\",\"PeriodicalId\":48001,\"journal\":{\"name\":\"Journal of Educational and Behavioral Statistics\",\"volume\":\"47 1\",\"pages\":\"367 - 405\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2021-02-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Educational and Behavioral Statistics\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.3102/10769986211070625\",\"RegionNum\":3,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"EDUCATION & EDUCATIONAL RESEARCH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Educational and Behavioral Statistics","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.3102/10769986211070625","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
引用次数: 6

摘要

本文为常用的差分(DID)和比较中断时间序列(CITS)面板数据估计量开发了新的幂分析闭式方差表达式。主要贡献是将治疗时间的变化纳入分析。幂公式还考虑了实践中出现的其他关键设计特征:自相关误差、不相等的测量间隔以及由于治疗分配单位而导致的聚类。我们考虑横截面和纵向模型的幂公式,并考虑协变。说明性的功率分析提供了关于适当样本大小的指导。关键发现是,考虑到治疗时间会增加所需的样本量。此外,DID估计器比标准CITS和ITS估计器具有更大的功率。一个可用的Shiny R仪表板为所考虑的估计量执行样本量计算。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Statistical Power for Estimating Treatment Effects Using Difference-in-Differences and Comparative Interrupted Time Series Estimators With Variation in Treatment Timing
This article develops new closed-form variance expressions for power analyses for commonly used difference-in-differences (DID) and comparative interrupted time series (CITS) panel data estimators. The main contribution is to incorporate variation in treatment timing into the analysis. The power formulas also account for other key design features that arise in practice: autocorrelated errors, unequal measurement intervals, and clustering due to the unit of treatment assignment. We consider power formulas for both cross-sectional and longitudinal models and allow for covariates. An illustrative power analysis provides guidance on appropriate sample sizes. The key finding is that accounting for treatment timing increases required sample sizes. Further, DID estimators have considerably more power than standard CITS and ITS estimators. An available Shiny R dashboard performs the sample size calculations for the considered estimators.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
4.40
自引率
4.20%
发文量
21
期刊介绍: Journal of Educational and Behavioral Statistics, sponsored jointly by the American Educational Research Association and the American Statistical Association, publishes articles that are original and provide methods that are useful to those studying problems and issues in educational or behavioral research. Typical papers introduce new methods of analysis. Critical reviews of current practice, tutorial presentations of less well known methods, and novel applications of already-known methods are also of interest. Papers discussing statistical techniques without specific educational or behavioral interest or focusing on substantive results without developing new statistical methods or models or making novel use of existing methods have lower priority. Simulation studies, either to demonstrate properties of an existing method or to compare several existing methods (without providing a new method), also have low priority. The Journal of Educational and Behavioral Statistics provides an outlet for papers that are original and provide methods that are useful to those studying problems and issues in educational or behavioral research. Typical papers introduce new methods of analysis, provide properties of these methods, and an example of use in education or behavioral research. Critical reviews of current practice, tutorial presentations of less well known methods, and novel applications of already-known methods are also sometimes accepted. Papers discussing statistical techniques without specific educational or behavioral interest or focusing on substantive results without developing new statistical methods or models or making novel use of existing methods have lower priority. Simulation studies, either to demonstrate properties of an existing method or to compare several existing methods (without providing a new method), also have low priority.
期刊最新文献
Improving Balance in Educational Measurement: A Legacy of E. F. Lindquist A Simple Technique Assessing Ordinal and Disordinal Interaction Effects A Comparison of Latent Semantic Analysis and Latent Dirichlet Allocation in Educational Measurement Sample Size Calculation and Optimal Design for Multivariate Regression-Based Norming Corrigendum to Power Approximations for Overall Average Effects in Meta-Analysis With Dependent Effect Sizes
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1