全球智库信息集成服务系统(G2TT): Asymdystopia: The Threat of Small Biases in Evaluations of Education Interventions that Need to be Powered to Detect Small Impacts

G2TT

来源类型	Publication
	Asymdystopia: The Threat of Small Biases in Evaluations of Education Interventions that Need to be Powered to Detect Small Impacts
	John Deke; Thomas Wei; and Tim Kautz
发表日期	2017-10-03
出版者	Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance
出版年	2017
语种	英语
概述	The authors examine the potential for small biases to increase the risk of making false inferences as studies are powered to detect smaller impacts and recommend strategies researchers can use to avoid or mitigate these biases.",
摘要	Key Findings: For RCTs, evaluators must either achieve much lower rates of missing data than before or offer a strong justification for why missing data are unlikely to be related to study outcomes. For RDDs, state-of-the-art statistical methods can protect against inaccuracies from incorrect regression models, but this protection comes at a cost – much larger sample sizes are needed in order to detect small effects when using these methods. Evaluators of education interventions are increasingly designing studies to detect impacts much smaller than the 0.20 standard deviations that Cohen (1988) characterized as “small.” While the need to detect smaller impacts is based on compelling arguments that such impacts are substantively meaningful, the drive to detect smaller impacts may create a new challenge for researchers: the need to guard against smaller biases. The purpose of this paper is twofold. First, we examine the potential for small biases to increase the risk of making false inferences as studies are powered to detect smaller impacts, a phenomenon we refer to as asymdystopia. We examine this potential for two of the most rigorous designs commonly used in education research—randomized controlled trials (RCTs) and regression discontinuity designs (RDDs). Second, we recommend strategies researchers can use to avoid or mitigate these biases.
URL	https://www.mathematica.org/our-publications-and-findings/publications/asymdystopia-the-threat-of-small-biases-in-evaluations-of-education-interventions-that-need
来源智库	Mathematica Policy Research (United States)
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/489030
推荐引用方式 GB/T 7714	John Deke,Thomas Wei,and Tim Kautz. Asymdystopia: The Threat of Small Biases in Evaluations of Education Interventions that Need to be Powered to Detect Small Impacts. 2017.

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文件名称/大小	资源类型	版本类型	开放类型	使用许可
Asymdystopia IES rpt（2020KB）	智库出版物		限制开放	CC BY-NC-SA	浏览