Block What You Can, Except When You Shouldn’t

IF 1.9 3区心理学 Q2 EDUCATION & EDUCATIONAL RESEARCH

Journal of Educational and Behavioral Statistics Pub Date : 2020-10-26 DOI:10.3102/10769986211027240

Nicole E. Pashley, Luke W. Miratrix

引用次数: 7

Abstract

Several branches of the potential outcome causal inference literature have discussed the merits of blocking versus complete randomization. Some have concluded it can never hurt the precision of estimates, and some have concluded it can hurt. In this article, we reconcile these apparently conflicting views, give a more thorough discussion of what guarantees no harm, and discuss how other aspects of a blocked design can cost, all in terms of estimator precision. We discuss how the different findings are due to different sampling models and assumptions of how the blocks were formed. We also connect these ideas to common misconceptions; for instance, we show that analyzing a blocked experiment as if it were completely randomized, a seemingly conservative method, can actually backfire in some cases. Overall, we find that blocking can have a price but that this price is usually small and the potential for gain can be large. It is hard to go too far wrong with blocking.

查看原文本刊更多论文

阻止你能阻止的，除非你不应该阻止

潜在结果因果推理文献的几个分支已经讨论了阻断与完全随机化的优点。一些人得出结论，它永远不会损害估计的准确性，而另一些人则得出结论，它可能会造成损害。在本文中，我们调和了这些明显冲突的观点，更深入地讨论了什么保证不会造成伤害，并讨论了阻塞设计的其他方面可能造成的成本，所有这些都是根据估算器的精度进行的。我们讨论了不同的发现是如何由于不同的采样模型和假设是如何形成的块。我们还将这些想法与常见的误解联系起来;例如，我们表明，分析一个封闭的实验，就好像它是完全随机的，一个看似保守的方法，实际上可能在某些情况下适得其反。总的来说，我们发现封锁是有代价的，但这个代价通常很小，而收益的潜力可能很大。阻塞很难犯太大的错误。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Educational and Behavioral Statistics Multiple-

CiteScore

4.40

自引率

4.20%

发文量

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.