Block What You Can, Except When You Shouldn't

Autor/inn/en	Pashley, Nicole E.; Miratrix, Luke W.
Titel	Block What You Can, Except When You Shouldn't
Quelle	In: Journal of Educational and Behavioral Statistics, 47 (2022) 1, S.69-100 (32 Seiten)Infoseite zur Zeitschrift PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/10769986211027240
Schlagwörter	Research Design; Experimental Groups; Control Groups; Sampling; Misconceptions + Suchen Sie Ihr Suchwort? Forschungsdesign; Missverständnis
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01