ERIC Number: EJ1323829
Record Type: Journal
Publication Date: 2022-Feb
Pages: 32
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1076-9986
EISSN: EISSN-1935-1054
Available Date: N/A
Block What You Can, Except When You Shouldn't
Pashley, Nicole E.; Miratrix, Luke W.
Journal of Educational and Behavioral Statistics, v47 n1 p69-100 Feb 2022
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.
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: Institute of Education Sciences (ED)
Authoring Institution: N/A
IES Funded: Yes
Grant or Contract Numbers: R305D150040
Author Affiliations: N/A