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The Essential Role of Pair Matching in Cluster-Randomized Experiments |
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Abstract:
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Most political science field experiments are cluster-randomized but ignore both the methods necessary for these data and design features that can save considerable efficiency. We develop simple and powerful methods for use in these experiments. |
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1 (255), cluster (255), pair (199), estim (194), match (158), random (138), design (117), e (104), wk (104), q (99), varianc (96), size (93), matched-pair (91), within (89), unit (88), sampl (84), assumpt (82), n2k (76), treatment (75), n1k (75), ect (68), |
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Association:
Name: MPSA Annual National Conference
URL: http://www.indiana.edu/~mpsa/
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Citation:
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MLA Citation:
| Imai, Kosuke., King, Gary. and Nall, Clayton. "The Essential Role of Pair Matching in Cluster-Randomized Experiments" Paper presented at the annual meeting of the MPSA Annual National Conference, Palmer House Hotel, Hilton, Chicago, IL, Apr 03, 2008 <Not Available>. 2008-12-10 <http://www.allacademic.com/meta/p268697_index.html> |
APA Citation:
| Imai, K. , King, G. and Nall, C. , 2008-04-03 "The Essential Role of Pair Matching in Cluster-Randomized Experiments" Paper presented at the annual meeting of the MPSA Annual National Conference, Palmer House Hotel, Hilton, Chicago, IL Online <APPLICATION/PDF>. 2008-12-10 from http://www.allacademic.com/meta/p268697_index.html |
Publication Type: Conference Paper/Unpublished Manuscript
Abstract: Most political science field experiments are cluster-randomized but ignore both the methods necessary for these data and design features that can save considerable efficiency. We develop simple and powerful methods for use in these experiments. |
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56 |
| Word count: |
22078 |
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| The Essential Role of Pair Matching in Cluster-Randomized Experiments with Application to the Mexican Universal Health Insurance Evaluation∗ Kosuke Imai† Gary King‡ Clayton Nall§ First Draft: July 17 2007 This Draft: January 18 2008 Abstract A basic feature of many field experiments is that investigators are only able to randomize clusters of individuals — such as households communities firms medical practices schools or classrooms — even when the individual is the unit of interest. To recoup some of the |
| to the editor: The merits of matching in community intervention trials: a cautionary tale by N. Klar and A. Donner. Statistics in Medicine 17 18 2149–2151. Turner R. M. White I. R. and Croudace T. (2007). Analysis of cluster-randomized cross-over data. Statistics in Medicine 26 274–289. 54 Varnell S. Murray D. Janega J. and Blitstein J. (2004). Design and Analysis of Group-Randomized Trials: A Review of Recent Practices. American Journal of Public Health 93 9 393–399. What Works Clearinghouse |
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