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Balancing the Number and Size of Sites: An Economic Approach to the Optimal Design of Cluster Samples

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Author Info
Connelly, Luke B.

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Abstract

The design of randomised controlled trials (RCTs) entails decisions that have economic, as well as statistical implications. In particular, the choice of an individual or cluster randomisation design may affect the cost of achieving the desired level of power, other things equal. Furthermore, if cluster randomisation is chosen, the researcher must decide how to balance the number of clusters, or "sites", and the size of each site. This paper investigates these interrelated statistical and economic issues. Its principal purpose is to elucidate the statistical and economic trade-offs to assist researchers to employ RCT designs that have desired economic, as well as statistical, properties.

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File URL: http://mpra.ub.uni-muenchen.de/14676/
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Publisher Info
Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 14676.

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Date of creation: 2003
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Handle: RePEc:pra:mprapa:14676

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Related research
Keywords: Cluster sample; optimal design; economic analysis;

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Find related papers by JEL classification:
C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
D24 - Microeconomics - - Production and Organizations - - - Production; Capital and Total Factor Productivity; Capacity

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

  1. Aigner, Dennis J., 1979. "Bayesian analysis of optimal sample size and a best decision rule for experiments in direct load control," Journal of Econometrics, Elsevier, vol. 9(1-2), pages 209-221, January. [Downloadable!] (restricted)
  2. Howes, Stephen & Lanjouw, Jean Olson, 1998. "Does Sample Design Matter for Poverty Rate Comparisons?," Review of Income and Wealth, Blackwell Publishing, vol. 44(1), pages 99-109, March.
  3. Aigner, Dennis J & Balestra, Pietro, 1988. "Optimal Experimental Design for Error Components Models," Econometrica, Econometric Society, vol. 56(4), pages 955-71, July. [Downloadable!] (restricted)
  4. Conlisk, John, 1973. "Choice of Response Functional Form in Designing Subsidy Experiments," Econometrica, Econometric Society, vol. 41(4), pages 643-56, July. [Downloadable!] (restricted)
  5. Morris, Carl, 1979. "A finite selection model for experimental design of the health insurance study," Journal of Econometrics, Elsevier, vol. 11(1), pages 43-61, September. [Downloadable!] (restricted)
  6. Aigner, Dennis J., 1979. "A brief introduction to the methodology of optimal experimental design," Journal of Econometrics, Elsevier, vol. 11(1), pages 7-26, September. [Downloadable!] (restricted)
  7. Fiebig, Denzil G. & Bartels, Robert & Aigner, Dennis J., 1991. "A random coefficient approach to the estimation of residential end-use load profiles," Journal of Econometrics, Elsevier, vol. 50(3), pages 297-327, December. [Downloadable!] (restricted)
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Khanam, Rasheda & Nghiem, Hong Son & Connelly, Luke B., 2008. "Child Health and the Income Gradient: Evidence from Australia," MPRA Paper 13959, University Library of Munich, Germany. [Downloadable!]
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