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


  • Connelly, Luke B.


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.

Suggested Citation

  • Connelly, Luke B., 2003. "Balancing the Number and Size of Sites: An Economic Approach to the Optimal Design of Cluster Samples," MPRA Paper 14676, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:14676

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    References listed on IDEAS

    1. 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.
    2. Deaton,Angus & Muellbauer,John, 1980. "Economics and Consumer Behavior," Cambridge Books, Cambridge University Press, number 9780521296762, May.
    3. 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.
    4. Aigner, Dennis J & Balestra, Pietro, 1988. "Optimal Experimental Design for Error Components Models," Econometrica, Econometric Society, vol. 56(4), pages 955-971, July.
    5. Aigner, Dennis J., 1979. "Sample design for electricity pricing experiments : Anticipated precision for a time-of-day pricing experiment," Journal of Econometrics, Elsevier, vol. 11(1), pages 195-205, September.
    6. Howes, Stephen & Lanjouw, Jean Olson, 1998. "Does Sample Design Matter for Poverty Rate Comparisons?," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 44(1), pages 99-109, March.
    7. Conlisk, John, 1973. "Choice of Response Functional Form in Designing Subsidy Experiments," Econometrica, Econometric Society, vol. 41(4), pages 643-656, July.
    8. 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.
    9. 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.
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    Cited by:

    1. Khanam, Rasheda & Nghiem, Hong Son & Connelly, Luke B., 2009. "Child health and the income gradient: Evidence from Australia," Journal of Health Economics, Elsevier, vol. 28(4), pages 805-817, July.
    2. Takashi Kikuchi & John Gittins, 2010. "A behavioural Bayes approach for sample size determination in cluster randomized clinical trials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(5), pages 875-888.

    More about this item


    Cluster sample; optimal design; economic analysis;

    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; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity


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