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Design Issues for Experiments in Multilevel Populations

Author

Listed:
  • Mirjam Moerbeek
  • Gerard J. P. van Breukelen
  • Martijn P. F. Berger

Abstract

For the design of experiments in multilevel populations the following questions may arise: What is the optimal level of randomization? Given a certain budget for sampling and measuring, what is the optimal allocation of units? What is the required budget for obtaining a certain power on the test of no treatment effect? In this article these questions will be dealt with for populations with two or three levels of nesting and continuous outcomes. Multilevel models are used to model the relationship between experimental condition and the outcome variable. The estimator of the regression, coefficient associated with treatment condition, a parameter assumed to be fixed in this paper; is of main interest and should be estimated as efficiently as possible. Therefore, its variance is used as a criterion for optimizing the level of randomization and the allocation of units.

Suggested Citation

  • Mirjam Moerbeek & Gerard J. P. van Breukelen & Martijn P. F. Berger, 2000. "Design Issues for Experiments in Multilevel Populations," Journal of Educational and Behavioral Statistics, , vol. 25(3), pages 271-284, September.
    • Handle: RePEc:sae:jedbes:v:25:y:2000:i:3:p:271-284
    DOI: 10.3102/10769986025003271
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    Cited by:

    1. Sheng Wu & Weng Kee Wong & Catherine M. Crespi, 2017. "Maximin optimal designs for cluster randomized trials," Biometrics, The International Biometric Society, vol. 73(3), pages 916-926, September.
    2. Kari Tokola & Andreas Lundell & Jaakko Nevalainen & Hannu Oja, 2014. "Design and cost optimization for hierarchical data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(2), pages 130-148, May.
    3. Steven Teerenstra & Bing Lu & John S. Preisser & Theo van Achterberg & George F. Borm, 2010. "Sample Size Considerations for GEE Analyses of Three-Level Cluster Randomized Trials," Biometrics, The International Biometric Society, vol. 66(4), pages 1230-1237, December.

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