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Going beyond simple sample size calculations: a practitioner's guide

Author

Listed:
  • Brendon McConnell

    (Institute for Fiscal Studies)

  • Marcos Vera-Hernandez

    (Institute for Fiscal Studies and University College London)

Abstract

Basic methods to compute required sample sizes are well understood and supported by widely available software. However, the sophistication of the methods commonly used has not kept pace with the complexity of commonly employed experimental designs. We compile available methods for sample size calculations for continuous and binary outcomes with and without covariates, for both clustered and non-clustered RCTs. Formulae for both panel data and unbalanced designs are provided. Extensions include methods to: (1) optimise the sample when costs constraints are binding, (2) compute the power of a complex design by simulation, and (3) adjust calculations for multiple testing. View accompanying sample size calculators for this paper.

Suggested Citation

  • Brendon McConnell & Marcos Vera-Hernandez, 2015. "Going beyond simple sample size calculations: a practitioner's guide," IFS Working Papers W15/17, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:ifsewp:15/17
    as

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    File URL: https://www.ifs.org.uk/uploads/publications/wps/WP201517_update_Sep15.pdf
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    References listed on IDEAS

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    Cited by:

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    2. Pedro Carneiro & Sokbae Lee & Daniel Wilhelm, 2020. "Optimal data collection for randomized control trials," The Econometrics Journal, Royal Economic Society, vol. 23(1), pages 1-31.
    3. Fafchamps, Marcel & Labonne, Julien, 2017. "Using Split Samples to Improve Inference on Causal Effects," Political Analysis, Cambridge University Press, vol. 25(4), pages 465-482, October.
    4. Marcel Fafchamps & Julien Labonne, 2016. "Using Split Samples to Improve Inference about Causal Effects," NBER Working Papers 21842, National Bureau of Economic Research, Inc.
    5. Bernal, Pedro & Martinez, Sebastian, 2020. "In-kind incentives and health worker performance: Experimental evidence from El Salvador," Journal of Health Economics, Elsevier, vol. 70(C).
    6. Orazio Attanasio & Matthew Bird & Lina Cardona-Sosa & Pablo Lavado, 2019. "Freeing Financial Education via Tablets: Experimental Evidence from Colombia," NBER Working Papers 25929, National Bureau of Economic Research, Inc.
    7. Bedoya Arguelles,Guadalupe & Bittarello,Luca & Davis,Jonathan Martin Villars & Mittag,Nikolas Karl & Bedoya Arguelles,Guadalupe & Bittarello,Luca & Davis,Jonathan Martin Villars & Mittag,Nikolas Karl, 2017. "Distributional impact analysis: toolkit and illustrations of impacts beyond the average treatment effect," Policy Research Working Paper Series 8139, The World Bank.
    8. Timothy Gubler & Ian Larkin & Lamar Pierce, 2018. "Doing Well by Making Well: The Impact of Corporate Wellness Programs on Employee Productivity," Management Science, INFORMS, vol. 64(11), pages 4967-4987, November.
    9. Marianna Battaglia & Lara Lebedinski, 2017. "The curse of low expectations," The Economics of Transition, The European Bank for Reconstruction and Development, vol. 25(4), pages 681-721, October.

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    More about this item

    Keywords

    Power analysis; Sample size calculations; Randomised Control Trials; Cluster Randomised Control Trial; Covariates; Cost Minimisation; Multiple outcomes; Simulation;
    All these keywords.

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments

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