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So you want to run an experiment, now what? Some Simple Rules of Thumb for Optimal Experimental Design

Author

Listed:
  • John A. List
  • Sally Sadoff
  • Mathis Wagner

Abstract

Experimental economics represents a strong growth industry. In the past several decades the method has expanded beyond intellectual curiosity, now meriting consideration alongside the other more traditional empirical approaches used in economics. Accompanying this growth is an influx of new experimenters who are in need of straightforward direction to make their designs more powerful. This study provides several simple rules of thumb that researchers can apply to improve the efficiency of their experimental designs. We buttress these points by including empirical examples from the literature.

Suggested Citation

  • John A. List & Sally Sadoff & Mathis Wagner, 2009. "So you want to run an experiment, now what? Some Simple Rules of Thumb for Optimal Experimental Design," Carlo Alberto Notebooks 125, Collegio Carlo Alberto.
    • Handle: RePEc:cca:wpaper:125
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    References listed on IDEAS

    as
    1. Jinyong Hahn & Keisuke Hirano & Dean Karlan, 2011. "Adaptive Experimental Design Using the Propensity Score," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 96-108, January.
    2. Rutström, E. Elisabet & Wilcox, Nathaniel T., 2009. "Stated beliefs versus inferred beliefs: A methodological inquiry and experimental test," Games and Economic Behavior, Elsevier, vol. 67(2), pages 616-632, November.
    3. Duflo, Esther & Glennerster, Rachel & Kremer, Michael, 2008. "Using Randomization in Development Economics Research: A Toolkit," Handbook of Development Economics, in: T. Paul Schultz & John A. Strauss (ed.), Handbook of Development Economics, edition 1, volume 4, chapter 61, pages 3895-3962, Elsevier.
    4. Glenn W. Harrison & John A. List, 2004. "Field Experiments," Journal of Economic Literature, American Economic Association, vol. 42(4), pages 1009-1055, December.
    5. Richard Blundell & Monica Costa Dias, 2009. "Alternative Approaches to Evaluation in Empirical Microeconomics," Journal of Human Resources, University of Wisconsin Press, vol. 44(3).
    6. Camerer, Colin F & Hogarth, Robin M, 1999. "The Effects of Financial Incentives in Experiments: A Review and Capital-Labor-Production Framework," Journal of Risk and Uncertainty, Springer, vol. 19(1-3), pages 7-42, December.
    7. repec:feb:artefa:0087 is not listed on IDEAS
    8. Dean Karlan & John A. List, 2007. "Does Price Matter in Charitable Giving? Evidence from a Large-Scale Natural Field Experiment," American Economic Review, American Economic Association, vol. 97(5), pages 1774-1793, December.
    9. Harrison, Glenn W. & Lau, Morten I. & Elisabet Rutström, E., 2009. "Risk attitudes, randomization to treatment, and self-selection into experiments," Journal of Economic Behavior & Organization, Elsevier, vol. 70(3), pages 498-507, June.
    10. El-Gamal, Mahmoud A & Palfrey, Thomas R, 1996. "Economical Experiments: Bayesian Efficient Experimental Design," International Journal of Game Theory, Springer;Game Theory Society, vol. 25(4), pages 495-517.
    11. List John A., 2007. "Field Experiments: A Bridge between Lab and Naturally Occurring Data," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 6(2), pages 1-47, April.
    12. Levitt, Steven D. & List, John A., 2009. "Field experiments in economics: The past, the present, and the future," European Economic Review, Elsevier, vol. 53(1), pages 1-18, January.
    13. Graham Loomes, 2005. "Modelling the Stochastic Component of Behaviour in Experiments: Some Issues for the Interpretation of Data," Experimental Economics, Springer;Economic Science Association, vol. 8(4), pages 301-323, December.
    14. repec:feb:artefa:0090 is not listed on IDEAS
    15. Steven D. Levitt & John A. List, 2007. "What Do Laboratory Experiments Measuring Social Preferences Reveal About the Real World?," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 153-174, Spring.
    16. John A. List, 2001. "Do Explicit Warnings Eliminate the Hypothetical Bias in Elicitation Procedures? Evidence from Field Auctions for Sportscards," American Economic Review, American Economic Association, vol. 91(5), pages 1498-1507, December.
    17. Lenth R. V., 2001. "Some Practical Guidelines for Effective Sample Size Determination," The American Statistician, American Statistical Association, vol. 55, pages 187-193, August.
    Full references (including those not matched with items on IDEAS)

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    JEL classification:

    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General

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