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

  • John A. List
  • Sally Sadoff
  • Mathis Wagner

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.

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File URL: http://www.carloalberto.org/assets/working-papers/no.125.pdf
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Paper provided by Collegio Carlo Alberto in its series Carlo Alberto Notebooks with number 125.

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Length: 22 pages
Date of creation: 2009
Date of revision:
Handle: RePEc:cca:wpaper:125
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  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. John List, 2006. "Field experiments: A bridge between lab and naturally occurring data," Artefactual Field Experiments 00083, The Field Experiments Website.
  3. Richard Blundell & Mónica Costa Dias, 2008. "Alternative Approaches to Evaluation in Empirical Microeconomics," CEF.UP Working Papers 0805, Universidade do Porto, Faculdade de Economia do Porto.
  4. 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.
  5. Elisabet Rutstrom & Glenn Harrison & Morten Lau, 2005. "Risk attitudes, randomization to treatment, and self-selection into experiments," Artefactual Field Experiments 00061, The Field Experiments Website.
  6. Glenn W. Harrison & John A. List, 2004. "Field Experiments," Journal of Economic Literature, American Economic Association, vol. 42(4), pages 1009-1055, December.
  7. 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.
  8. Jinyong Hahn & Keisuke Hirano & Dean Karlan, 2009. "Adaptive Experimental Design Using the Propensity Score," Working Papers 969, Economic Growth Center, Yale University.
  9. 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.
  10. repec:feb:artefa:0090 is not listed on IDEAS
  11. El-Gamal, Mahmoud A & Palfrey, Thomas R, 1996. "Economical Experiments: Bayesian Efficient Experimental Design," International Journal of Game Theory, Springer, vol. 25(4), pages 495-517.
  12. 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.
  13. 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.
  14. 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.
  15. Lenth R. V., 2001. "Some Practical Guidelines for Effective Sample Size Determination," The American Statistician, American Statistical Association, vol. 55, pages 187-193, August.
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