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Stability of Experimental Results: Forecasts and Evidence

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  • Stefano DellaVigna
  • Devin Pope

Abstract

How robust are experimental results to changes in design? And can researchers anticipate which changes matter most? We consider a specific context, a real-effort task with multiple behavioral treatments, and examine the stability along six dimensions: (i) pure replication; (ii) demographics; (iii) geography and culture; (iv) the task; (v) the output measure; (vi) the presence of a consent form. We use rank-order correlation across the treatments as measure of stability, and compare the observed correlation to the one under a benchmark of full stability (which allows for noise), and to expert forecasts. The academic experts expect that the pure replication will be close to perfect, that the results will differ sizably across demographic groups (age/gender/education), and that changes to the task and output will make a further impact. We find near perfect replication of the experimental results, and full stability of the results across demographics, significantly higher than the experts expected. The results are quite different across task and output change, mostly because the task change adds noise to the findings. The results are also stable to the lack of consent. Overall, the full stability benchmark is an excellent predictor of the observed stability, while expert forecasts are not that informative. This suggests that researchers' predictions about external validity may not be as informative as they expect. We discuss the implications of both the methods and the results for conceptual replication.

Suggested Citation

  • Stefano DellaVigna & Devin Pope, 2019. "Stability of Experimental Results: Forecasts and Evidence," NBER Working Papers 25858, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:25858
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    Cited by:

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    2. Isaiah Andrews & Drew Fudenberg & Lihua Lei & Annie Liang & Chaofeng Wu, 2022. "The Transfer Performance of Economic Models," Papers 2202.04796, arXiv.org, revised Jul 2024.
    3. Johannes G. Jaspersen & Marc A. Ragin & Justin R. Sydnor, 2022. "Insurance demand experiments: Comparing crowdworking to the lab," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 89(4), pages 1077-1107, December.

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    More about this item

    JEL classification:

    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments

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