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Factorial Designs, Model Selection, and (Incorrect) Inference in Randomized Experiments

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Listed:
  • Karthik Muralidharan
  • Mauricio Romero
  • Kaspar Wüthrich

Abstract

Factorial designs are widely used for studying multiple treatments in one experiment. While “long” model t-tests provide valid inferences, t-tests using the “short” model (ignoring interactions) yield higher power if interactions are zero, but incorrect inferences otherwise. Of 27 factorial experiments published in top-5 journals (2007--2017), 19 use the short model. After including all interactions, over half their results lose significance. Modest local power improvements over the long model are possible, but with lower power for most values of the interaction. If interactions are not of interest, leaving the interaction cells empty yields valid inferences and global power improvements.

Suggested Citation

  • Karthik Muralidharan & Mauricio Romero & Kaspar Wüthrich, 2019. "Factorial Designs, Model Selection, and (Incorrect) Inference in Randomized Experiments," NBER Working Papers 26562, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:26562
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    References listed on IDEAS

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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments

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