Experimenting with Measurement Error: Techniques with Applications to the Caltech Cohort Study

Ben Gillen, Erik Snowberg and Leeat Yariv

No 21517, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: Measurement error is ubiquitous in experimental work. It leads to imperfect statistical controls, attenuated estimated effects of elicited behaviors, and biased correlations between characteristics. We develop simple statistical techniques for dealing with experimental measurement error. These techniques are applied to data from the Caltech Cohort Study, which conducts repeated incentivized surveys of the Caltech student body. We illustrate the impact of measurement error by replicating three classic experiments, and showing that results change substantially when measurement error is taken into account. Collectively, these results show that failing to properly account for measurement error may cause a field-wide bias: it may lead scholars to identify "new" effects and phenomena that are actually similar to those previously documented.

JEL-codes: C81 C9 D8 J71 (search for similar items in EconPapers)
Date: 2015-09
New Economics Papers: this item is included in nep-ecm, nep-exp and nep-lma
Note: DEV LS POL
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Citations: View citations in EconPapers (34)

Published as Ben Gillen & Erik Snowberg & Leeat Yariv, 2019. "Experimenting with Measurement Error: Techniques with Applications to the Caltech Cohort Study," Journal of Political Economy, vol 127(4), pages 1826-1863.

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