Citation
Imai, Kosuke. (2005) “Do Get-Out-The-Vote Calls Reduce Turnout? The Importance of Statistical Methods for Field Experiments.” American Political Science Review, Vol. 99, No. 2 (May), pp. 283-300.
Abstract
In their landmark study of a field experiment, Gerber and Green (2000) found that get-out-the-votecallsreduceturnout by five percentage points. In this article, I introduce statistical methods that canuncover discrepancies between experimental design and actual implementation. The application ofthis methodology shows that Gerber and Green’s negative finding is caused by inadvertent deviationsfrom their stated experimental protocol. The initial discovery led to revisions of the original data by theauthors and retraction of the numerical results in their article. Analysis of their revised data, however, reveals new systematic patterns of implementation errors. Indeed, treatment assignments of the reviseddata appear to be even less randomized than before their corrections. To adjust for these problems, I employ a more appropriate statistical method and demonstrate that telephone canvassingincreasesturnout by five percentage points. This article demonstrates how statistical methods can find and correctcomplications of field experiments. (Note: This paper was submitted to APSR in August 2002, and was accepted for publication in August 2003. However, its actual publication has been delayed because the original authors have not finished writing their response.)
Related Papers
- If you are interested in designing and analyzing (properly) randomized experiments, see Horiuchi, Yusaku, Kosuke Imai, and Naoko Taniguchi (2007). “Designing and Analyzing Randomized Experiments.” American Journal of Political Science, Forthcoming.
- If you are interested in matching methods, see Ho, Daniel E., Kosuke Imai, Gary King, and Elizabeth A. Stuart (2007). “Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference,” Political Analysis, Forthcoming. The article comes with easy-to-use software called MatchIt
- If you are interested in propensity score methods, see Imai, Kosuke and David A. van Dyk (2004). “Causal Inference With General Treatment Regimes: Generalizing the Propensity Score,” Journal of the American Statistical Association, Theory and Methods Vol. 99, No. 467 (September), pp.854-866.