The renaissance of field experimentation in evaluating interventions

Annu Rev Psychol. 2009;60:607-29. doi: 10.1146/annurev.psych.60.110707.163544.


Most experiments are done in laboratories. However, there is also a theory and practice of field experimentation. It has had its successes and failures over the past four decades but is now increasingly used for answering causal questions. This is true for both randomized and-perhaps more surprisingly-nonrandomized experiments. In this article, we review the history of the use of field experiments, discuss some of the reasons for their current renaissance, and focus the bulk of the article on the particular technical developments that have made this renaissance possible across four kinds of widely used experimental and quasi-experimental designs-randomized experiments, regression discontinuity designs in which those units above a cutoff get one treatment and those below get another, short interrupted time series, and nonrandomized experiments using a nonequivalent comparison group. We focus this review on some of the key technical developments addressing problems that previously stymied accurate effect estimation, the solution of which opens the way for accurate estimation of effects under the often difficult conditions of field implementation-the estimation of treatment effects under partial treatment implementation, the prevention and analysis of attrition, analysis of nested designs, new analytic developments for both regression discontinuity designs and short interrupted time series, and propensity score analysis. We also cover the key empirical evidence showing the conditions under which some nonrandomized experiments may be able to approximate results from randomized experiments.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.
  • Review

MeSH terms

  • Bias
  • Clinical Trials as Topic / statistics & numerical data*
  • Evidence-Based Medicine / statistics & numerical data
  • Humans
  • Program Development*
  • Randomized Controlled Trials as Topic / statistics & numerical data*
  • Regression Analysis
  • Research Design / statistics & numerical data*