title: Pairwise cluster randomization: an exposition.
abstract: BACKGROUND
  Cluster randomization (CR) is often used for program evaluation when simple random assignment is inappropriate or infeasible. Pairwise cluster random (PCR) assignment is a more efficient alternative, but evaluators seemed to be deterred from PCR because of bias and identification problems. This article explains the problems, argues that they can be mitigated through design choices, and demonstrates that the suitability of PCR can be tested using Monte Carlo procedures.
  
  
  METHODS
  The article presents simple formulas showing how the PCR estimator is biased and explains why its standard error is not identified. Formal derivations appear in a longer companion article. Using those formulas, this article discusses how good design can mitigate the problems with bias and identification. Using Monte Carol simulation, this article also shows how to choose between CR and PCR at the design stage.
  
  
  CONCLUSIONS
  This article advocates for wider use of the PCR design. PCR loses its appeal when the investigator lacks baseline data for matching the clusters. Its use is less compelling when there are a large number of clusters. But when the evaluator is working with a fairly small number of clusters-26 in the running example used in this article-PCR is an attractive alternative to CR.
authors.affiliation: Abt Associates Inc., Cambridge, MA, USA bill_rhodes@abtassoc.com.
authors.forename: William
authors.initials: W
authors.lastname: Rhodes
mesh_terms: D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method; D015397:Program Evaluation / Q000379:methods*; D011897:Random Allocation; D012107:Research Design
keywords: content area; design and evaluation of programs and policies; methodological development; outcome evaluation (other than economic evaluation)