Designing and Analyzing Randomized Experiments in Political Science 

 I just read a paper by Yusaku Horiuchi, Kosuke Imai, and Naoko Taniguchi (HIT) on " Designing and Analyzing Randomized Experiments ." HIT draw upon the longstanding statistics literature on this topic and attempt to “pave the way for further development of more methodologically sophisticated experimental studies in political science.” While experiments are becoming more frequent in political science, HIT observe that a majority of recent studies do not randomize effectively and still ignore problems of noncompliance and or nonresponse. 

 Specifically, they offer four general recommendations:  

 (I) Researchers should obtain information about background characteristics of experimental subjects that can be used to predict their noncompliance, nonresponse, and the outcome. 

 (II) Researchers should conduct efficient randomization of treatments by using, for example, randomized-block and matched-pair designs. 
  
(III) Researchers must make every effort to record the precise treatment received by each experimental subject. 

 (IV) Finally, a valid statistical analysis of randomized experiments must properly account for noncompliance and nonresponse problems simultaneously. 

 Take a look. I agree with HIT that these issues are not new, yet too often ignored in political science (exceptions acknowledged). HIT illustrate their recommendations using a carefully crafted online experiment on Japanese elections. Statistically, they employ a Bayesian approach using the  general statistical framework of randomized experiments with noncompliance and nonresponse (Angrist, Imbens, and Rubin 1996; Imbens and Rubin 1997; Frangakis and Rubin 1999, 2002). There is also interesting new stuff on modeling causal heterogeneity in this framework (a big topic in of itself).