%0 Journal Article
%T Masked analysis for small-scale cluster randomized controlled trials.
%A Ferron JM
%A Nguyen D
%A Dedrick RF
%A Suldo SM
%A Shaunessy-Dedrick E
%J Behav Res Methods
%V 0
%N 0
%D Oct 2021 4
%M 34608614
%F 5.953
%R 10.3758/s13428-021-01708-0
%X Researchers conducting small-scale cluster randomized controlled trials (RCTs) during the pilot testing of an intervention often look for evidence of promise to justify an efficacy trial. We developed a method to test for intervention effects that is adaptive (i.e., responsive to data exploration), requires few assumptions, and is statistically valid (i.e., controls the type I error rate), by adapting masked visual analysis techniques to cluster RCTs. We illustrate the creation of masked graphs and their analysis using data from a pilot study in which 15 high school programs were randomly assigned to either business as usual or an intervention developed to promote psychological and academic well-being in 9th grade students in accelerated coursework. We conclude that in small-scale cluster RCTs there can be benefits of testing for effects without a priori specification of a statistical model or test statistic.