A Monte Carlo study examined the statistical performance of single sample and bootstrap methods that can be used to test and form confidence interval estimates of indirect effects in two cluster randomized experimental designs. The designs were similar in that they featured random assignment of clusters to one of two treatment conditions and included a single intervening variable and outcome, but they differed in whether the mediator was measured at the participant or site level. A bias-corrected bootstrap had the best statistical performance for each design and was closely followed by the empirical-Mtest, either of which is recommended for testing and estimating indirect effects in multilevel designs. In addition, consistent with previous research, the commonly used z test had relatively poor performance.