Hoffman et al.  proposed an elegant resampling method for analyzing clustered binary data. The focus of their paper was to perform association tests on clustered binary data using within-cluster-resampling (WCR) method. Follmann et al.  extended Hoffman et al.'s procedure more generally with applicability to angular data, combining of p-values, testing of vectors of parameters, and Bayesian inference. Follmann et al.  termed their procedure multiple outputation because all "excess" data within each cluster is thrown out multiple times. Herein, we refer to this procedure as WCR-MO. For any statistical test to be useful for a particular design, it must be robust, have adequate power, and be easy to implement and flexible. WCR-MO can be easily extended to continuous data and is a computationally intensive but simple and highly flexible method. Considering family as a cluster, one can apply WCR to familial data in genetic studies. Using simulations, we evaluated WCR-MO's robustness for analysis of a continuous trait in terms of type I error rates in genetic research. WCR-MO performed well at the 5% α-level. However, it provided inflated type I error rates for α-levels less than 5% implying the procedure is liberal and may not be ready for application to genetic studies where α levels used are typically much less than 0.05.