The performance of the normal theory bootstrap (NTB), the percentile bootstrap (PB), and the bias-corrected and accelerated (BCa) bootstrap confidence intervals (CIs) for coefficient omega was assessed through a Monte Carlo simulation under conditions not previously investigated. Of particular interests were nonnormal Likert-type and binary items. The results show a clear order in performance. The NTB CI had the best performance in that it had more consistent acceptable coverage under the simulation conditions investigated. The results suggest that the NTB CI can be used for sample sizes larger than 50. The NTB CI is still a good choice for a sample size of 50 so long as there are more than 5 items. If one does not wish to make the normality assumption about coefficient omega, then the PB CI for sample sizes of 100 or more or the BCa CI for samples sizes of 150 or more are good choices.