The coefficient of variation (CV) is a helpful quantity to describe the variation in evaluating results from different populations. There are many papers discussing methods of constructing confidence intervals for a single CV, such as exact method and approximation methods for CV when the underlying distribution is a normal distribution. However, the exact method is computationally cumbersome, and approximation methods can't be applied when the underlying distribution is unknown. In this thesis, we propose the generalized confidence interval for CV when the underlying distribution is normal and three empirical likelihood-based non-parametric intervals for CV when the underlying distribution is unknown. Simulation studies are conducted to compare the relative performances of these intervals based on the coverage probability and average interval length. Finally, the application of the proposed methods is demonstrated by using some real examples.