Linear mixed-effects models are very popular and powerful tools in many scientific fields such as zoology, biology, and education. Estimators of fixed effects do not only depend on the variances of error terms but they also depend on random terms in mixed-effect models. When the distributions of random effects are unknown or enough sample size cannot be obtained, standard methods may fail. This study aims to determine a promising confidence interval method among existing methods in terms of coverage probability of true value of parameter. Standard and parametric bootstrap-based confidence interval methods for nested error regression model were compared in the simulation study under small samples. It is observed that parametric bootstrap-based method provides better coverage rates for small intra-correlation and profile likelihood method usually provides better results for moderate and strong correlation.