Ramsay-curve item response theory (RC-IRT) is a nonparametric procedure that estimates the latent trait using splines, and no distributional assumption about the latent trait is required. For item parameters of the two-parameter logistic (2-PL), three-parameter logistic (3-PL), and polytomous IRT models, RC-IRT can provide more accurate estimates than the commonly used marginal maximum likelihood estimation (MMLE) when the latent trait is not normally distributed. However, standard errors (SEs) for the item parameter estimates have not been developed in RC-IRT as no analytical solution is readily available. In such cases, bootstrapping provides an alternative way to estimate SEs. Using bootstrapping, the observed sample is treated as the pseudopopulation from which "n" repeated random samples are drawn with replacement. The same estimation procedure is employed on each random sample and the point estimates are retained. Then, the SE of a particular parameter estimate is the standard deviation of the retained estimates, and the associated confidence interval (CI) can be determined by two percentiles. In this research, bootstrapping was utilized to estimate SEs and CIs for item parameters in the 2-PL model, and the performance of bootstrapping was compared with that of MMLE. Three criteria were used to assess the performance of bootstrapping relative to MMLE. First, the average standard error (ASE) was calculated across 40 items to summarize the overall magnitude of SEs. Smaller ASE indicates higher efficiency. Second, the correlation between the two sets of SEs was calculated, measuring agreement between bootstrapping and MMLE. Last, the number of items not covered by 95% CIs was counted. Results of the research are presented. (Contains 1 figure, 1 table and 1 note.)