We propose a precise and practical way to calculate standard errors of parameters in the Rasch model. Because the information function is the asymptotic variance of the maximum likelihood estimator (MLE), it may be far from the variance of MLE in a finite sample. We advocate the use of a plugin estimator of empirical variance rather than the information function to calculate the standard error. In the study, we bring forth a novel technique to simplify the computation involved in estimating the empirical variance, which lowers the cost of the estimation. I derive theorems about statistical properties and use R to develop the procedure.