Real-world data collections often contain missing values, which can bring serious problems for data analysis. Simply discarding records with missing values tend to create bias in analysis. Missing data imputation methods try to fill in the missing values with estimated values. While numerous imputations methods have been proposed, these methods are mostly judged by their imputation accuracy, and little attention has been paid to their efficiency. With the increasing size of data collections, the imputation efficiency becomes an important issue. In this work we conduct an experimental comparison of several popular imputation methods, focusing on their time efficiency and scalability in terms of sample size and record dimension (number of attributes). We believe these results can provide a guide to data analysts when choosing imputation methods.