To address practical needs, a number of software has been developed for missing data analysis. The purpose of this review is to provide an overview on commonly used missing data software programs, including both routines integrated in common statistical software packages (e.g., SPSS MVA) and specialized programs (e.g., NORM and SOLAS). The latter are relatively new and address specific needs in missing data analysis. While the missing-data software share many similarities such as windows interface and the ability to handle complex methods, they often differ from each other in specific missing methods and functions included. The review will focus on a comparison of software's (a) ability to inspect patterns in missing data, (b) available strategies to handle it and (c) the specific techniques such as, deletion, simple imputation and multiple imputations. The specific methods will include pairwise deletion, mean substitution, regression, expectation and maximization, factored likelihood and multiple imputations. Since the type of missing data, the complexity of the research question and the need to preserve the data will dictate the software best suited to handle the problem, a guideline will be provided on how to select appropriate missing data software. Along with demonstrations of some software, a step-by-step review of the missing data analysis procedure will be provided, including proper data preparation, inspecting the missing data for a pattern, selecting the most appropriate missing value strategy and method, and the specifics demands of the software. Finally, advantages and limitations of the popular software programs will be reviewed. Keyword(s): exercise/fitness, measurement/evaluation, research