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Evaluation studies often lack sophistication in their statistical analyses, particularly where there are small data sets or missing data. Until recently, the methods used for analysing incomplete data focused on removing the missing values, either by deleting records with incomplete information or by substituting the missing values with estimated mean scores. These methods, though simple to implement, are problematic. However, recent advances in theoretical and computational statistics have led to more flexible techniques with sound statistical bases. These procedures involve multiple imputation (MI), a technique in which the missing values are replaced by m > 1 estimated values, where m is typically small (e.g. 3-10). Each of the resultant m data sets is then analysed by standard methods, and the results are combined to produce estimates and confidence intervals that incorporate missing data uncertainty. This paper reviews the key ideas of multiple imputation, discusses the currently available software programs relevant to evaluation studies, and demonstrates their use with data from a study of the adoption and implementation of information technology in Bali, Indonesia.