Missing data are a common practical problem for longitudinal designs. Time-series analysis is a longitudinal method that involves a large number of observations on a single unit. Four different missing-data methods (deletion, mean substitution, mean of adjacent observations, and maximum likelihood estimation) were evaluated. Computer-generated time-series data of length 100 were generated for 50 different conditions representing five levels of autocorrelation, two levels of slope, and five levels of proportion of missing data. Methods were compared with respect to the accuracy of estimation for four parameters (level, error variance, degree of autocorrelation, and slope). The choice of method had a major impact on the analysis. The maximum likelihood very accurately estimated all four parameters under all conditions tested. The mean of the series was the least accurate approach. Statistical methods such as the maximum likelihood procedure represent a superior approach to missing data.