Methods of Analysing Missing Values in a Regression Model

Different methods of imputation are adopted in this study to compensate for missing values encountered in the data collected. The imputation methods considered are the overall mean value, Random Overall, Logistic Regression, Linear Regression, Predictive Match, Multiple Imputations and the Hot Deck Imputation. The various values obtained by the methods were analysed and compared using Bartlett's test statistic for equality of variances among groups (Mean Square Errors of the seven methods). The software packages used for this research work are Winmice, Solas and SAS. Different values were estimated applying the various methods. However, results obtained from the test showed that the variances among the groups have no significant differences, that is, any of the imputation methods could be used. Further test using relative variance revealed that the multiple imputation method may be preferred.