Missing data continues to be an issue in any field that deals with data due to the fact that almost all the widely accepted and standard statistical methods assume complete data for all variables included in the analysis. Hence, in most studies statistical power is weakened and parameter estimates are biased, leading to weak conclusions and generalizations.

Many studies have established that multiple imputation methods are effective ways of handling missing data. This paper examines three different imputation methods (predictive mean matching; Bayesian linear regression; linear regression, non Bayesian) in the MICE package in the statistical software, R, to ascertain which of the three methods imputes data that yields parameter estimates closest to the parameter estimates of a complete data given different percentages of missingness. The paper extends the analysis by generating a pseudo data of the original data to establish how the imputation methods perform under varying conditions.