This paper investigates three MICE methods: Predictive Mean Matching (PMM), Quantile Regression-based Multiple Imputation (QR-basedMI) and Simple Random Sampling Imputation (SRSI) at imputation numbers 5, 15, 20 and 30 with 5% and 20% missing values, to ascertain the one that produces imputed values that best matches the observed values and compare the model fit based on the AIC and MSE. The results show that; QR-basedMI produced more imputed values that didn’t match the observed, SRSI produced imputed values that match the observed values better as the number of imputations increases while PMM produced imputed values that matched the observed at all number of imputations and missingness considered. The model fit results for 5% missingness showed that QR-basedMI produced the best results in terms of MSE except for M=15, while AIC results showed that PMM produced best result for M= 5, QR-basedMI produced best results for M=15 and for M=20 and 30 SRSI produced the best results. The model fit results for 20% missingness shows that PMM produced the best results at all the number of imputations considered for both AIC and MSE except the AIC at M=15 where SRSI was seen to produce the best results. It is concluded that in comparison, the PMM is most suited when missingness is 20% but for 5% missingness the model fit is best with QR-basedMI.