A Comparison of Advanced Methods used for Missing Data Imputation under Different Conditions

In this study, it is aimed to comparatively research of data sets obtained imputation for missing values that is formed by different ratios(%15 and %25) and in different structures (MCAR and MAR) with different methods. This study has been conducted on data set formed by points of 3129 students who participated in mathematics self-efficacy survey and answered it completely among 4848 students- age group of 15-who participated in PISA 2012 from Turkey. Missing data sets have been constituted by deleting data in different ratios to be constitute different structures in the data set. These data sets have been completed by six different nearby value imputation including EM, BIM, PSM, MCMC, MDIM, and RIM. Obtained data sets have been compared with full data sets by scale points of students. In the scope of the research, correlation between obtained scale points and scale points of real data has been seen quite high. Similarly, when scale points is considered, correlation of missing data imputation methods with each other have also been found quite high. Considering the difference between the totals and avarages of student scores calculated from the full data set and imputed data sets EM and MCMC is founded that the best missing data imputation methods under all conditions