Analysis of Missing Data in Progressed Learners: The Use of Multiple Imputation Methods

Abstract

The issue of missing data raises concerns in all statistical and educational research. In this study, we focus on missing data in school-based assessment data generated by progressed high school learners (those who did not meet the promotional requirements for their current grades but were allowed to move to the next grade because of policy stipulations). There are a number of approaches available for handling missing data in statistical literature. Multiple imputation is an approach statisticians often recommend for dealing with missing data. We analysed a longitudinal dataset composed of progressed high school learners with missing data points, imputing the missing values with multiple imputation by chained equations and Amelia II. The missing data points in our dataset were due to learners not submitting their tasks, which then impacted negatively on their results. We found a higher proportion of missing values in mathematics/mathematical literacy, science and accounting. Our results showed that progressed learners struggle more with these subjects where knowledge develops cumulatively, and that their gaps in prior knowledge probably hinder them in understanding new concepts. Thus, the policy on progression of learners brings challenges to the already strained educational system and requires a more specialised system of support.