A Comparison of Hot Deck Imputation and Substitution Methods in the Estimation of Missing Data

Abstract

It is of great importance to obtain data in an accurate and incomplete way for adequate conclusions to be drawn from investigations conducted. Due to various reasons, certain parts of an investigation might not be observed, and as a result of this, data might be missing and obtained incompletely. Missing value may not only be based on a single variable but also a multitude of variables. In this study, missing data in different proportions and belonging to more than a variable were produced. When data were considered within a context which is missing completely at random, Hot Deck imputation, random Hot Deck imputation and substitution methods (mean, median) were compared in the estimation of missing value. As a result of analysis, Hot Deck imputation method was found to be more effective in the estimation of missing value.