xii Preface In real life, a set of data invariably contains missing data. The problem then is to reconstitute the most probable values through processes such as interpolation and extrapolation before using that set. Methods for resolving the problem of missing data have been extensively explored in statistical texts (Abdella, 2005; Little & Rubin, 1987). The initial work on compensating for missing data was focused on improving survey data. In this book, missing data interpolation is called imputation to distinguish it from the statistical approach. Imputation is viewed as an alternative approach to deal with missing data. There are two ways to deal with missing data: these are either to estimate the missing data or to delete any vector (data set) with missing value(s). This book focuses on methods that estimate the missing values. Of particular importance to the area of missing data interpolation is to analyze the nature of the missing data, and this is termed the missing.data.mechanism. Little and Rubin (1987) categorized three missing