Missing Data Imputation: A Practical Guide

This chapter demonstrates handling missing values in data analysis aimed at practitioners who seek a hands-on approach. The methods are presented straightforwardly, avoiding complex mathematical formulations or theoretical explanations. An actual data set with missing values on multiple variables is used to demonstrate various techniques for handling missing data, including listwise deletion, pairwise deletion, mean imputation, and multiple imputations. Each method is explained briefly, along with its key advantages and limitations, to help practitioners decide which method to use depending on the nature of the data and research questions. The chapter also discusses common misconceptions and pitfalls of missing value handling techniques, such as the potential for bias and the danger of assuming missing data to be missing completely at random (MCAR). The chapter suggests best practices in handling missing data, such as conducting sensitivity analyses and reporting the proportion of missing data in the data set to ensure the accuracy and reliability of the results.