In research, missing data occur when a data value is unavailable. Many empirical studies encounter missing data. Missing data can occur in many stages of research due to many different causes in many different forms. Each type of missing data may have different reasons, and also different implication for the methods to deal with the missing data.The underlying reasons for missing data can be described as missing data mechanisms.
Missing observations are defined as NA in R. Missing data can have different implications for data summaries, analyses and conclusions based on the data with missing values. In this post, different types of missing data are reviewed and explored in data examples.
This post demonstrates how to perform passive multiple imputation to deal with missing items in a multi-item questionnaire.