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.
Missing data occurs in many empirical studies. It is vital for study results to handle the missing data correctly. The best solution to deal with missing data depends on the reasons for the occurrence of missing data and on the analysis that is planned. In the project a guide was developed to find the best way to deal with missing data in multi-item questionnaires. The website www.missingdata.nl also provides a lot of information about missing data and methodology.