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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.

This document describes the use of the Agree package for the data example that was used in the paper on specific agreement on dichotomous outcomes in the situation of more than two raters.

This document describes the use of the Agree package for two data examples that are used in the paper on specific agreement on polytomous outcomes in the situation of more than two raters. The first data example is an example of ordinal ratings and the second example of nominal rating.