The types and origins of missing data

First, we have to take a quick look at the possible different sources of missing data to identify why and how we usually get missing values. There are quite a few different reasons for data loss, which can be categorized into 3 different types.

For example, the main cause of missing data might be a malfunctioning device or the human factor of incorrectly entering data. Missing Completely at Random (MCAR) means that every value in the dataset has the same probability of being missed, so no systematic error or distortion is to be expected due to missing data, and nor can we explain the pattern of missing values. This is the best situation if we have NA (meaning: no answer, not applicable or not available) values in our data set.

But a much more frequent and unfortunate type of missing data is Missing at Random (MAR) compared to MCAR. In the case of MAR, the pattern of missing values is known or at least can be identified, although it has nothing to do with...