3 Fundamental Classes of Missing Data

Taken from Missingness mechanism, James Carpenter’s and Mike Kenward’s site on the statistics of missing data.

They outline three different mechanisms that would cause data to be missing:

  1. Missing Completely At Random (MCAR): when the reason for the data being missing does not depend on its value or lack of value.
  2. Missing At Random (MAR): when the reason for missing data can be explained by the observed data; after accounting for this, there is no further information in the unseen data.
  3. Not Missing At Random (NMAR):  when even after considering the information in the rest of the data, the reason for missing information depends on that unseen information

In this case, the conceptual common denominator seems to be “the relationship between the reason information is missing, and the content of the data set”.

This is an interesting differentiation to use potentially in a taxonomy of missing data reasons, because it looks at reasons from a different perspective.  (I.e. not from the perspective of the context in which the information was fetched, and also not from the perspective of whether the information is known vs. available)

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One response to “3 Fundamental Classes of Missing Data

  1. Pingback: Links to Statistical Approaches to Missing Data « History of an Idea: Missing Data

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