Missing Not at Random

Data are missing not at random (MNAR) when the missing values on a variable are related to the values of that variable itself, even after controlling for other variables. For example, when data are missing on IQ and only the people with low IQ values have missing observations for this variable. A problem with the MNAR mechanism is that it is impossible to verify that scores are MNAR without knowing the missing values.