Contents
How to decide whether missing values are Mar, MCAR, or Mnar-cross?
Here you can use the simplest imputation methods or if feasible remove the data but you can never prove data is MCAR. Rather you have to show it is unlikely it is MAR or MNAR. Is not what it sounds (Missing at random), it only means data is missing randomly related to the value of the observation but NOT randomly as related to other variables.
When do observations not depend on MCAR or Mar?
However, after conditioning on this information, whether or not data are missing does not depend on the values of the missing data. When observations are neither MCAR nor MAR, they are classified as Missing Not At Random (MNAR), i.e. the probability of an observation being missing depends on unobserved measurements.
What does missing completely at random MCAR mean?
Missing Completely at Random, MCAR, means there is no relationship between the missingness of the data and any values, observed or missing. Those missing data points are a random subset of the data.
What does missing not at random ( MNAR ) mean?
Missing not at random (MNAR). When data are MNAR, the fact that the data are missing is systematically related to the unobserved data, that is, the missingness is related to events or factors which are not measured by the researcher.
When do you use multiple imputation in MCAR?
If you believe your data are MAR rather than MCAR, then you should definitely consider using multiple imputation. If the probability of a particular value being missing depends on the unobserved data, then the data are “missing not at random” (MNAR).
Can you use multiple imputation with Mnar data?
In theory multiple imputation can give unbiased estimates with MNAR data, but only if the imputation method includes a model of the missingness mechanism. You’d need to code such a method yourself; it cannot be done using mi impute, ice, etc. In practice, if your data are MNAR it’s going to be very hard to carry out legitimate analysis.
What does missing completely at random ( MCAR ) mean?
Missing Completely at Random (MCAR) Missing Completely at Random is pretty straightforward. What it means is what is says: the propensity for a data point to be missing is completely random. There’s no relationship between whether a data point is missing and any values in the data set, missing or observed.