When do we say data are missing at random?
When we say data are missing at random, we mean that missing data on a partly missing variable (Y) is related to some other completely observed variables (X) in the analysis model but not to the values of Y itself. It is not specifically related to the missing information.
What happens if there is missing data in a data set?
If there is missing data elsewhere in the data set, the existing values are used. Since a pairwise deletion uses all information observed, it preserves more information than the listwise deletion. Pairwise deletion is known to be less biased for the MCAR or MAR data.
What are the mechanisms by which missing data occurs?
The mechanisms by which missing data occurs are illustrated, and the methods for handling the missing data are discussed. The paper concludes with recommendations for the handling of missing data. Keywords: Expectation-Maximization, Imputation, Missing data, Sensitivity analysis
What does missing not at random ( MNAR ) mean?
If the characters of the data do not meet those of MCAR or MAR, then they fall into the category of missing not at random (MNAR). The cases of MNAR data are problematic. The only way to obtain an unbiased estimate of the parameters in such a case is to model the missing data.
What to do if there is too much missing data?
If there is too much data missing for a variable, it may be an option to delete the variable or the column from the dataset. There is no rule of thumbs for this, but it depends on the situation, and a proper analysis of data is needed before the variable is dropped altogether.
What are the different assumptions for missing data?
There are different assumptions about missing data mechanisms: a) Missing completely at random (MCAR): Suppose variable Y has some missing values. We will say that these values are MCAR if the probability of missing data on Y is unrelated to the value of Y itself or to the values of any other variable in the data set.