Which term describes data that is missing but not at random?

The term that denotes data that is missing but not at random is "NMAR," which stands for Not Missing At Random. This means that the reason for the missing data is related to the unobserved data itself. In other words, the likelihood of a data point being missing is connected directly to the value of that data point. This creates potential biases in analyses since the missingness reflects information about the data that is absent.

Understanding NMAR is crucial in the context of data analysis, because traditional methods of handling missing data, such as mean imputation or listwise deletion, may not adequately address the biases introduced by NMAR. Instead, researchers might need to employ more sophisticated techniques that aim to explicitly model the process of missing data in order to yield valid conclusions.

In contrast, the other terms related to missing data are as follows: "MCAR" (Missing Completely At Random) indicates that missingness is entirely random and unrelated to any measured or unmeasured variables; "MAR" (Missing At Random) indicates that the missingness can be explained by observed data. "Listwise Deletion" is a method for handling missing data where entire rows with any missing values are discarded from the analysis, which is generally not advisable for NMAR