Which of the following best describes NMAR?

The classification of NMAR, or Not Missing At Random, refers specifically to a situation where the reason for the missing data is related to the unobserved data itself. In other words, the missingness is dependent on the value of the variable that is missing. This means that if you have data missing in such a way that its missingness influences the interpretation of the remaining data, this relationship must be acknowledged and addressed in analysis.

For instance, if participants with a certain characteristic (such as a high income) are less likely to respond to survey questions about financial habits, the data regarding those questions will be skewed because the missing data is not a random occurrence but rather specific to a particular subgroup. Understanding this distinction is crucial because it affects how analysts approach the data, particularly in terms of imputing missing values or generalizing findings.

In contrast, the other choices present scenarios that do not accurately capture this concept. Data missing randomly indicates a different situation (commonly referred to as MAR or Missing At Random), where the missingness is unrelated to the missing data. Data that is completely missing without correlation suggests a lack of systematic reason behind the missingness but doesn’t necessarily apply to NMAR. Lastly, data validated through research protocols does not connect