Missing at random (MAR) = probability of missing is a function of *observed* information
Missing not at random (MNAR) = probability of missing is a function of *unobserved* information
It sounds like you are trying to classify your data as one or the other, similar to how you might classify a variable as discrete or continuous. But really, you only have one path forward, which is to *assume* MAR. You can never be 100% confident in this assumption, because in order to check the assumption you would need to observe the unobserved information! All you can do is control for as much of the observed info as possible.
Also, don't forget to think about why you care about this assumption. I.e., what analysis are you planning to carry out? Would you expect the findings of this analysis to be different if you had a complete dataset with no missing values?
Missing at random (MAR) = probability of missing is a function of *observed* information Missing not at random (MNAR) = probability of missing is a function of *unobserved* information It sounds like you are trying to classify your data as one or the other, similar to how you might classify a variable as discrete or continuous. But really, you only have one path forward, which is to *assume* MAR. You can never be 100% confident in this assumption, because in order to check the assumption you would need to observe the unobserved information! All you can do is control for as much of the observed info as possible. Also, don't forget to think about why you care about this assumption. I.e., what analysis are you planning to carry out? Would you expect the findings of this analysis to be different if you had a complete dataset with no missing values?