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24 de abr. de 2020 · Summary. Unless you are in academics your burden of proof is probably low, so MAR is a good standard assumption that should be checked. Otherwise remember: MCAR - All is good, remvove NAs or impute. MAR - Be cautios, use advanced imputation methods like MICE. MNAR - You are fucked, get new/more data. Share.
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28 de ago. de 2016 · For the same reason, P>0.05 does not prove that the missing data are MCAR, because p-values don't prove the absence (or presence) of effects, but indicate whether a model is consistent with some data. Say you get a p-value of 0.04 using Little's MCAR test. That means, given that your missing data is MCAR, you would get the type of missing data ...
MCAR: Missing completely at random, this is great. It means that the non-response is completely random. So your survey is not biased. MAR: Missing at random, worse situation. Imagine you are asking for IQ and you have much more females participants than males.
13 de jul. de 2020 · Jul 13, 2020 at 22:43. I mean, we can turn the dataframe into binary of whether data is present or missing (assuming that the dataset that we have was collected in an unbiased manner). If the missingness in column A and column B is highly correlated, then it'll show that the missingness in col A or B could potentially be MNAR (?).
Missing at random means that missingness depends on some measured factors, like age or sex, so that you can use some models to fill in the missing patterns. Missing not at random means missingness depends on things you did not measure. In the question OP says NMAR vs. MAR but OP means MAR vs MCAR. – AdamO.
4 de abr. de 2018 · While some literatures did encourage a test for MCAR for determining a proper approach for dealing with missing data, some other literatures did also indicate that multiple imputation can deal with both MCAR and MAR (Enders, 2010). In such a case, it seems that I can proceed to the phase of imputation regardless MCAR or MAR (except for MNAR).
17 de jul. de 2011 · Just to be clear, your data is not necessarily MAR or MCAR! It could be missing based on unobserved predictors, or in the worst case missing precisely because of the value the variable would have taken. I recommend Gelman and Hill's chapter on imputation to help you understand imputation.
30 de nov. de 2020 · In this sense you can say that: a value is MAR, if the "missingness" depends on solely on observed variables. a value is MCAR, if the "missingness" depends solely on unobserved variables. Try to think of it more intuitively, a value can't be missing completely at random if we can (at least partially) account for its missingness with data at our ...
