Timeline for Is it ever okay to drop missing observations?
Current License: CC BY-SA 3.0
13 events
| when toggle format | what | by | license | comment | |
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| Aug 27, 2020 at 12:59 | comment | added | boot-scootin | Would it make sense, in the case of structural/mechanical missing values, e.g. in your pregnancy-male example, to add an indicator like "pregnancy-does-not-apply"? That is, some indicator as to whether this attribute should be "available" at all to a particular case? | |
| Mar 28, 2017 at 15:21 | comment | added | EJ16 | The research has to look at pairs, because that's part of the hypothesis. So, it can't be country year. | |
| Mar 28, 2017 at 13:39 | comment | added | user78229 | Paired observations? Why not break them up into a separate record for each country-year, creating a more easily analyzed panel data model structure? | |
| Mar 26, 2017 at 22:58 | vote | accept | EJ16 | ||
| Mar 26, 2017 at 22:58 | comment | added | EJ16 | Hi, it's actually dyad year. country a-country b year. | |
| Mar 26, 2017 at 22:22 | comment | added | user78229 | I think it depends on the unit of analysis. Is your data at the level of the individual case? If so, then per kejtil b halvorsen's comment, creating a third outcome dropped or unknown is preferable to deleting the observations. | |
| Mar 26, 2017 at 19:42 | comment | added | EJ16 | DJohnson, thank you for clarifying. I have seen some research that have imputed "zeros" for the missing values, and then use a zero inflated regression. However, in this case, the zeros actually mean something, because it is a rate. So, I'm not sure what to do with these missing values since I shouldn't convert them to zeros. | |
| Mar 26, 2017 at 18:10 | comment | added | user78229 | +1 Good answer. One point worth noting is that "mechanical" MVs are more commonly referred to as "structural zeros" or null values, at least in the US literature. | |
| Mar 26, 2017 at 15:48 | comment | added | EJ16 | Maarten, never mind. I re-read the answer, and I now understand that MI would not suffice for this. It's currently showing that about half of the cases are otherwise closed and therefore missing. I guess my question is still what to do with these cases, because the standard procedure is not to include them in the rate calculations. | |
| Mar 26, 2017 at 15:07 | comment | added | EJ16 | Perhaps, and those were recorded as accepted/denied. But there are years were no decisions were made at all, and the only decisions that were made were "otherwise closed." So, that's the part that I'm stuck at the moment. | |
| Mar 26, 2017 at 15:05 | comment | added | EJ16 | Maarten, thank you so much. It makes sense. I also thought that perhaps it was a form of censoring (e.g. migrant death). But I don't understand what "imputing those values hides that feature."Does this mean, then, that multiple imputation should not be done? If so, what are the other options? I'm still scratching my head. | |
| Mar 26, 2017 at 15:05 | comment | added | EdM | Unlike for male pregnancy, however, there could have been an accept/reject decision in the cases where people died, moved on, or stopped showing up. In survival analysis these could be treated intelligently as censored cases, provided that the censoring was uninformative. I wonder if there is some way to incorporate censored status in analysis for this case at hand. | |
| Mar 26, 2017 at 14:55 | history | answered | Maarten Buis | CC BY-SA 3.0 |