Inscrit le: 10 Oct 2017
|Posté le: Lun 18 Déc - 12:38 (2017) Sujet du message: Dealing with missing data
I have a household data set (cross-sectional data) with over 36,000 observations. However, after running mdesc I've noticed that for most variables over 50% of the data is missing due to family members not responding to the survey question. I need to compute the missing values as dropping them would mean I'd lose the large majority of my sample. The variables I am looking at are a mix of categorical and numerical variables. For example I have highest educational attainment (in categories) and wages (numerical). I am not quite sure whether I should use multiple imputation or mean replacement to deal with my missing data? And if there are any useful codes you could suggest?
I didn't find the right solution from the Internet.
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