Single or multiple imputation?

Vishnu Ganglani writes:

It appears that multiple imputation appears to be the best way to impute missing data because of the more accurate quantification of variance. However, when imputing missing data for income values in national household surveys, would you recommend it would be practical to maintain the multiple datasets associated with multiple imputations, or a single imputation method would suffice. I have worked on household survey projects (in Scotland) and in the past gone with suggesting single methods for ease of implementation, but with the availability of open source R software I am think of performing multiple imputation methodologies, but a bit apprehensive because of the complexity and also the need to maintain multiple datasets (ease of implementation).

My reply: In many applications I’ve just used a single random imputation to avoid the awkwardness of working with multiple datasets. But if there’s any concern, I’d recommend doing parallel analyses on multiple imputed datasets and then combining inferences at the end.