st: R: Imputation vs substitution with mean

["Carlo Lazzaro" <carlo.lazzaro@tiscalinet.it>]

Dear James,
sample mean substitution is a very ill-advised approach (even if you have
few missing values).
In the best case, replacing missing values with the mean of the existing
observation will reduce the variance, making all the subsequent statistics
biased.
Also last observation carried forward (LOCF) belongs to the "don't do it"
group.

You can find wide coverage of this topic in: 

- Roderick J. A. Little, Donald B. Rubin. Statistical Analysis with Missing
Data, 2nd Edition. Wiley Series in Probability and Statistics, 2002.
- John W. Graham. Missing Data: Analysis and Design. Springer, 2012.

There are other textbooks on dealing with missing data; a Google search can
give you a comprehensive picture of what has been published so far.

Multiple imputation is the way to go; I would point you to -[MI] intro
substantive - in Stata .pdf manual (as usual, a very good first step).


I do hope this helps.

Best regards,
Carlo



-----Messaggio originale-----
Da: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di James Bernard
Inviato: venerdì 18 ottobre 2013 07:38
A: statalist@hsphsun2.harvard.edu
Oggetto: st: Imputation vs substitution with mean

Hi All,

I have a longitudinal dataset with a dependent variable of count type.
Mu independent variable are of continuous and categorical type.

My independent variables have many missing values. I want to substitute the
missing values.

One way is to go through what I found out to be "imputation".

I heard that an easier approach is substituting the values with group
mean/population mean. Is this very different form imputation done by
-mi-command in Stata?

Thanks in advance,

James
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