Mean Imputation

by Karen


I’m on a mission to warn the innocent of the dangers of mean imputation.  Perhaps that’s a bit dramatic, but mean imputation (also called mean substitution) really ought to be a last resort in most situations.  There are many alternatives to mean imputation that provide much more accurate parameter estimates, so there really is no excuse to use it.

This post is the first in a series explaining the many reasons not to use mean imputation (and to be fair, its advantages).

First, a definition: mean imputation is the replacement of a missing observation with the mean of the non-missing observations for that variable.

Problem #1: Mean imputation does not preserve the relationships among variables.

True, imputing the mean preserves the mean of the observed data.  So if the data are missing at random, the estimate of the mean remains unbiased. This is the original logic involved in mean imputation.

If all you are doing is estimating means (which is rarely the point of research studies), mean imputation will not bias your parameter estimate.  (It will bias your standard error, but I will get to that in another post).

Since most research studies are interested in the relationship among variables, mean imputation is not a good solution.  The following graph illustrates this well:

This graph illustrates hypothetical data between X=years of education and Y=annual income in thousands with n=50.  The blue circles are the original data and the solid blue line indicates the best fit regression line for the full data set.  The correlation between X and Y is r=.53.

I then randomly deleted 12 observations of income (Y) and substituted the mean.  The dataset with the mean imputations is shown by the red dots.  Blue circles with red dots inside them represent non-missing data.  Blue circles that are empty represent the missing data.   If you look across the graph at Y=39, you will see a row of red dots without blue circles.  These represent the imputed values.

The dotted red line represents the new best fit regression line with the imputed data.  As you can see, it is less steep than the original line, and the new correlation is now r=.39.  It is still significant, but it is clearly biased.  The real relationship is quite underestimated.

One note: if X were missing instead of Y, mean substitution would actually artificially inflate the correlation.

{ 6 comments… read them below or add one }

Queen sheba July 14, 2015 at 11:20 am

am carrying out a research on mean imputation and random know which one is more efficient


Andreea August 28, 2014 at 12:28 pm

any examples of papers that have used mean imputation out of curiosity? It is said many researchers still used this method so I’d be curious to see such examples. Thanks


Mar June 9, 2014 at 4:26 am

This may sound very basic but how do we conduct a mean imputation in spss?



Karen June 25, 2014 at 10:37 am

Hi Mar,

Many procedures have a checkbox, but I have to say, most of the time mean imputation causes more problems than it solves. There are many, many better approaches.


Revathi sathish October 17, 2012 at 5:06 am

It s good explanation. can you tell me about hot deck, cold deck, deductive, cell mean imputations advantage and disadvantage.


Karen October 23, 2012 at 3:30 pm

Hi Revathi,

There is some, though not detailed, information about these in our webinar Approaches to Missing Data: The Good, the Bad, and the Unthinkable: . The recording is free.


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