#Inserting data pspp software#

However, let’s move on to the more important part – the drawbacks of mean imputation:

We learned some reasons why mean imputation is so popular among data users. Mean substitution might be a valid approach, in case that the univariate average of your variables is the only metric your are interested in.

If the response mechanism is MCAR, the sample mean of your variable is not biased.

You can explain the imputation method easily to your audience and everybody with basic knowledge in statistics will get what you’ve done.

Mean imputation is very simple to understand and to apply (more on that later in the R and SPSS examples).

Since mean imputation replaces all missing values, you can keep your whole database.

#Inserting data pspp software#

Missing values in your data do not reduce your sample size, as it would be the case with listwise deletion (the default of many statistical software packages, e.g.However, I’ll be fair and show you also the advantages of the method: You probably already noticed that I’m not a big fan of mean imputation.

Sounds easy to apply, doesn’t it? So why is it so evil to use mean substitution? Click on the buttons below to select the topic you are interested in: Mean Imputation Pros & Cons Mean Imputation in R (Example) Mean Imputation in SPSS (Video) Ask me a Question (It's Free)Īdvantages and Drawbacks of Mean Substitution Mean imputation (or mean substitution) replaces missing values of a certain variable by the mean of non-missing cases of that variable.