You did not tell us very much about the nature of your missing data. Did you check for MCAR (Missing Completely at Random)? Given that you cannot assume MCAR, mean substitution can lead to biased estimators.

As a non-mathematical starting point, I can recommend the following two references:

1. Graham, Hohn W. (2009): Missing Data Analysis: Making It Work in the Real World.
2. Allison, Paul (2002): Missing data. (see section "Imputation", p. 11)

You did not tell us very much about the nature of your missing data. Did you check for MCAR (Missing Completely at Random)? Given that you cannot assume MCAR, mean substitution can lead to biased estimators.

As a non-mathematical starting point, I can recommend the following two references:

1. Graham, Hohn W. (2009): Missing Data Analysis: Making It Work in the Real World.
2. Allison, Paul (2002): Missing data. (see section "Imputation", p. 11)