Missing data are a common problem, which can lead to biased results if the missingness is
not taken into account at the analysis stage. Imputation is often suggested as a strategy to
deal with item nonresponse allowing the analyst to use standard complete data methods
after the imputation. However, several misconceptions about the aims and goals (isn't
imputation making up data?) of imputation make some users skeptical about the approach.
In this course we will illustrate why thinking about the missing data is important and clarify
which goals a useful imputation method should try to achieve (and which not).