Inaugural lecture PD Kristian Kleinke

Missing values are one of the most persistent challenges in social science research. Hardly any data set is complete, and this is precisely where a methodological problem begins that can have far-reaching consequences for analysis and interpretation.

In his inaugural lecture "Applied Multiple Imputation", PD Kristian Kleinke will focus on one of the central methods for dealing with this problem: multiple imputation, which is based on Donald Rubin's theory. This method makes it possible not to simply ignore missing data or replace them with simple substitute values, but to reconstruct them in a statistically sound manner - and to explicitly take into account the uncertainty of these estimates.

The lecture gives an overview of current approaches and at the same time shows that there is no "one right solution". Instead, the choice of imputation method depends heavily on the respective application context: small samples pose different requirements than complex panel data, and non-linear relationships pose additional methodological challenges.

The focus is therefore not only on technical procedures, but also on their limitations - and on the question of how reliable research results actually are when data remain incomplete.

In his inaugural lecture, PD Kristian Kleinke invites us to take a closer look behind the scenes of modern data analysis and to critically examine the possibilities, but also the pitfalls, of multiple imputation.