thesis_Daniela Noethen_print final - Jacobs University

Intergenerational Knowledge Transfer in Work Teams: A Multilevel Social Network Perspective
twice in the data set, once with person A considered as source, and once with person B as
source. However, we do not see this to lead to statistical problems.
Other factors that we controlled for were the following: 1) the source’s status as an
employee in or the supervisor of the team, 2) the source having a part-time or full-time
contract, 3) the source’s self-reported percentage of knowledge transfer within the team
versus beyond the borders of the team (depending on their task, some employees had to
collaborate more or less with other teams, customers, or external institutions, which may of
course influence the frequency of their knowledge transfer within the team), 4) the number of
teams the source formed part of (some participants worked in two or more completely
different teams and took part in the survey for several teams – two factors that might
influence their report of knowledge transfer), 5) the team size (the larger the team, the less
often one can, on average, transfer knowledge to a single colleague), and 6) the source’s
membership to one of the branches of the administration via an organization dummy.
3.4.3. Analytical strategy and statistical approach
In a first step, we conducted a missing variable analysis and filled missing data using the
expectation-maximization (EM) algorithm (Dempster, Laird, & Rubin, 1977). Missing data
were filled for independent as well as dependent variables following Graham’s (2009)
recommendation that this produces less bias than listwise or pairwise deletion. In a next step,
we computed simple descriptives and zero order correlations. To obtain information about the
distribution of variance across the three different levels, we calculated the proportion of
variance located at each level equivalent to calculations of the ICC(1) (Bliese, 1998):
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, (5)
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