thesis_Daniela Noethen_print final - Jacobs University

Intergenerational Knowledge Transfer in Work Teams: A Multilevel Social Network Perspective
.
, (6)
with σ 2 as the between-dyad variance at the dyadic level (Level 1), τ 00 as the between-person
variance at the individual level (Level 2), and τ 000 as the between-team variance at the team
level (Level 3).
As mentioned before, our data were non-independent with dyads nested within persons
and persons within teams. For the hypotheses tests, we thus used multilevel modeling
techniques (Bliese, 1998; Hofmann, 1997; Hox, 2002; Raudenbush & Bryk, 2002).
Employing the HLM 6.08 statistical package, we built the following three-level models: in a
first step we inserted the control variables at all three levels, centered around their grand
means (Model 1). For knowledge reception, as well as the subsequent explanatory variables
entered at the dyadic level, we assumed random slopes as we expected unexplained variability
between individuals in the effect of these variables. In a second step, we entered the age
difference at the dyadic level, age of the source at the individual level, and age diversity at the
team level to investigate the various age related effects on knowledge transfer (Model 2). In a
last step, we entered difference in team tenure at the dyadic and the source’s team tenure at
the individual level to test one of the potential direct effects associated with the age effects
(Model 3).
In order to check if the respective model could account for variance in the dependent
variable, goodness of fit equivalent to the R 2 in regression statistics was calculated following a
procedure suggested by Raudenbush and Bryk (2002). First, a one-way ANOVA model was
computed in HLM with knowledge transfer as dependent variable. Then, the values for σ 2 , τ 00,
and τ 000 from this baseline model were compared with the σ 2 , τ 00, and τ 000 values from
subsequent models in the following manner:
When dyad level predictors were added
, (7)
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