thesis_Daniela Noethen_print final - Jacobs University

Preventing Knowledge Loss When Employees Expect to Leave
point Likert scale from “completely disagree” to “completely agree”, in this case with a
Cronbach’s α of 0.93. Individual ratings of intragroup trust were aggregated to the team level,
with a mean r WG(J) of 0.85 (Cohen, Doveh, & Eick, 2001; James, Demaree, & Wolf, 1984), an
ICC(1) of 0.26, and ICC(2) of 0.75 (Shrout & Fleiss, 1979; McGraw & Wong, 1996),
showing that there is sufficient agreement between team member ratings to justify
aggregation.
4.4.3. 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). The EM
algorithm is an iterative process with two steps for each iteration; the first step computes
expected values based on observed data and estimates from the last iteration, the second step
provides maximum-likelihood estimates of the parameters in question based on values from
the first step. 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. We then computed simple descriptives and zero-order correlations,
conducted ANOVAs to compute ICC(1)s, and calculated the r WG(J) for intragroup trust .
To test our hypotheses, we had to take into account that subjects who originate from
different teams within an organization, as in the present sample, are nested within teams, with
the consequence that data are non-independent (Bliese, 1998; Hoffman, 1997; Hox, 2002;
Raudenbush and Bryk, 2002). If non-independence is not accounted for, standard errors are
too small and results from regression-based statistics are biased. Employing the HLM 6.08
statistical package, we thus built the following two-level models to test our hypotheses: In a
first step, we entered the control variables at the individual and team level (Model 1). These
and all other variables were entered into the models centered around their grand mean. In a
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