The name is absent

M. Wilke et al.: Risk Specificity for Risk-Defusing Operators

Table 2

Explanation of Coding Categories Shown With Examples From the Scenario “Avian Flu Pandemic” (see Appendix)

Logit analyses describe a direct relation between inde-
pendent and dependent variables. The aim of the estimation
of multiple influence factors (type of risk and risk domain)
is to make their specific effects visible (Urban, 1993). For
example, the natural logarithm of the ratio between the fre-
quencies of both categories of dependent variables (no vs.
at least one question or statement concerning new alterna-
tives, possibilities for control, or worst-case plans) are rep-
resented as a sum of effect parameters (λ coefficients) un-
der the influence of selected categories of independent
variables (e.g., of the interaction between the normal type
of risk and the risk domain of politics). The explained quan-
tities are therefore not the cell frequencies themselves, but
the ratio between two probabilities of specific expressions
of variables, so-called “odds ratios” (Andreβ, Hagenaars,
& Kühnel, 1997).

This way, the directed hypotheses regarding the influ-
ence of type of risk as well as other possible differences in
the search for RDOs could be tested. Based on the compu-
tation of λ coefficients, the rank order of the influence fac-
tors type of risk, risk domain, and interaction on the influ-
ence of the three RDOs new alternatives, possibilities for
control, and worst-case plans was computed. The estima-
tion of parameters and effect sizes from the logit model is
based on maximum likelihood. Using one-dimensional χ²
tests, the frequency distributions of different variables like
background knowledge, attitude, situation, probability, and
positive and negative consequences were analyzed in rela-
tion to the four types of risks.

Results

Mean frequencies can be misleading if one participant asks
a lot of questions in one category and other participants ask
no questions at all (see Huber et al., 2001). Therefore, we
counted the number of participants with at least one state-
ment or question in a given category.

Data Analysis Based on Logits

First, we specified a model which identified the main ef-
fects of type of risk and risk domain as well as the interac-
tions between them. The resulting Pearson χ² (p = 0.64) and
the likelihood ratio (p = 0.34) showed that the observed and
expected values were congruent. Thus, the specified mod-
el can be used to explain our data.

Testing for risk specificity of active risk defusing re-
vealed a significant negative λ coefficient for possibilities
for control and worst-case plans for normal risks: λ = -0.85,
p < 0.05. For normal risks, fewer participants formulated
questions or statements for possibilities for control and
worst-case plans than for new alternatives. For medium

Swiss J Psychol 67 (1), © 2008 by Verlag Hans Huber, Hogrefe AG, Bern

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