Determinants of Emotional Experiences in Traffic Situations ... - OPUS

Introducing a Multivariate Model for Predicting Driving Performance 109
To further account for the influence of single personal characteristics on anger and driving
performance, a structure equation model approach with AMOS 19 as the statistical tool was used
(Figure 2).
* p< .05; ** p< .01; *** p< .001
Figure 2: Path Models of personal characteristics, reported anger and driving parameters in the first
study.
The quality indices for all models were the comparative fit index (CFI, where a value above .90 is
acceptable), a root mean square error of approximation (RMSEA, where a value below .005 indicates
a close fit between data and the model) and the χ2-Value (where a non-significant value indicates a
good fit between sample covariance and fitted covariance) (Bentler, 1990; Arbuckle & Wothke, 1999).
Before the analysis was started, Pearson product-moment correlations and multicolinearity statistics
among independent variables were calculated. The values of the conditioning index were below .20
indicating no multicolinearity.
The indices for the quality of the model fit were in the expected range: χ2-Values were
between 0.78 and 1.14, CFI- and RMSEA-Values of all four models were always at the optimum
(1 for CFI and 0 for RMSEA, respectively). Compared to the multiple regressions, the path models
showed a similar pattern of influences from personal characteristics and anger on driving parameters.
Focussing on driving motivation and gender both were positively associated with higher speeds
(β = .25), more speeding violations (β = .26 and .31), longitudinal acceleration (only gender with β =
.37) and lateral acceleration (only driving motivation: β = .25). Anger itself couldn’t be explained
!