20190926_040837144.pdf - acts of risk-taking in traffic such as speeding tailgating not stopping when the traffic light turn red driving too close to

20190926_040837144.pdf - acts of risk-taking in traffic...

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acts of risk-taking in traffic, such as speeding, tailgating, not stopping when the traffic light turn red, driving too close to the car in front, etc. (Rundmo, 1996; Rundmo & Ulleberg, 2000). Only the respondents who possessed a driving license were asked to fill out this part of the questionnaire. The respondents were asked to indicate how often they committed the different acts of risk-taking, ranging from never to very often. A complete list of the behavioural items with their mean score and standard deviation is presented in the Appendix. The respondents with a driving licence were also asked to report how many times they, as a driver, had been involved in a traffic accident. Statistical analysis Confirmatory as well as exploratory factor analyses were carried out to examine the structure of the attitude items. First, confirmatory factor analysis (maximum- likelihood method) was performed to assess how well the original four factor structure of the YDAS fit the data. The covariance matrix of the YDAS was analysed by means of the LISREL 8 Program (Jöreskog & Sörbom, 1993). 105 Paper I: Risk-taking attitudes Thereafter, a principal component analysis (PCA), with varimax rotation, was used to determine the underlying dimensionality of the YDAS items. In order to exclude unreliable items from the YDAS, items with a factor loading below .50 were excluded. The final clusterings of items obtained in the PCA were interpreted to indicate different dimensions of the risk-taking attitudes of the YDAS. Each factor’s theoretical substance was evaluated on the basis of the content of the items clustering on the factor. With the purpose of comparing the fit of the factor structure suggested by Malfetti et al. (1989) and the factor structure suggested by the exploratory PCA, various fit indexes were used: the goodness-of-fit index (GFI), the adjusted goodness-of-fit index (AGFI), the comparative fit index (CFI), the root mean square error of approximation (RMSEA), and the expected cross-validiation index (ECVI). Traditionally, a GFI, an AGFI, and a CFI above .90 have been an agreed-upon cutoff criteria, indicating a close fit between the model and the data (Hoyle and Panter, 1995; Hu & Bentler, 1995; Loehlin, 1998). However, Hu and Bentler (1999) later concluded that the CFI should be close to .95 in order to claim a good fit between the hypothesized model and the observed data. An RMSEA of .05 or
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  • Fall '15
  • YDAS

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