Why are there sex differences in risky driving? the relationship between sex and gender‐role on aggressive driving,traffic offences,and accident involvement among young turkish drivers

The aim of this study was to investigate how sex (male and female) and gender‐role (masculinity and femininity) and their interaction were associated with risky driving behaviors, traffic offences, and accident involvement among young Turkish drivers. Three‐hundred and fifty‐four young drivers (221 males and 133 females) filled in a form including the short form of Bem Sex‐Role Inventory (BSRI), the Driver Behaviour Questionnaire (DBQ), questions about a driver's accident history, and background information. The effects were tested on outcome variables by using hierarchical regression analysis. It was found that sex (being male) predicted only the ordinary violations. While masculinity score predicted positively the number of offences, and aggressive and ordinary (highway code) violations, femininity score predicted negatively the number of accidents and offences, aggressive and ordinary violations, and errors. The effect of interaction between masculinity and femininity was only found on the number of accidents and aggressive violations among young drivers. There was no significant interaction effect between sex and gender roles on criterion variables. Aggr. Behav. 00:1–12, 2005. © 2005 Wiley‐Liss, Inc.     

What can we learn from Plausible Values?

In this paper, we show that the marginal distribution of plausible values is a consistent estimator of the true latent variable distribution, and, furthermore, that convergence is monotone in an embedding in which the number of items tends to infinity. We use this result to clarify some of the misconceptions that exist about plausible values, and also show how they can be used in the analyses of educational surveys.     

Revealing Hidden Curvilinear Relations Between Work Engagement and Its Predictors: Demonstrating the Added Value of Generalized Additive Model (GAM)

Previous studies measuring different aspects of the quality of life have, as a rule, presumed linear relationships between a dependent variable and its predictors. This article utilizes non-parametric statistical methodology to explore curvilinear relations between work engagement and its main predictors: job demands, job control and social support. Firstly, the study examines what additional information non-linear modeling can reveal regarding the relationship between work engagement and the three predictors in question. Secondly, the article compares the explanatory power of non-linear and linear modeling with regard to work engagement. The generalized additive model (GAM), that makes possible non-linear modeling, is compared with the widely used simply linear generalized linear model (GML) procedure. Based on the survey data (N = 7,867) collected in eight European countries in 2007, the article presents the following main results. GAM clearly fitted the data better than GLM. All investigated job characteristics had curvilinear relationships with work engagement, although job demands and job control relationships were almost linear. Social support had a clear U-shaped curvilinear connection to work engagement. Interactions between the three job characteristics were also found. Interaction between job demands and social support was curvilinear in shape. Finally, GAM proved to be a more practical and efficient tool of analysis than GLM in situations where there are reasons to assume curvilinear relationships, complex interactions effects between predictors.