Recent detection methods for Differential Item Functioning (DIF) include approaches like Rasch Trees, DIFlasso, GPCMlasso and Item Focussed Trees, all of which -in contrast to well established methods -can handle metric covariates inducing DIF. A new estimation method shall address their downsides by mainly aiming at combining three central virtues: the use of conditional likelihood for estimation, the incorporation of linear influence of metric covariates on item difficulty and the possibility to detect different DIF types: certain items showing DIF, certain covariates inducing DIF, or certain covariates inducing DIF in certain items.Each of the approaches mentioned lacks in two of these aspects. We introduce a method for DIF detection, which firstly utilizes the conditional likelihood for estimation combined with group Lasso-penalization for item or variable selection and L1-penalization for interaction selection, secondly incorporates linear effects instead of approximation through step functions, and thirdly provides the possibility to investigate any of the three DIF types. The method is described theoretically, challenges in implementation are discussed. A dataset is analysed for all DIF types and shows comparable results between methods. Simulation studies per DIF type reveal competitive performance of cmlDIFlasso, particularly when selecting interactions in case of large sample sizes and numbers of parameters. Coupled with low computation times, cmlDIFlasso seems a worthwhile option for applied DIF detection.

<p>  </p> <p>Risk assessment and behavior of social entities, such as societies, organizations or groups, are shaped by shared values and beliefs. Such shared convictions on how risk is perceived and handled are widely labeled as risk culture. While risk culture is a promising approach for comprehensively considering risk aspects in social dynamics, its structure still lacks conceptual clarity. In this regard, the recently introduced Risk Culture Framework (RCF) was aimed at providing an operationalization foundation for risk culture research through a 3x3 grid representing different cultural levels and influencing domains. However, until now, the RCF has neither been empirically applied nor tested. In the current study, the structural fit of the model is evaluated using empirical data pertaining to health risks gathered by an exploratory questionnaire (<em>N</em> = 500). For the sake of methodological consistency, the cultural level of implicit factors was not considered due to its methodological specificity. Confirmatory factor analyses were used to analyze the fit of the assumed model structure as well as that of other applicable models. Model indices for the RCF-oriented risk culture model structure were acceptable and better than those for the other models tested. Overall, results support the theoretical-based structure of the RCF, and provide a foundation for further research on risk culture. Future approaches and applications of the RCF to more specific risk subjects are discussed.</p>

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<p>  </p> <p>Risk assessment and behavior of social entities, such as societies, organizations or groups, are shaped by shared values and beliefs. Such shared convictions on how risk is perceived and handled are widely labeled as risk culture. While risk culture is a promising approach for comprehensively considering risk aspects in social dynamics, its structure still lacks conceptual clarity. In this regard, the recently introduced Risk Culture Framework (RCF) was aimed at providing an operationalization foundation for risk culture research through a 3x3 grid representing different cultural levels and influencing domains. However, until now, the RCF has neither been empirically applied nor tested. In the current study, the structural fit of the model is evaluated using empirical data pertaining to health risks gathered by an exploratory questionnaire (<em>N</em> = 500). For the sake of methodological consistency, the cultural level of implicit factors was not considered due to its methodological specificity. Confirmatory factor analyses were used to analyze the fit of the assumed model structure as well as that of other applicable models. Model indices for the RCF-oriented risk culture model structure were acceptable and better than those for the other models tested. Overall, results support the theoretical-based structure of the RCF, and provide a foundation for further research on risk culture. Future approaches and applications of the RCF to more specific risk subjects are discussed.</p>

A modified and improved inductive inferential approach to evaluate item discriminations in a conditional maximum likelihood and Rasch modeling framework is suggested. The new approach involves the derivation of four hypothesis tests. It implies a linear restriction of the assumed set of probability distributions in the classical approach that represents scenarios of different item discriminations in a straightforward and efficient manner. Its improvement is discussed, compared to classical procedures (tests and information criteria), and illustrated in Monte Carlo experiments as well as real data examples from educational research. The results show an improvement of power of the modified tests of up to 0.3.