A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models, combine features of generalized linear mixed models and structural equation models and consist of a response model and a structural model for the latent variables. The response model generalizes GLMMs to incorporate factor structures in addition to random intercepts and coefficients. As in GLMMs, the data can have an arbitrary number of levels and can be highly unbalanced (...) with different numbers of lower-level units in the higher-level units and missing data. A wide range of response processes can be modeled including ordered and unordered categorical responses, counts, and responses of mixed types. The structural model is similar to the structural part of a SEM except that it may include latent and observed variables varying at different levels. For example, unit-level latent variables can be regressed on cluster-level latent variables. Special cases of this framework are explored and data from the British Social Attitudes Survey are used for illustration. Maximum likelihood estimation and empirical Bayes latent score prediction within the GLLAMM framework can be performed using adaptive quadrature in gllamm, a freely available program running in Stata. (shrink)

One sector that severely suffers from the outbreak of the coronavirus is carsharing. The downswing of the carsharing industry may not only experience negative economic consequences but also ecological ones. Carsharing has the potential to reduce emissions, occupied space, and congestion and hence can actively contribute to mitigating climate change. As Bill Gates strikingly states: “Covid-19 is awful. Climate change could be worse.” For this reason, it is important to understand which underlying mechanisms drive carsharing usage during the Covid-19 pandemic. (...) The current research has the overall objective to provide deeper insights into the mediating mechanisms that explain carsharing usage intention during the Covid-19 pandemic. In particular, we draw on signaling theory to explore how different claims that prompt two different opposing underlying processes influence carsharing usage intention. An online experiment employing a 3 × 2 between-subjects design with participants acquired by the online panel platform Clickworker was conducted in April 2020. Fictitious labels and fictitious advertisements served as stimulus material and constituted the five experimental conditions. The data were analyzed by a multicategorial moderated mediation analysis and a multivariate analysis of covariance. Results reveal that environmental claims can stimulate perceived ecological benefits, which, in turn, positively affect carsharing usage intention. Interestingly, our research demonstrates that safety claims cannot decrease perceived physical risk in the context of Covid-19 and carsharing. Nevertheless, perceived physical risk has a negative influence on carsharing usage intention and hence should not be discarded altogether. The findings of this article offer new insights into the mental processes that guide consumer decision-making during the coronavirus crisis and also offer important policy implications by highlighting the relevance of environmental claims during the Covid-19 pandemic. Furthermore, the negative influence of perceived physical risk on carsharing usage intention points to the need for alternative measures to reduce users' risk perceptions. (shrink)

Models of risk-taking as used in the social sciences may be improved by including concepts from life history theory, particularly environmental unpredictability and life expectancy. Community college students completed self-report questionnaires measuring these constructs along with several well-known correlates. The frequency of risk-taking was higher for those with higher future unpredictability beliefs and shorter lifespan estimates (as measured by the Future Lifespan Assessment developed for this study), and unpredictability beliefs remained significant after accounting for standard predictors, such as sex and (...) temperament. The results demonstrate the usefulness of applying concepts from life history theory to enhance our understanding of human behavior. (shrink)