Identifiable finite mixtures of location models for clustering mixed-mode data

For clustering mixed categorical and continuous data, Lawrence and Krzanowski (1996) proposed a finite mixture model in which component densities conform to the location model. In the graphical models literature the location model is known as the homogeneous Conditional Gaussian model. In this paper it is shown that their model is not identifiable without imposing additional restrictions. Specifically, for g groups and m locations, (g!)m–1 distinct sets of parameter values (not including permutations of the group mixing parameters) produce the same likelihood function. Excessive shrinkage of parameter estimates in a simulation experiment reported by Lawrence and Krzanowski (1996) is shown to be an artifact of the model's non-identifiability. Identifiable finite mixture models can be obtained by imposing restrictions on the conditional means of the continuous variables. These new identified models are assessed in simulation experiments. The conditional mean structure of the continuous variables in the restricted location mixture models is similar to that in the underlying variable mixture models proposed by Everitt (1988), but the restricted location mixture models are more computationally tractable.     

Responsible Conduct of Research Measure: initial development and pilot study

Although much discussion has been focused on research misconduct (RM) and questionable research practices, to date no self-report measures exist to examine this phenomenon. To help fill this void, the authors developed the Responsible Conduct of Research Measure (RCRM) through multiple pilot study waves involving researchers in the social and behavioral sciences. Preliminary results reveal adequate validity and reliability. The authors discuss limitations of the study as well as some possible directions for future research on this topic.