Mixture of latent trait analyzers for model-based clustering of categorical data

Model-based clustering methods for continuous data are well established and commonly used in a wide range of applications. However, model-based clustering methods for categorical data are less standard. Latent class analysis is a commonly used method for model-based clustering of binary data and/or categorical data, but due to an assumed local independence structure there may not be a correspondence between the estimated latent classes and groups in the population of interest. The mixture of latent trait analyzers model extends latent class analysis by assuming a model for the categorical response variables that depends on both a categorical latent class and a continuous latent trait variable; the discrete latent class accommodates group structure and the continuous latent trait accommodates dependence within these groups. Fitting the mixture of latent trait analyzers model is potentially difficult because the likelihood function involves an integral that cannot be evaluated analytically. We develop a variational approach for fitting the mixture of latent trait models and this provides an efficient model fitting strategy. The mixture of latent trait analyzers model is demonstrated on the analysis of data from the National Long Term Care Survey (NLTCS) and voting in the U.S. Congress. The model is shown to yield intuitive clustering results and it gives a much better fit than either latent class analysis or latent trait analysis alone.     

Die Kontaktgestaltung im Coaching

This article focusses on the many forms of the contact behaviour of systems. To this end, the gestalt theoretical concept of contact, and the system functions of the sociological system theory are combined to a new approach. In the second part of the article, this approach is transferred to the counseling format of coaching and ultimately made usable for coaches.     

Evaluating Probabilistic Forecasts with Bayesian Signal Detection Models

We propose the use of signal detection theory (SDT) to evaluate the performance of both probabilistic forecasting systems and individual forecasters. The main advantage of SDT is that it provides a principled way to distinguish the response from system diagnosticity, which is defined as the ability to distinguish events that occur from those that do not. There are two challenges in applying SDT to probabilistic forecasts. First, the SDT model must handle judged probabilities rather than the conventional binary decisions. Second, the model must be able to operate in the presence of sparse data generated within the context of human forecasting systems. Our approach is to specify a model of how individual forecasts are generated from underlying representations and use Bayesian inference to estimate the underlying latent parameters. Given our estimate of the underlying representations, features of the classic SDT model, such as the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), follow immediately. We show how our approach allows ROC curves and AUCs to be applied to individuals within a group of forecasters, estimated as a function of time, and extended to measure differences in forecastability across different domains. Among the advantages of this method is that it depends only on the ordinal properties of the probabilistic forecasts. We conclude with a brief discussion of how this approach might facilitate decision making.     

A Bayesian approach to non-parametric monotone function estimation

Summary.  The paper proposes two Bayesian approaches to non-parametric monotone function estimation. The first approach uses a hierarchical Bayes framework and a characterization of smooth monotone functions given by Ramsay that allows unconstrained estimation. The second approach uses a Bayesian regression spline model of Smith and Kohn with a mixture distribution of constrained normal distributions as the prior for the regression coefficients to ensure the monotonicity of the resulting function estimate. The small sample properties of the two function estimators across a range of functions are provided via simulation and compared with existing methods. Asymptotic results are also given that show that Bayesian methods provide consistent function estimators for a large class of smooth functions. An example is provided involving economic demand functions that illustrates the application of the constrained regression spline estimator in the context of a multiple-regression model where two functions are constrained to be monotone.     

Grey seals red in tooth and claw: how Darwin helps model their population

Grey seals in Britain are a conservation success, brought back from the brink of extinction by protection in the early 20th century. The public love them. Fishermen hate them. The resurgence of the "grey guzzlers" threatens their livelihood, they say. Len Thomas shows how Charles Darwin's ideas are used to determine seal numbers and population dynamics. Surprisingly, Darwin's theories have also influenced the statistical tools that are used.     

Confronting or Complementing? A Case Study on NGO–State Relations from Kerala, India

NGOs have, of late, found some of their traditional domains, such as provision of micro-credit and participatory development, coinciding with or being taken away by the state. How do they position themselves and retain relevance vis-a-vis the state in the changed scenario? Tracing the trajectory of interventions of a local NGO in Kerala, India, this article shows that NGOs exhibit ‘multiple identities’—selective collaboration, gap-filling and posing alternatives—in the process of engagement with the state. The ‘strategizing’ of such identities may hold the key to their relevance vis-a-vis the state.     

‘We’re Civil Servants’: The Status of Trauma-Informed Care in the Community

ABSTRACT

Trauma-informed care (TIC) in social service organizations means that the organizations operate with the understanding that everyone involved has possibly experienced trauma in their lifetime. This qualitative study examined local service organizations’ usage of the 5 main principles of TIC: safety, trustworthiness, collaboration, empowerment, and choice (as developed by Fallot & Harris, 2006). Ten focus groups (n = 69) and 6 individual interviews (n = 6) with employees from administration through management were interviewed such that almost all facets of each agency were represented. The participants were asked about their agencies’ policies and practices for utilizing the 5 principles of TIC. The results suggest that the vast majority of organizations in this study implemented many of the principles of TIC with clients, though they had not labeled their practices as “trauma-informed.” However, although clients were receiving TIC, some of the principles were neglected as they pertain to staff, such as choice and empowerment. The findings of this study suggest that agencies are unaware of the relevance of TIC as it relates to staff. It is recommended that future research examine whether the use of TIC in agencies prevents “burnout,” high turnover rates, and vicarious traumatization of staff.     

Beyond talking – children's participation in Norwegian care and protection cases

The attempt to give children an effective voice in social work processes which can have substantial impact on their lives takes different forms in countries with varying professional cultures and legal frameworks. This paper reports on a study of children's participation in decision-making in care and protection services in Norway, which was carried out in conjunction with a project to support social work teams in enabling children to participate, using materials borrowed from England and Wales. The results showed that (a) taking an active part in decision-making did not correlate perfectly with (b) having an influence on the outcome. Cases were therefore classified as ‘participation’ or ‘non-participation’ using a combination of both the above criteria. Statistical analysis of the factors influencing children's participation enabled the proposition of a model which appeared to explain much of the variance. The results point to the inadequacy of equating social work conversation with children with effective participation in decision-making.