Suchergebnisse

Abstract  In the Netherlands, the Dutch government instituted policies that enable persons with profound intellectual and multiple disabilities (PIMD) to attend day services. Over the past 15 years, surveys have indicated a progressive increase in the number of hours that such adults spend at day activities centers. However, information about how these hours are spent is lacking. This study was designed to sample 7 residential centers to determine the amount of time spent in day activities centers, the number of hours that were spent on fixed programming or individually orientated activities, and what types of activities were prominent. Results showed that persons with PIMD spent, on average, 14.2 h per week at day activities centers. Of that time, 28.9% was spent on group activities and 13.0% on individual activities. Whether these activities could be considered purposeful or productive for an individual participant is unknown. The authors question whether the policies of the Dutch government aimed at enabling persons with PIMD to enjoy purposeful, rather than just longer, activities during the day, have been productively implemented.

The p2model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p2model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p2model defines an identical p2model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p2model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.

With the development of an MCMC algorithm, Bayesian model selection for thep2model for directed graphs has become possible. This paper presents an empirical exploration in using approximate Bayes factors for model selection. For a social network of Dutch secondary school pupils from different ethnic backgrounds it is investigated whether pupils report that they receive more emotional support from within their own ethnic group. Approximated Bayes factors seem to work, but considerable margins of error have to be reckoned with.

A random effects model is proposed for the analysis of binary dyadic data that represent a social network or directed graph, using nodal and/or dyadic attributes as covariates. The network structure is reflected by modeling the dependence between the relations to and from the same actor or node. Parameter estimates are proposed that are based on an iterated generalized least‐squares procedure. An application is presented to a data set on friendship relations between American lawyers.