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For the construction of tests and questionnaires that require multiple raters (e.g., a child behaviour checklist completed by both parents) a novel ordinal scaling technique is currently being further developed, called two‐level Mokken scale analysis. The technique uses within‐rater and between‐rater coefficients to assess the scalability of the test. These coefficients are generalizations of Mokken's scalability coefficients. In this paper we derived standard errors for the two‐level coefficients and for their ratios. The coefficients, the estimates, the estimated standard errors and the software implementation are discussed and illustrated using a real‐data example, and a small‐scale simulation study demonstrates the accuracy of the estimates. [ABSTRACT FROM AUTHOR]

The P2 model is a statistical model for the analysis of binary relational data with covariates, as occur in social network studies. It can be characterized as a multinomial regression model with crossed random effects that reflect actor heterogeneity and dependence between the ties from and to the same actor in the network. Three Markov chain Monte Carlo (MCMC) estimation methods for the P2 model are presented to improve iterative generalized least squares (IGLS) estimation developed earlier, two of which use random walk proposals. The third method, an independence chain sampler, and one of the random walk algorithms use normal approximations of the binary network data to generate proposals in the MCMC algorithms. A large-scale simulation study compares MCMC estimates with IGLS estimates for networks with 20 and 40 actors. It was found that the IGLS estimates have a smaller variance but are severely biased, while the MCMC estimates have a larger variance with a small bias. For networks with 20 actors, mean squared errors are generally comparable or smaller for the IGLS estimates. For networks with 40 actors, mean squared errors are the smallest for the MCMC estimates. Coverage rates of confidence intervals are good for the MCMC estimates but not for the IGLS estimates. [ABSTRACT FROM AUTHOR]

For this study, information on Who Bullies Who was collected from 54 school classes with 918 children ( M age = 11) and 13,606 dyadic relations. Bullying and victimization were viewed separately from the point of view of the bully and the victim. The two perspectives were highly complementary. The probability of a bully–victim relationship was higher if the bully was more dominant than the victim, and if the victim was more vulnerable than the bully and more rejected by the class. In a bully–victim dyad, boys were more often the bullies. There was no finding of sex effect for victimization. Liking reduced and disliking increased the probability of a bully–victim relationship. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With the development of an MCMC algorithm, Bayesian model selection for thep2 model 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The p(2) model is a statistical model for the analysis of binary relational data with covariates, as occur in social network studies. It can be characterized as a multinomial regression model with crossed random effects that reflect actor heterogeneity and dependence between the ties from and to the same actor in the network. Three Markov chain Monte Carlo (MCMC) estimation methods for the p(2) model are presented to improve iterative generalized least squares (IGLS) estimation developed earlier, two of which use random walk proposals. The third method, an independence chain sampler, and one of the random walk algorithms use normal approximations of the binary network data to generate proposals in the MCMC algorithms. A large-scale simulation study compares MCMC estimates with IGLS estimates for networks with 20 and 40 actors. It was found that the IGLS estimates have a smaller variance but are severely biased, while the MCMC estimates have a larger variance with a small bias. For networks with 20 actors, mean squared errors are generally comparable or smaller for the IGLS estimates. For networks with 40 actors, mean squared errors are the smallest for the MCMC estimates. Coverage rates of confidence intervals are good for the MCMC estimates but not for the IGLS estimates.