The effect of choice on motivation for young children on the autism spectrum during discrete trial teaching

Self-determination and decision-making are acknowledged internationally as key rights of persons with disabilities and should play an important role in the development of educational plans and procedures. Not only is the chance for individuals with developmental disabilities to select their own tasks, leisure activities or reinforcers a valuable way of enhancing rights-based education and personal dignity, but choice-making opportunities may also function as a useful clinical or educational tool if they actually improve the efficacy of programmes aimed at the acquisition of socially relevant behaviours and life skills or the reduction of challenging behaviours.

The study reported here assessed whether or not choice affected effectiveness of an educational procedure for three children on the autism spectrum. Following a preference assessment, a number of discrete teaching trials were conducted with each child and, contingent upon targeted responses, either the child or the therapist selected one of three preferred reinforcer items. Reinforcer choice did not affect intervention effectiveness for two of the children; however, performance and motivation improved for the third child. Results re-affirmed the importance of thorough preference assessments prior to intervention and showed that additional stimulus choice contingent on the target response may improve motivation and outcomes for some children.

Assessment of the benefits of Discrete Conditional Survival Models in modelling ambulance response times

Many of the challenges faced in health care delivery can be informed through building models. In particular, Discrete Conditional Survival (DCS) models, recently under development, can provide policymakers with a flexible tool to assess time-to-event data. The DCS model is capable of modelling the survival curve based on various underlying distribution types and is capable of clustering or grouping observations (based on other covariate information) external to the distribution fits. The flexibility of the model comes through the choice of data mining techniques that are available in ascertaining the different subsets and also in the choice of distribution types available in modelling these informed subsets. This paper presents an illustrated example of the Discrete Conditional Survival model being deployed to represent ambulance response-times by a fully parameterised model. This model is contrasted against use of a parametric accelerated failure-time model, illustrating the strength and usefulness of Discrete Conditional Survival models.

Cheap and expensive alternatives in stated choice experiments: are they equally considered by respondents?

When analysing choice experiments respondents are assumed to attend all attributes and alternatives in the same way. However, because of the important role that the price of the alternatives can play in the decision-making process, the level of price of a specific alternative may have consequences on the level of consideration given to the remaining attributes of the alternative. In this article, we propose the use of a discrete mixtures logit approach to accommodate respondents ignoring alternatives in stated choice experiments. Our results indicate a higher propensity for respondents to ignore attributes when they face cheap rather than expensive alternatives. We also find that allowing for this leads to substantial improvements in the model performance.

A field experiment on a steel Gerber-truss bridge for damage detection utilizing vehicle-induced vibrations

A field experiment was conducted on a real continuous steel Gerber-truss bridge with artificial damage applied. This article summarizes the results of the experiment for bridge damage detection utilizing traffic-induced vibrations. It investigates the sensitivities of a number of quantities to bridge damage including the identified modal parameters and their statistical patterns, Nair’s damage indicator and its statistical pattern and different sets of measurement points. The modal parameters are identified by autoregressive time-series models. The decision on bridge health condition is made and the sensitivity of variables is evaluated with the aid of the Mahalanobis–Taguchi system, a multivariate pattern recognition tool. Several observations are made as follows. For the modal parameters, although bridge damage detection can be achieved by performing Mahalanobis–Taguchi system on certain modal parameters of certain sets of measurement points, difficulties were faced in subjective selection of meaningful bridge modes and low sensitivity of the statistical pattern of the modal parameters to damage. For Nair’s damage indicator, bridge damage detection could be achieved by performing Mahalanobis–Taguchi system on Nair’s damage indicators of most sets of measurement points. As a damage indicator, Nair’s damage indicator was superior to the modal parameters. Three main advantages were observed: it does not require any subjective decision in calculating Nair’s damage indicator, thus potential human errors can be prevented and an automatic detection task can be achieved; its statistical pattern has high sensitivity to damage and, finally, it is flexible regarding the choice of sets of measurement points.