Effects of an Antecedent Intervention on Repetitive Behaviors of a Child With Autism

Repetitive self-injurious eye poking and stereotypic hand clapping were assessed in a child with autism and hearing impairment. A functional behavioral assessment conducted to identify the variables maintaining eye-poking behaviors indicated automatic reinforcement. Choice and environmental enrichment strategies were implemented to decrease the motivating operations associated with eye poking. Providing a choice of toys to hold in his hand led to the greatest reductions in both repetitive eye poking and hand clapping. Three follow-up observations were conducted 9 months after the intervention, which indicated that eye poking was eliminated and hand clapping was greatly reduced. This antecedent intervention was simple and effective, two hallmarks of sustainable behavior interventions.     

An exploratory mindset reduces preference for prototypes and increases preference for novel exemplars

Previous research has shown that prototypes of familiar categories are preferred over novel exemplars of familiar and unfamiliar categories. The present research demonstrates a reversal of this effect by simply inducing an exploratory mindset. Specifically, participants were asked to judge the attractiveness of dot patterns that represented prototypes of familiar categories, exemplars of familiar categories, or exemplars of novel categories. An exploratory mindset was manipulated by asking participants to imagine the stimuli as stars (versus as peas). Results show that participants in the exploration condition preferred exemplars of novel categories (thereby reversing the classical prototypicality effect), whereas participants in the control condition preferred prototypes. The role of mindsets and familiarity in attractiveness ratings is discussed.     

Generalized full-information item bifactor analysis

Full-information item bifactor analysis is an important statistical method in psychological and educational measurement. Current methods are limited to single-group analysis and inflexible in the types of item response models supported. We propose a flexible multiple-group item bifactor analysis framework that supports a variety of multidimensional item response theory models for an arbitrary mixing of dichotomous, ordinal, and nominal items. The extended item bifactor model also enables the estimation of latent variable means and variances when data from more than 1 group are present. Generalized user-defined parameter restrictions are permitted within or across groups. We derive an efficient full-information maximum marginal likelihood estimator. Our estimation method achieves substantial computational savings by extending Gibbons and Hedeker's (1992) bifactor dimension reduction method so that the optimization of the marginal log-likelihood requires only 2-dimensional integration regardless of the dimensionality of the latent variables. We use simulation studies to demonstrate the flexibility and accuracy of the proposed methods. We apply the model to study cross-country differences, including differential item functioning, using data from a large international education survey on mathematics literacy.