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Abstract: Cultural Consensus Theory (CCT) is a popular information pooling methodology used in the social and behavioral sciences. CCT consists of cognitive models designed to determine a consensus truth shared by a group of informants (respondents), and to better understand the cognitive characteristics of the informants (e.g. level knowledge, response biases). However prior to this paper, no CCT models have been developed that allow the possibility of the informant responses to come from a mixture of two or more consensus answer patterns. The major advance in the current paper is to endow one of the popular CCT models, the General Condorcet Model (GCM) for dichotomous responses, with the possibility of having several latent consensus answer patterns, each corresponding to a different, latent subgroup of informants. In addition, we augment the model to allow the possibility of questions having differential difficulty (cultural saliency). This is the first CCT finite-mixture model, and it is named the Multi-Culture GCM (MC-GCM). The model is developed axiomatically and a notable property is derived that can suggest the appropriate number of mixtures for a given data set. The model is extended in a hierarchical Bayesian framework and its application is successfully demonstrated on both simulated and real data, including a new experimental data set on political viewpoints. [Copyright &y& Elsevier]

Abstract: Experimentation is ubiquitous in the field of psychology and fundamental to the advancement of its science, and one of the biggest challenges for researchers is designing experiments that can conclusively discriminate the theoretical hypotheses or models under investigation. The recognition of this challenge has led to the development of sophisticated statistical methods that aid in the design of experiments and that are within the reach of everyday experimental scientists. This tutorial paper introduces the reader to an implementable experimentation methodology, dubbed Adaptive Design Optimization, that can help scientists to conduct “smart” experiments that are maximally informative and highly efficient, which in turn should accelerate scientific discovery in psychology and beyond. [Copyright &y& Elsevier]