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In this paper, we propose a cluster-MDS model for two-way one-mode continuous rating dissimilarity data. The model aims at
partitioning the objects into classes and simultaneously representing the cluster centers in a low-dimensional space. Under
the normal distribution assumption, a latent class model is developed in terms of the set of dissimilarities in a maximum
likelihood framework. In each iteration, the probability that a dissimilarity belongs to each of the blocks conforming to
a partition of the original dissimilarity matrix, and the rest of parameters, are estimated in a simulated annealing based
algorithm. A model selection strategy is used to test the number of latent classes and the dimensionality of the problem.
Both simulated and classical dissimilarity data are analyzed to illustrate the model. 相似文献
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In this article we propose a model-free diagnostic for single-peakedness (unimodality) of item responses. Presuming a unidimensional unfolding scale and a given item ordering, we approximate item response functions of all items based on ordered conditional means (OCM). The proposed OCM methodology is based on Thurstone &; Chave's (1929) criterion of irrelevance, which is a graphical, exploratory method for evaluating the “relevance” of dichotomous attitude items. We generalized this criterion to graded response items and quantified the relevance by fitting a unimodal smoother. The resulting goodness-of-fit was used to determine item fit and aggregated scale fit. Based on a simulation procedure, cutoff values were proposed for the measures of item fit. These cutoff values showed high power rates and acceptable Type I error rates. We present 2 applications of the OCM method. First, we apply the OCM method to personality data from the Developmental Profile; second, we analyze attitude data collected by Roberts and Laughlin (1996) concerning opinions of capital punishment. 相似文献
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José Fernando Vera Mark de Rooij Willem J. Heiser 《The British journal of mathematical and statistical psychology》2014,67(3):514-540
In this paper we propose a latent class distance association model for clustering in the predictor space of large contingency tables with a categorical response variable. The rows of such a table are characterized as profiles of a set of explanatory variables, while the columns represent a single outcome variable. In many cases such tables are sparse, with many zero entries, which makes traditional models problematic. By clustering the row profiles into a few specific classes and representing these together with the categories of the response variable in a low‐dimensional Euclidean space using a distance association model, a parsimonious prediction model can be obtained. A generalized EM algorithm is proposed to estimate the model parameters and the adjusted Bayesian information criterion statistic is employed to test the number of mixture components and the dimensionality of the representation. An empirical example highlighting the advantages of the new approach and comparing it with traditional approaches is presented. 相似文献
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