Abnormal motor behavior associated with psychopathology was investigated in this study using a nontraditional inventory, the Movement Psychodiagnostic Inventory, with a sample of psychiatric patients that included personality disorders and individuals with schizophrenia and schizophrenia spectrum disorders. The study was designed to address several methodological and measurement problems noted in the literature, and to this end ratings were made from video-taped observations allowing detection of subtle movements described in terms of their dynamic, spatial, rhythmic, and muscular involvement rather than labeled with traditional terminology. In addition, parameters of nonverbal communicative behavior were also rated. Even though diagnostic groups not associated with disturbed motor behavior were included, univariate analysis failed to reveal differences between diagnostic groups with a long history of association with abnormal motor behavior and those without such a history. Multidimensional scaling and cluster analysis were thus employed to uncover the structure of the data in exploratory analyses that used two distance measures and several methods each of multidimensional scaling and cluster analysis. Results were interpreted to indicate that different patterns of movement behavior were observed for each group of patients that were not measure- or method-dependent. The results of the study are discussed within the context of the potential for the inventory and the advancement of the study of motor behavior and psychopathology, an area that has the potential to effect progress in diagnosis, etiology, pathogenesis, and treatment of mental disorders.

There has been great advancement on research for preferential choice in field of marketing. When we look at preferential choice data, there are two components to consider: the individuals and the items. Coombs (1950; 1964) introduced the unfolding technique on preferential choice data. In 1960, Bennett and Hays went on to create a multidimensional unfolding model. Hojo (1997;1998) showed rank data could be used in multidimensional scaling, however he did not implement a Bayesian technique. In 2010, Fong, DeSarbo, Park, and Scott proposed a new Bayesian vector Multidimensional Scaling (MDS) model which was applied to data from a five-point Likert scale survey. This paper focused on Bayesian approach choice behavior multidimensional space model for the analysis of partially ranked data (rank top 3 from J data) to provide a joint space of individuals and products, using MCMC procedure. The procedure is similar to what Fong, DeSarbo, Park, and Scott (2010) did but this study used partial rank data instead of Likert scale data. The goal of this study was to create a probability-based model that calculates the average product utility which indicates how popular the product is. Lambdas or the item loadings are the direction of the products and thetas are the direction for the individuals. In addition, this study dealt with rotational invariance by calculating the optimal lambda values for each iteration and each dimension by flipping the sign so it approaches the average value. To determine the number of dimensions of the datasets, the sum of squared loadings were calculated. We applied the MCMC procedure to simulated data in which we sampled the loadings from the normal distribution as well as loadings from the real datasets. In addition, we applied the MCMC procedure to the real dataset and created a multidimensional space for the products.

In recent years, cognitive diagnostic models (CDMs) have been widely used in educational assessment to provide a diagnostic profile (mastery/non-mastery) analysis for examinees, which gives insights into learning and teaching. However, there is often uncertainty about the specification of the Q-matrix that is required for CDMs, given that it is based on expert judgment. The current study uses a Bayesian approach to examine recovery of Q-matrix elements in the presence of uncertainty about some elements. The first simulation examined the situation where there is complete uncertainty about whether or not an attribute is required, when in fact it is required. The simulation results showed that recovery was generally excellent. However, recovery broke down when other elements of the Q-matrix were misspecified. Further simulations showed that, if one has some information about the attributes for a few items, then recovery improves considerably, but this also depends on how many other elements are misspecified. A second set of simulations examined the situation where uncertain Q-matrix elements were scattered throughout the Q-matrix. Recovery was generally excellent, even when some other elements were misspecified. A third set of simulations showed that using more informative priors did not uniformly improve recovery. An application of the approach to data from TIMSS (2007) suggested some alternative Q-matrices.

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Thesis--University of Michigan. / Bibliography: p. 109-113.

Thesis (M. Soc. Sc.)--University of Hong Kong, 1991.

Thesis (M.A.)--University of Missouri-Columbia, 2007. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on May 28, 2009) Includes bibliographical references.

Thesis (Ph. D.)--University of Texas at Austin, 1998. / Vita. Includes bibliographical references (leaves 109-113). Available also in a digital version from Dissertation Abstracts.