The purpose of this research was to compare the equating performance of various equating procedures for the multidimensional tests. To examine the various equating procedures, simulated data sets were used that were generated based on a multidimensional item response theory (MIRT) framework. Various equating procedures were examined, including both unidimensional and the multidimensional equating procedures based on an IRT framework in addition to traditional equating procedures. Specifically, the performance of the following six equating procedures under the random groups design was compared: (1) unidimensional IRT observed score equating, (2) unidimensional IRT true score equating, (3) full MIRT observed score equating, (4) unidimensionalized MIRT observed score equating, (5) unidimensionalized MIRT true score equating, and (6) equipercentile equating. A total of four factors (test length, sample size, form difficulty differences, and correlations between dimensions) were expected to impact equating performance, and their impacts were investigated by creating two conditions per each factor: long vs. short test, large vs. small sample size, some vs. no form differences, and high vs. low correlation between dimensions. This simulation study over 50 replications yielded several patterns of equating performance of the six procedures across the simulation conditions. The following six findings are notable: (1) the full MIRT procedure provided more accurate equating results (i.e., less degree of error) than other equating procedures especially when the correlation between dimensions was low; (2) the equipercentile procedure was more likely than the IRT methods to yield a larger amount of random error and overall error across all the conditions; (3) equating for multidimensional tests was more accurate when form differences were small, sample size was large, and test length was long; (4) even when multidimensional tests were used (i.e., the unidimensionality assumptions were violated), still the unidimensional IRT procedures were found to yield quite accurate equating results; and (5) whether an equating procedure is an observed or a true score procedure did not seem to yield any differences in equating results. Building upon these findings, some theoretical and practical implications are discussed, and future research directions are suggested to strengthen the generalizability of the current findings. Given that only a handful of studies have been conducted in the MIRT literature, such research is expected to examine the various specific conditions where these findings are likely to be hold, thereby leading to practical guidelines that can be used in various operational testing situations.

Public street surveillance, a domain of Closed Circuit Television (CCTV), has grown enormously and is becoming common place with increasing utilization in society as an all-purpose security tool. Previous authors (Ditton, 1999; Davies, 1998; Horne, 1998; Tomkins, 1998) have raised concern over social, civil and privacy issues, but there has been limited research to quantify these concerns. There are a number of core aspects that could relocate the risk perception and therefore, social support of public street surveillance. This study utilized the psychometric paradigm to quantitatively measure the social risk perception of public street surveillance. The psychometric paradigm is a method that presents risk perception in a two factor representation, being dread risk and familiarity to risk. Four additional control activities and technologies were tested, being radioactive waste, drinking water chlorination, coal mining disease and home swimming pools. Analysis included spatial representation, and multidimensional scaling (MDS) Euclidean and INDSCAL methods. The study utilized a seven point Likert scale, pre and post methodology, and had a target population of N=2106, with a sample of N=135 (alpha=0.7).

We present a new method for rendering novel images of flexible 3D objects from a small number of example images in correspondence. The strength of the method is the ability to synthesize images whose viewing position is significantly far away from the viewing cone of the example images ("view extrapolation"), yet without ever modeling the 3D structure of the scene. The method relies on synthesizing a chain of "trilinear tensors" that governs the warping function from the example images to the novel image, together with a multi-dimensional interpolation function that synthesizes the non-rigid motions of the viewed object from the virtual camera position. We show that two closely spaced example images alone are sufficient in practice to synthesize a significant viewing cone, thus demonstrating the ability of representing an object by a relatively small number of model images --- for the purpose of cheap and fast viewers that can run on standard hardware.

Data warehouses were envisioned to facilitate analytical reporting and data visualization by providing a model for the flow of data from operational databases to decision support environments. Decision support environments provide a multidimensional conceptual view of the underlying data warehouse, which is usually stored in relational DBMSs. Typically, there is an impedance mismatch between this conceptual view — shared also by all decision support applications accessing the data warehouse — and the physical model of the data stored in relational DBMSs. This thesis presents a mapping compilation algorithm in the context of the Conceptual Integration Model (CIM) [67] framework. In the CIM framework, the relationships between the conceptual model and the physical model are specified by a set of attribute-to-attribute correspondences. The algorithm compiles these correspondences into a set of mappings that associate each construct in the conceptual model with a query on the physical model. Moreover, the homogeneity and summarizability of data in conceptual models is the key to accurate query answering, a necessity in decision making environments. A data-driven approach to refactor relational models into summarizable schemas and instances is proposed as the solution of this issue. We outline the algorithms and challenges in bridging multidimensional conceptual models and the physical model of data warehouses and discuss experimental results.

Data warehouses were envisioned to facilitate analytical reporting and data visualization by providing a model for the flow of data from operational databases to decision support environments. Decision support environments provide a multidimensional conceptual view of the underlying data warehouse, which is usually stored in relational DBMSs. Typically, there is an impedance mismatch between this conceptual view — shared also by all decision support applications accessing the data warehouse — and the physical model of the data stored in relational DBMSs. This thesis presents a mapping compilation algorithm in the context of the Conceptual Integration Model (CIM) [67] framework. In the CIM framework, the relationships between the conceptual model and the physical model are specified by a set of attribute-to-attribute correspondences. The algorithm compiles these correspondences into a set of mappings that associate each construct in the conceptual model with a query on the physical model. Moreover, the homogeneity and summarizability of data in conceptual models is the key to accurate query answering, a necessity in decision making environments. A data-driven approach to refactor relational models into summarizable schemas and instances is proposed as the solution of this issue. We outline the algorithms and challenges in bridging multidimensional conceptual models and the physical model of data warehouses and discuss experimental results.

No description available.

This research aims at revising the Victim Scale of the School Bullying Scales to examine bullying behaviors with high frequency and high severity. Participants were 1611 secondary school students in Kaohsiung. The Rasch Rating Scale Model was employed to analyze the collected data. Results showed that the revised Victim Scale fit well, exhibiting good evidence of the construct validity. Male students considered that victimized behaviors with high frequency and high severity were ¡§friendship being breached,¡¨ ¡§belongings being taken without permission,¡¨ ¡§goods being breached,¡¨ ¡§being tattled,¡¨ ¡§being hit or kicked,¡¨ and ¡§being neglected intentionally. Female students revealed different patterns of bullying behavior, while female students regard ¡§friendship being breached,¡¨ ¡§being crowded out of a group,¡¨ ¡§being isolation,¡¨ ¡§belongings being taken without permission,¡¨ ¡§being tattled,¡¨ ¡§being criticized online,¡¨ ¡§being neglected intentionally¡¨ and ¡§others kept silent to me on purpose¡¨ as victimized behaviors with high frequency and high severity. Finally, the implications for school bullying intervention and prevention were discussed. Keywords: school bullying, Rasch measurement, multidimensional Rasch analysis, DIF

No description available.

Thesis (Ph. D.)--University of Washington, 2003. / Vita. Includes bibliographical references (leaves 145-155).

In this dissertation we consider several limiting criteria forn-dimensional diffusion processes defined as solutions of stochasticdifferential equations. Our main interest is in criteria for polynomialand exponential rates of convergence to the steady state distributionin the total variation norm. Resulting criteria should place assumptionsonly on the coefficients of the elliptic differentialoperator governing the diffusion.Coupling of Harris chains is one of the main methods employed in thisdissertation.