The effectiveness of multidimensional scaling (MDS) techniques in recovering the underlying dimensionality of dichotomously-scored data was examined for unidimensional and multidimensional data. Thirty-three data sets of varying numbers of dimensions with differing patterns of item discrimination were generated using a multidimensional latent trait model in a Monte Carlo simulation study. Margin-sensitive measures (agreement, phi, and kappa) and margin-free measures (Φ/ Φ(max), Yule's Q, and the tetrachoric correlation) were used as measures of similarity and the resulting matrices were scaled in one through five dimensions. Values of the stress coefficient, S₁, S₁ by dimensionality plots, and plot configurations were examined to determine the dimensionality of the item set. Principal components analyses (PCAs) of phi and tetrachoric matrices were carried out as a basis for comparison. In addition, MDS and PCA were used to examine a data set comprised of items obtained from the routing tests of the Head Start Measures Battery. Two effects of item discrimination on MDS results were especially noteworthy. First, factors tended to be located equally distant from each other in the MDS space. Items were located closest to the factor for which the primary factor loading occurred. Second, as item discrimination decreased, items tended to be more widely dispersed from their appropriate locations in space. Extra dimensions in the MDS representational space were required for margin-sensitive coefficients to accommodate difficulty effects. Margin-free coefficients generally eliminated difficulty-related dimensions, although occasional problems were noted with the tetrachoric correlation. Analysis of the HSMB revealed that the data were primarily unidimensional, although specific effects due to each subtest were clearly present in the analysis. MDS was found to be a useful technique and its use in conjunction with PCA or factor analysis is recommended.

No description available.

Tese de mestrado. Estatística Aplicada e Modelação. 2005. Faculdade de Engenharia. Universidade do Porto

This thesis argues that a multidimensional profile incorporating mastery goals, performance goals, social goals and extrinsic goals, as well as factors relating to self-perceptions, would provide a better understanding of achievement motivation in adolescents than a univariate or dichotomous framework. Additionally this thesis also explores whether the use of lower-order dimensions provides information that offers a more detailed analysis of achievement goals over and above that found by the higher-order factors alone. A newly developed multidimensional measure, the SMOSA (Self Motivational Orientation Scale for Adolescents) of achievement motivation was used to examine changes of different motivational pursuits and perceptions of self across grades and sex in an adolescent population. The information found provides a more detailed analysis than previous research, which relied on an evaluation of means to explain differences between samples. Therefore, educators will be provided with a comprehensive understanding of the patterns of change in achievement motivation during adolescence and such knowledge may equip them with a way of measuring students’ approaches to facilitative learning and the ability to explore students’ paths for optimal engagement. / Doctor of Philosophy (PhD)

This thesis argues that a multidimensional profile incorporating mastery goals, performance goals, social goals and extrinsic goals, as well as factors relating to self-perceptions, would provide a better understanding of achievement motivation in adolescents than a univariate or dichotomous framework. Additionally this thesis also explores whether the use of lower-order dimensions provides information that offers a more detailed analysis of achievement goals over and above that found by the higher-order factors alone. A newly developed multidimensional measure, the SMOSA (Self Motivational Orientation Scale for Adolescents) of achievement motivation was used to examine changes of different motivational pursuits and perceptions of self across grades and sex in an adolescent population. The information found provides a more detailed analysis than previous research, which relied on an evaluation of means to explain differences between samples. Therefore, educators will be provided with a comprehensive understanding of the patterns of change in achievement motivation during adolescence and such knowledge may equip them with a way of measuring students’ approaches to facilitative learning and the ability to explore students’ paths for optimal engagement. / Doctor of Philosophy (PhD)

Logistic and economic constraints often dictate the spatial sampling of a seismic survey. The process of acquisition records a finite number of spatial samples of the continuous wave field. The latter leads to a regular or irregular distribution of seismograms. Seismic reconstruction methods are used to recover non-acquired data and to synthesize a dense distribution of sources and receivers that mimics a properly sampled survey. This dissertation examines the seismic sampling problem and proposes algorithms for efficient multidimensional seismic data reconstruction. In particular, I address the problem of reconstructing irregularly sampled data using multidimensional linear prediction filters. The methodology entails a strategy that consists of two steps. First, the unaliased part of the wave field is reconstructed via Fourier reconstruction (Minimum Weighted Norm Interpolation). Then, prediction filters for all the frequencies are extracted from the reconstructed low frequencies. The latter permits the the recovery of aliased data with Multi-Step Auto-Regressive (MSAR) algorithm. The recovered prediction filters are used to reconstruct the complete data in either the f-x domain (MSAR-X) or the f-k domain (MSAR-K). The thesis also presents the use of Exponentially Weighted Recursive Least Squares (EWRLS) to estimate adaptive prediction filters for f-x seismic interpolation. Adaptive prediction filters are able to model signals where the dominant wavenumbers are varying in space. This concept leads to a f-x interpolation method that does not require windowing strategies for optimal results. In other words, adaptive prediction filters can be used to interpolate waveforms with spatially variant dips. / Geophysics

No description available.

Thesis (Ph. D.)--University of Toronto, 2004. / Adviser: Shizuhiko Nishisato.

Thesis (Ph. D.)--Brigham Young University. Dept. of Instructional Psychology and Technology, 2004. / Includes bibliographical references (p. 103-106).

Tese (doutorado)—Universidade de Brasília, Departamento de Economia, Brasília, 2018. / Submitted by Raquel Viana (raquelviana@bce.unb.br) on 2018-09-04T17:49:43Z No. of bitstreams: 1 2018_HenriqueRogêBatista.pdf: 2274217 bytes, checksum: 1c97359d8bee4084df55a695aa353504 (MD5) / Approved for entry into archive by Raquel Viana (raquelviana@bce.unb.br) on 2018-09-17T16:40:58Z (GMT) No. of bitstreams: 1 2018_HenriqueRogêBatista.pdf: 2274217 bytes, checksum: 1c97359d8bee4084df55a695aa353504 (MD5) / Made available in DSpace on 2018-09-17T16:40:58Z (GMT). No. of bitstreams: 1 2018_HenriqueRogêBatista.pdf: 2274217 bytes, checksum: 1c97359d8bee4084df55a695aa353504 (MD5) Previous issue date: 2018-09-04 / Abordar a desigualdade do ponto de vista multidimensional vem ganhando espaço na literatura econômica nos últimos anos, com os estudos que associam a qualidade de vida dos indivíduos a uma cesta de bens e serviços e não apenas à renda ou ao consumo. A tese busca contribuir, do ponto de vista metodológico e empírico, a análise multidimensional da desigualdade. Esta tese encontra-se estruturada em três capítulos, além da introdução, que sublinha a importância da análise multidimensional da desigualdade, e estabelece os atributos e dimensões escolhidos para medi-la. O Capítulo 1 trata da metodologia normativa da desigualdade multidimensional, onde a seleção das variáveis, a definição dos parâmetros e as técnicas de agregação são analisadas. A tese parte da construção de uma matriz de distribuição das dimensões selecionadas na Pesquisa Nacional por Amostra de Domicílios - PNAD, do Instituto Brasileiro de Geografia e Estatística - IBGE, se restringindo aos anos de 2001, 2005, 2009, 2013 e 2015. A seleção das dimensões está de acordo com a disponibilidade de dados e contempla as esferas econômica, do trabalho, da educação, do saneamento básico e do interesse comum. Os primeiros resultados são apresentados no Capítulo 2, onde a desigualdade é analisada do ponto de vista desagregado. Ou seja, analisa-se a evolução, ao longo dos anos da amostra, de cada uma das dimensões ou atributos selecionados para analisar a desigualdade na qualidade de vida dos indivíduos. No Capítulo 3, a desigualdade é analisada do ponto de vista agregado, ou seja, pela criação de um índice que engloba as dimensões e atributos selecionados, permitindo tirar conclusões sobre a dinâmica da desigualdade do bem-estar ou da qualidade de vida no tempo. / Addressing inequality from the multidimensional point of view has been gaining ground in the economic literature in recent years, with studies that associate individuals' quality of life with a basket of goods and services, not just income or consumption. The thesis seeks to contribute, from the methodological and empirical point of view, the multidimensional analysis of inequality. This thesis is structured in three chapters, in addition to the introduction, which underlines the importance of multidimensional analysis of inequality, and establishes the attributes and dimensions chosen to measure it. Chapter 1 deals with the normative methodology of multidimensional inequality, where the selection of variables, parameter definition and aggregation techniques are analyzed. The thesis starts from the construction of a distribution matrix of the selected dimensions in the National Survey by Household Sample - PNAD, from the Brazilian Institute of Geography and Statistics - IBGE, restricting itself to the years of 2001, 2005, 2009, 2013 and 2015. The selection of dimensions is in accordance with the availability of data and it covers the economic, labor, education, basic sanitation and common interest spheres. The first results are presented in Chapter 2, where inequality is analyzed from a disaggregated point of view. In other words, the evolution of the sample is analyzed in each of the dimensions or attributes selected to evaluate the inequality on the quality of life of individuals. In Chapter 3, inequality is analyzed from the aggregate point of view, by the creation of an index that encompasses the selected dimensions and attributes, allowing conclusions to be drawn from the dynamics of well-being inequality or quality of life over time.