Imagery : correlação entre o motor imagery e as habilidades técnicas de condução de bola no futebol

Dissertação apresentada à Escola Superior de Educação do Instituto Politécnico de Castelo Branco para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Atividade Física.

Etapas de cambio comportamental frente al consumo de drogas ilegales, tabaco y alcohol en escolares de 9 a 17 años de Bogotá – Colombia

Objetivo: El propósito del estudio fue describir estadísticamente las etapas de cambio comportamental frente al consumo de sustancias psicoactivas –SPA– (alcohol, tabaco y drogas ilegales) en escolares entre 9 y 17 años de Bogotá- Colombia, pertenecientes al estudio FUPRECOL. Método: Se trata de un estudio descriptivo y transversal en 6.965 niños y adolescentes entre 9 y 17 años, pertenecientes a 24 instituciones educativas oficiales de Bogotá - Colombia. La medición de los procesos de cambio propuestos por el Modelo Transteórico (MTT), aplicados al consumo de drogas, tabaco y alcohol se aplicaron de manera auto-diligenciada mediante un cuestionario estructurado. Resultados: De la muestra evaluada, el 58,4% fueron mujeres con un promedio de edad 12,74 ± 2.38 años. En la población en general, frente al consumo de drogas, el 6% de los escolares se encontraban en etapa de pre-contemplación, 44 % en contemplación; 30% en preparación/acción, 20% en mantenimiento. Con relación al consumo de alcohol, el 5% de los niños y adolescentes se encontraban en etapa de pre-contemplación, 36 % en contemplación; 12% en preparación/acción, 46% en mantenimiento. Frente al tabaco, el 4% de los niños y adolescentes se encontraban en etapa de pre-contemplación, 33 % en contemplación; 12% en preparación/acción, 51% en mantenimiento. Conclusiones: En los escolares evaluados, un importante porcentaje se ubica en la etapa de mantenimiento frente a la intención de consumo de tabaco y alcohol. Frente al consumo de drogas ilegales los niños y adolescentes están en la etapa de contemplación. Se requieren esfuerzos mayores para fomentar programas preventivos que enseñen sobre el riesgo del abuso/dependencia de este tipo de sustancias psicoactiva sobre la salud; dándole prioridad en las agendas y políticas públicas dentro del ámbito escolar.

Knockin' on Digital Doors. Dealing with online [dis]credit in an era of digital scientific inquiry

This chapter aims at presenting and discussing credible online recruitment eliciting techniques targeting scientific purposes adjusted to the digital age. Based on several illustrations conducted by the author within the framework of both quantitative and qualitative inquiries, this chapter critically explores the digital ethos in three main challenges faced when dealing with online recruitment for scientific purposes: entering the normality of the everyday life, entering the idiosyncrasy of multicultural lives, and entering the chaos of busy lives. By the end, a toolbox for establishing and evaluating (dis)credibility within online recruitment strategies is presented. Moreover, it is argued that success of data collection at the present time in online environments seems to rely as ever on internal factors of the communication process vis-à-vis e-mail content, design and related strategies.

Assessing the fit of unidimensional IRT models for binary data under model misspecification

Model misspecification affects the classical test statistics used to assess the fit of the Item Response Theory (IRT) models. Robust tests have been derived under model misspecification, as the Generalized Lagrange Multiplier and Hausman tests, but their use has not been largely explored in the IRT framework. In the first part of the thesis, we introduce the Generalized Lagrange Multiplier test to detect differential item response functioning in IRT models for binary data under model misspecification. By means of a simulation study and a real data analysis, we compare its performance with the classical Lagrange Multiplier test, computed using the Hessian and the cross-product matrix, and the Generalized Jackknife Score test. The power of these tests is computed empirically and asymptotically. The misspecifications considered are local dependence among items and non-normal distribution of the latent variable. The results highlight that, under mild model misspecification, all tests have good performance while, under strong model misspecification, the performance of the tests deteriorates. None of the tests considered show an overall superior performance than the others. In the second part of the thesis, we extend the Generalized Hausman test to detect non-normality of the latent variable distribution. To build the test, we consider a seminonparametric-IRT model, that assumes a more flexible latent variable distribution. By means of a simulation study and two real applications, we compare the performance of the Generalized Hausman test with the M2 limited information goodness-of-fit test and the Likelihood-Ratio test. Additionally, the information criteria are computed. The Generalized Hausman test has a better performance than the Likelihood-Ratio test in terms of Type I error rates and the M2 test in terms of power. The performance of the Generalized Hausman test and the information criteria deteriorates when the sample size is small and with a few items.

GENEOs, compactifications, and graphs

Our objective in this thesis is to study the pseudo-metric and topological structure of the space of group equivariant non-expansive operators (GENEOs). We introduce the notions of compactification of a perception pair, collectionwise surjectivity, and compactification of a space of GENEOs. We obtain some compactification results for perception pairs and the space of GENEOs. We show that when the data spaces are totally bounded and endow the common domains with metric structures, the perception pairs and every collectionwise surjective space of GENEOs can be embedded isometrically into the compact ones through compatible embeddings. An important part of the study of topology of the space of GENEOs is to populate it in a rich manner. We introduce the notion of a generalized permutant and show that this concept too, like that of a permutant, is useful in defining new GENEOs. We define the analogues of some of the aforementioned concepts in a graph theoretic setting, enabling us to use the power of the theory of GENEOs for the study of graphs in an efficient way. We define the notions of a graph perception pair, graph permutant, and a graph GENEO. We develop two models for the theory of graph GENEOs. The first model addresses the case of graphs having weights assigned to their vertices, while the second one addresses weighted on the edges. We prove some new results in the proposed theory of graph GENEOs and exhibit the power of our models by describing their applications to the structural study of simple graphs. We introduce the concept of a graph permutant and show that this concept can be used to define new graph GENEOs between distinct graph perception pairs, thereby enabling us to populate the space of graph GENEOs in a rich manner and shed more light on its structure.

Joint value at risk: a new conditional risk measure

In this PhD thesis a new firm level conditional risk measure is developed. It is named Joint Value at Risk (JVaR) and is defined as a quantile of a conditional distribution of interest, where the conditioning event is a latent upper tail event. It addresses the problem of how risk changes under extreme volatility scenarios. The properties of JVaR are studied based on a stochastic volatility representation of the underlying process. We prove that JVaR is leverage consistent, i.e. it is an increasing function of the dependence parameter in the stochastic representation. A feasible class of nonparametric M-estimators is introduced by exploiting the elicitability of quantiles and the stochastic ordering theory. Consistency and asymptotic normality of the two stage M-estimator are derived, and a simulation study is reported to illustrate its finite-sample properties. Parametric estimation methods are also discussed. The relation with the VaR is exploited to introduce a volatility contribution measure, and a tail risk measure is also proposed. The analysis of the dynamic JVaR is presented based on asymmetric stochastic volatility models. Empirical results with S&P500 data show that accounting for extreme volatility levels is relevant to better characterize the evolution of risk. The work is complemented by a review of the literature, where we provide an overview on quantile risk measures, elicitable functionals and several stochastic orderings.