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- A phenomenological study of Irish and Portuguese women’s experiences of receiving family support when studying STEM subjects at technical institutesPublication . Chance, S. M.; Williams, B.This paper reports a research study of women’s experiences of receiving family support when studying science, technology, engineering, and mathematics (STEM) subjects at technical institutes in Ireland and Portugal. Specifically, it reports phenomenological analysis of 19 interviews conducted during the 2014-2015 academic years with female students studying engineering subjects at technical institutes in Ireland and Portugal. It identifies forms of positive support received from family as well as problematic family dynamics and concerns. Parents, uncles, and aunts provide many positive forces, as do surrogates (i.e., adopted family and close mentors). Cousins and brothers also provide role models and information. For our participants, meeting family obligations and being first-generation college students presents some challenges and stress.
- Estudo comparativo sobre seleção de variáveis em classificação supervisionadaPublication . Ferreira, Ana Sousa; Marques, Anabela
- Features selection in Discrete Discriminant AnalysisPublication . Marques, Anabela; Ferreira, Ana Sousa; Cardoso, MargaridaIn discrete discriminant analysis dimensionality problems occur, particularly when dealing with data from the social sciences, humanities and health. In these domains, one often has to classify entities with a high number of explanatory variables when compared to the number of observations available. In the present work we address the problem of features selection in classification, aiming to identify the variables that most discriminate between the a priori defined classes, reducing the number of parameters to estimate, turning the results easier to interpret and reducing the runtime of the methods used. We specially address classification using a recently methodological approach based on a linear combination of the First-order Independence Model (FOIM) and the Dependence Trees Model (DTM). Data of small and moderate size are considered.
- Combining models in discrete discriminant analysisPublication . Marques, Anabela; Ferreira, Ana Sousa; Cardoso, MargaridaDiverse Discrete Discriminant Analysis (DDA) models perform differently on different sample observations (Brito et al. (2006)). This fact has encouraged research in combined models for DDA. This research seems to be specially promising when the a priori classes are not well separated or when small or moderate sized samples are considered, which often occurs in practice. In this work we evaluate the performance of a linear combination of two DDA models (Marques et al. (2008)): the First-Order Independence Model (FOIM) and the Dependence Trees Model (DTM) (Celeux and Nakache (1994). The pro- posed methodology also uses a Hierarchical Coupling Model (HIERM) when addressing multiclass classification problems, decomposing the multiclass problems into several bi-class problems, using a binary tree structure (Sousa Ferreira (2000)). The analysis is based both on simulated and real datasets. Results include measures of precision regarding a training set, a test set and cross-validation. The R software is used for the algorithm's implementation.
- Discrete discriminant analysis: The performance of combining modelsPublication . Marques, Anabela; Ferreira, Ana Sousa; Cardoso, MargaridaThe idea of combinig models in Discrete Discriminant Analysis (DDA) is present in a growing number of papers which aim to obtain more robust and more stable models than any of the competing ones. This seems to be a promising approach since it is known that different DDA models perform differently on different subjects [1]. This is a particularly relevant issue when the groups are not well separeted, which often occurs in practice. recently, a new methodological approach was proposed based on a linear combination of the First-order Independence Model (FOIM) and the Dependence trees Model (DTM) ([3] and [2]). In the present work we further explore the referred approach. Since FOIM assumes that the P discrete predictive variables are independent in each group and DTM takes the predictors relationships into account, we think that the proposed approach could be sucessfully applied to many real situations. In order to evaluate its performance, we consider both real and simulated data. Furthermore we present comparisons with alternative models performance. According to the training sample size the leave-one-out approach, v-fold cross validation or assessing the error rate in a test sample are considered. The MATLAB software is used for the algorithms' implementation.
- Combining models in discrete discriminant analysis in the multiclass casePublication . Marques, Anabela; Ferreira, Ana Sousa; Cardoso, MargaridaThe idea of combining models in Discrete Discriminant Analysis (DDA) is present in a growing number of papers which aim to obtain more robust and more stable models than any of the competing ones. This seems to be a promising approach since it is known that different DDA models perform differently on different subjects (Brito et al.(2006)). In particular, this will be a more relevant issue if the groups are not well separated, which often occurs in practice. In the present work a new methodological approach is suggested which is based on DDA models' combination. The multiclass problem is decomposed into several dichotomous problems that are nested in a hierarchical binary tree (Sousa Ferreira (2000), Brito et al. (2006)) and at each level of the binary tree a new combining model is proposed to derive the decision rule. This combining model is based on two well known models in the literature - the First-order Independence Model (FOIM) and the Dependence Trees Model (DTM) (Celeux and Nakache (1994)). The MATLAB software is used for the algorithms' implementation and the proposed approach is illustrated in a DDA application.
- Classificação hierárquica de consensoPublication . Marques, AnabelaNeste trabalho apresenta-se a técnica Classificação segundo uma direcção, introduzida por Vichi, em 1995. Esta técnica tem por base a teoria da Análise Classificatória e dos Métodos de Consenso. Aplicando a Classificação segundo uma direcção a um conjunto de dados tridimensionais obtém-se uma classificação hierárquica única.
- Métodos de consenso em análise de dadosPublication . Marques, AnabelaNeste trabalho apresenta-se alguns métodos de consenso que permitem ao investigador encontrar uma classificação hierárquica única para um conjunto de r classificações hierárquicas, ou r dendrogramas, ou r árvores.
- Resultados de uma Escala de Sugestionabilidade: Classificação em grupos demográficosPublication . Marques, Anabela; Ferreira, Ana Sousa; Cardoso, Margarida; Pires, RuteA natureza imperfeita dos processos de recuperação da memória, nomeadamente o esquecimento e as distorções, tem importantes implicações na psicologia clínica e forense. A GSS1-Escala de Sugestionabilidade de Gudjonsson foi desenvolvida para avaliar a tendência que algumas pessoas têm para ceder perante questões falaciosas quando entrevistadas. Neste trabalho comparam-se os resultados de diversas técnicas de análise discriminante, no sentido de estudar a associação entre a sugestionabilidade e algumas características demográficas dos inquiridos.
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