Centro Interdisciplinar de Ciências Sociais - Açores
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O CICS.NOVA.UAc é uma unidade descentralizada do CICS.NOVA, vocacionada para o desenvolvimento da investigação científica, formação, extensão cultural e prestação de serviços à comunidade, de forma interdisciplinar e com destaque para as seguintes áreas científicas: a) Sociologia; b) Demografia; c) Psicologia; d) Ciências da Educação.
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Browsing Centro Interdisciplinar de Ciências Sociais - Açores by Author "Bacelar-Nicolau, Helena"
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- Análise dos perfis de consumo de cannabis pelos adolescentes de Ponta DelgadaPublication . Sousa, Áurea; Pereira, Hélder Rocha; Raposo, Sara; Silva, Osvaldo; Bacelar-Nicolau, HelenaA cannabis é a droga ilícita mais produzida e consumida na Europa e, embora seja uma “droga leve”, é reconhecido o seu impacto nas alterações de memória, nas sensações e nos comportamentos. Apresentam-se as principais conclusões obtidas, com base num questionário e em métodos de Análise de Dados (do univariado ao multivariado), com o objetivo de estimar a prevalência e determinar os perfis de consumo de cannabis por parte dos estudantes do Ensino Secundário do concelho de Ponta Delgada (Açores).
- Classes de objectos simbólicos: dados da indústria automóvelPublication . Sousa, Áurea; Bacelar-Nicolau, Helena; Nicolau, Fernando C.; Silva, OsvaldoNeste trabalho, é abordada a Análise Classificatória Hierárquica Ascendente (ACHA) de dados simbólicos ou complexos (generalizações de dados clássicos), com base no coeficiente de afinidade generalizado ponderado e em critérios de agregação clássicos e probabilísticos, estes últimos no âmbito da metodologia VL. São apresentados os principais resultados obtidos com a ACHA de 33 modelos de carros (dados simbólicos na área da indústria automóvel), com base no coeficiente de afinidade generalizado ponderado, centrado e reduzido pelo método de Wald e Wolfowitz, comparando-se os resultados obtidos com os de outros autores e com a partição definida a priori pelas categorias ("Utilitário", "Berlina", "Desportivo", "Luxo") a que os modelos de carros pertencem.
- Clustering an interval data set : are the main partitions similar to a priori partition?Publication . Sousa, Áurea; Bacelar-Nicolau, Helena; Nicolau, Fernando C.; Silva, OsvaldoIn this paper we compare the best partitions of data units (cities) obtained from different algorithms of Ascendant Hierarchical Cluster Analysis (AHCA) of a well-known data set of the literature on symbolic data analysis (“city temperature interval data set”) with a priori partition of cities given by a panel of human observers. The AHCA was based on the weighted generalised affinity with equal weights, and on the probabilistic coefficient associated with the asymptotic standardized weighted generalized affinity coefficient by the method of Wald and Wolfowitz. These similarity coefficients between elements were combined with three aggregation criteria, one classical, Single Linkage (SL), and the other ones probabilistic, AV1 and AVB, the last ones in the scope of the VL methodology. The evaluation of the partitions in order to find the partitioning that best fits the underlying data was carried out using some validation measures based on the similarity matrices. In general, global satisfactory results have been obtained using our methods, being the best partitions quite close (or even coinciding) with the a priori partition provided by the panel of human observers.
- Entrepreneurship Education : The role of the Higher Education Institutions in the Entrepreneurial Attitudes of the StudentsPublication . Sousa, Áurea; Couto, Gualter; Branco, Nélia Cavaco; Silva, Osvaldo; Bacelar-Nicolau, HelenaThe entrepreneurship education has been taking an increasing importance in the educational programs, emphasizing the role of the higher education institutions as promoters of the entrepreneurship. In this work, which is based on the responses obtained through a survey conducted with a sample consisting of 305 students of the Azores University, enrolled in courses of different scientific areas, we aim to assess how, in the student’s perspective, the university can stimulate the interest of their students in creating business. From hierarchical clustering methods we obtain a typology of variables linked to initiatives and activities that could be developed by the university. The main results obtained from some non-parametric hypothesis tests and from correspondence analysis, simple and multiple, are also presented.
- Hierarchical cluster analysis of groups of individuals : application to business dataPublication . Sousa, Áurea; Bacelar-Nicolau, Helena; Silva, OsvaldoWe present one example, in which the data are issued from a questionnaire in order to find satisfaction typologies (with the services provided by an automobile company) of independent groups of individuals. The Agglomerative Hierarchical Cluster Analysis (AHCA) was based on two approaches: one based on a particular case of the generalized weighted affinity coefficient, which deals with classical data, and the other one on the weighted generalized affinity coefficient for the case of symbolic/complex data. Both measures of comparison between elements were combined with classical and probabilistic aggregation criteria. We used the global statistics of levels (STAT) to evaluate the quality of the obtained partitions.
- On clustering interval data with different scales of measures : experimental resultsPublication . Sousa, Áurea; Bacelar-Nicolau, Helena; Nicolau, Fernando C.; Silva, OsvaldoSymbolic Data Analysis can be defined as the extension of standard data analysis to more complex data tables. We illustrate the application of the Ascendant Hierarchical Cluster Analysis (AHCA) to a symbolic data set (with a known structure) in the field of the automobile industry (car data set), in which objects are described by variables whose values are intervals of the real data set (interval variables). The AHCA of thirty-three car models, described by eight interval variables (with different scales of measure), was based on the standardized weighted generalized affinity coefficient, by the method of Wald and Wolfowitz. We applied three probabilistic aggregation criteria in the scope of the VL methodology (V for Validity, L for Linkage). Moreover, we compare the achieved results with those obtained by other authors, and with a priori partition into four clusters defined by the category (Utilitarian, Berlina, Sporting and Luxury) to which the car belong. We used the global statistics of levels (STAT) to evaluate the obtained partitions.
- Probabilistic approach for comparing partitionsPublication . Silva, Osvaldo; Bacelar-Nicolau, Helena; Nicolau, Fernando C.; Sousa, ÁureaThe comparison of two partitions in Cluster Analysis can be performed using various classical coefficients (or indexes) in the context of three approaches (based, respectively, on the count of pairs, on the pairing of the classes and on the variation of information). However, different indexes usually highlight different peculiarities of the partitions to compare. Moreover, these coefficients may have different variation ranges or they do not vary in the predicted interval, but rather only in one of their subintervals. Furthermore, there is a great diversity of validation techniques capable of assisting in the choice of the best partitioning of the elements to be classified, but in general each one tends to favour a certain kind of algorithm. Thus, it is useful to find ways to compare the results obtained using different approaches. In order to assist this assessment, a probabilistic approach to comparing partitions is presented and exemplified. This approach, based on the VL (Validity Linkage) Similarity, has the advantage, among others, of standardizing the measurement scales in a unique probabilistic scale. In this work, the partitions obtained from the agglomerative hierarchical cluster analysis of a dataset in the field of teaching are evaluated using classical and probabilistic (of VL type) indexes, and the obtained results are compared.
- Probabilistic approach for comparing partitionsPublication . Silva, Osvaldo; Bacelar-Nicolau, Helena; Nicolau, Fernando C.; Sousa, ÁureaThe comparison of two partitions in Cluster Analysis can be performed using various classical coefficients (or indexes) in the context of three approaches (based, respectively, on the count of pairs, on the pairing of the classes and on the variation of information). However, different indexes usually highlight different peculiarities of the partitions to compare. Moreover, these coefficients may have different variation ranges or they do not vary in the predicted interval, but rather only in one of their subintervals. Furthermore, there is a great diversity of validation techniques capable of assisting in the choice of the best partitioning of the elements to be classified, but in general each one tends to favour a certain kind of algorithm. Thus, it is useful to find ways to compare the results obtained using different approaches. In order to assist this assessment, a probabilistic approach to comparing partitions is presented and exemplified. This approach, based on the VL (Validity Linkage) Similarity, has the advantage, among others, of standardizing the measurement scales in a unique probabilistic scale. In this work, the partitions obtained from the agglomerative hierarchical cluster analysis of a dataset in the field of teaching are evaluated using classical and probabilistic (of VL type) indexes, and the obtained results are compared.