Relationships between the perceived quality of life and the personality styles measured with the The Millon Index of Personality Styles Revised (MIPS-R)

This exploratory study aims to determine whether the personality styles measured with the Portuguese adaptation of Millon Index of Personality Styles Revised, MIPS-R affect the perceived quality of life. The MIPS-R is a theory-based inventory that measures 24 personality styles in normally functioning adults. Life satisfaction was measured with the Portuguese version of the Quality of Life Inventory, QOLI (Fagulha, Duarte & Miranda, 2000). It refers to a person’s subjective evaluation of the degree to which his/her most important needs, goals and wishes have been fulfilled. This study was carried out with a sample of 43 college students, 36 females (age mean = 19,7; SD = 3,1) and 7 males (age mean = 27,4; SD = 11,4). Based on the participants’ overall life satisfaction score three groups were defined: (1) Low/Very Low quality of life, (2) Average quality of life, (3) High quality of life. Discriminant Factor Analysis (DFA) and the Kruskal-Wallis Test were used to identify the styles that most differentiate these groups and to compare each style in the groups. The Other-Nurturing style is the one that best differentiates the groups. DFA results will be further exploited. Considering the Kruskal-Wallis Test, differences are observed in the Pleasure-Enhancing (p=.006), the Actively Modifying (p=.002), the Gregarious/Outgoing (p=.012), the Passively Accommodating (p=.027), the Asocial/Withdrawing (p=.036), the Unconventional/Dissenting (p=.041) and in the Dissatisfied/Complaining (p=.019) styles. Multiple comparisons were used to compare these styles in the groups. The authors believe that the discussion of these results will provide a better understanding of the MIPS-R.Instituto de Psicologia das Relações Humana

Desenvolvimento de um modelo estatístico de prognóstico no âmbito da Sepsis, utilizando o conceito PIRO

Relatório de estágio curricular de mestrado em Estatística de SistemasA Sepsis é uma infecção geral grave, caracterizada por uma resposta in amatória sistémica (designada por Síndrome de Resposta In amatória Sistémica), geralmente causada pela presença de um agente infeccioso na corrente sanguínea. A Sepsis tem sido identi cada em diversos estudos epidemiológicos como sendo a principal causa de morte nos doentes críticos, internados nas Unidades de Cuidados Intensivos. A Sepsis pode sistematizar-se através do conceito PIRO (Predisposição, Infecção, Resposta e disfunção do Órgão). Existem factores susceptíveis de serem importantes em cada um dos componentes PIRO. Neste trabalho foram estudados os factores P de Predisposição, tais como o sexo, idade, doenças crónicas, comorbilidades, e os factores R de Resposta, tais como a temperatura, frequência cardíaca, respiratória, número de leucócitos, neutró los, PCR. O objectivo é encontrar os factores que mais contribuem para a mortalidade hospitalar. Relativamente aos factores de Predisposição foi efectuada a regressão logística com os métodos stepwise e o método de selecção de variáveis LASSO. Este último método é particularmente importante quando estamos perante um grande número de variáveis explicativas, pois tem a vantagem de reduzir algumas das variáveis a zero, dependendo do valor do parâmetro que faz essa redução. O problema da selecção de variáveis permite decidir quais as variáveis a incluir no modelo de modo a obter um bom tradeo entre viés e variância. Isto leva-nos a um conjunto de variáveis mais parcimonioso e que esteja associado com a mortalidade hospitalar. Quanto às variáveis R, após análise estatística univariada e bivariada podemos chegar à conclusão de quais as variáveis que são signi cativamente diferentes entre os indivíduos que faleceram e que tiveram alta e de como variaram durante os dias de internamento na Unidade de Cuidados Intensivos (UCI). Dado que estamos perante dados longitudinais, esta primeira análise permitir-nos-á decidir qual a melhor metodologia a implementar. Conhecendo bem o comportamento das variáveis estamos em condições de, mais tarde desenvolver um modelo que nos permita construir um score PIRO.Sepsis a serious general infection, characterized by a systemic in ammatory response (referred to as Systemic In ammatory Response Syndrome), usually caused by the presence of an infectious agent in the bloodstream. The Sepsis has been identi ed in several epidemiological studies as being the leading cause of death in critically ill patients hospitalized in intensive care units. Sepsis can be systematized through the PIRO concept (predisposition, infection, response and organ dysfunction). There are factors likely to be important in each of the components PIRO. In this work we studied the factors P of predisposition, such as gender, age, chronic illness, comorbidities, and R factors of response, such as temperature, heart rate, respiratory rate, leukocyte count, CRP. The aim is to nd the factors that most contribute to hospital mortality. As regards predisposing factors, logistic regression was performed with stepwise methods and variable selection method LASSO. The latter method is particularly important when we are dealing with a large number of explanatory variables; it has the advantage of shrink some of the variables to zero, depending on the value of the parameter that causes this shrinkage. The problem of variable selection allows you to decide which variables to include in the model to obtain a good tradeo between bias and variance. This leads us to a more parsimonious set of variables and is association with hospital mortality. As for the R variables, after univariate and bivariate statistical analysis we reach the conclusion which variables are signi cantly di erent between the patients who died and who went discharged and how was the variation of variables along the days of stay in Intensive Care Unit (ICU). Since we are dealing with longitudinal data, this initial analysis will allow us to decide on the best methodology to implement. Knowing well the behavior of the variables we are able to later develop a model that allows us to build a PIRO score

Enhancing the selection of a model-based clustering with external qualitative variables

In cluster analysis, it can be useful to interpret the partition built from the data in the light of external categorical variables which were not directly involved to cluster the data. An approach is proposed in the model-based clustering context to select a model and a number of clusters which both fit the data well and take advantage of the potential illustrative ability of the external variables. This approach makes use of the integrated joint likelihood of the data and the partitions at hand, namely the model-based partition and the partitions associated to the external variables. It is noteworthy that each mixture model is fitted by the maximum likelihood methodology to the data, excluding the external variables which are used to select a relevant mixture model only. Numerical experiments illustrate the promising behaviour of the derived criterion

Combining models in supervised classification: New developments

Resumo da comunicação oral apresentada em XVIII Jornadas de Classificação e Análise de Dados (JOCLAD2011), Vila Real, de 7 a 9 de Abril de 2011In Discrete Discriminant Analysis dimensionality problems often occur. In this context, we propose a combining models approach, taking profit from several potential models. In the bi-class case, a single combination coefficient is considered and estimated using several strategies. In the multi-class case, the decomposition into several bi-class problems embedded in a binary tree is implemented. New developments of this approach are presented and their performances assessed on real or simulated data

O ciclo 3-8 anos: a primeira inclusão na educação básica

A escolaridade obrigatória começou por focar a alfabetização associada ao ler, escrever e contar. Com o desenvolvimento das sociedades preconiza-se a educação de todos com a satisfação das suas necessidades básicas de aprendizagem, exigindo uma educação pessoal e social desde mais cedo e durante mais tempo. Ao conceito tradicional de ensino primário e educação pré-escolar sucede o conceito de educação básica dos 3/4 aos 11/12 anos (ou um pouco menos) e dentro desta surge como inovação estratégica privilegiada o ciclo 3-8. Pretende-se contribuir para uma educação que promova o desenvolvimento sustentável, de todos e de cada um, a nível pessoal e social para a saúde e para o ambiente a partir da junção da educação pré-escolar com os primeiros anos da educação escolar considerando a idade dos três aos oito anos como o grande desafio para a concretização da primeira etapa de educação básica de qualidade para todos, em que se aprende a respeitar o ritmo individual de cada um

Combining models in discrete discriminant analysis in the multiclass case

Resumo de comunicação oral em póster apresentado em COMPSTAT2008 - 18th International Conference on Computational Statistics, Porto, Portugal, 24 a 29 de Agosto 2008The 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

Features selection in Discrete Discriminant Analysis

Resumo de comunicação em póster apresentada em 14th International Conference on Applied Stochastic Models and Data Analysis (ASMDA2011), Rome, June 7-10 2011In 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

Classification and combining models

Trabalho apresentado em SMTDA 2010: Stochastic Modeling Techniques and Data Analysis International Conference, Chania, Crete, Greece, 8-11 june 2010In the context of Discrete Discriminant Analysis (DDA) the idea of combining models is present in a growing number of papers aiming to obtain more robust and more stable models. This seems to be a promising approach since it is known that different DDA models perform differently on different subjects. Furthermore, the idea of combining models is particularly relevant when the groups are not well separeted, which often occurs in practice. Recently, we proposed a new DDA approach which is based on a linear combination of the First-order Independence Model (FOIM) and the Dependence Trees Model (DTM). In the present work we apply this new approach to classify consumers of a Portuguese cultural institution. We specifically focus on the performance of alternative models' combinations assessing the error rate and the Huberty index in a test sample. We use the R software for the algorithms' implementation and evaluation