Structural Analysis of Networks

Network analysis naturally relies on graph theory and, more particularly, on the use of node and edge metrics to identify the salient properties in graphs. When building visual maps of networks, these metrics are turned into useful visual cues or are used interactively to filter out parts of a graph while querying it, for instance. Over the years, analysts from different application domains have designed metrics to serve specific needs. Network science is an inherently cross-disciplinary field, which leads to the publication of metrics with similar goals; different names and descriptions of their analytics often mask the similarity between two metrics that originated in different fields. Here, we study a set of graph metrics and compare their relative values and behaviors in an effort to survey their potential contributions to the spatial analysis of networks.

Emotion and reason in everyday risk perception

Although research has documented the importance of emotion in risk perception, little is knownabout its prevalence in everyday life. Using the Experience Sampling Method, 94 part-timestudents were prompted at random via cellular telephones to report on mood state and threeemotions and to assess risk on thirty occasions during their working hours. The emotions valence, arousal, and dominance were measured using self-assessment manikins (Bradley &Lang, 1994). Hierarchical linear models (HLM) revealed that mood state and emotions explainedsignificant variance in risk perception. In addition, valence and arousal accounted for varianceover and above reason (measured by severity and possibility of risks). Six risks were reassessedin a post-experimental session and found to be lower than their real-time counterparts.The study demonstrates the feasibility and value of collecting representative samples of data withsimple technology. Evidence for the statistical consistency of the HLM estimates is provided inan Appendix.

Associationism and electoral participation: A multilevel study of 2000 Spanish general election

This work presents an application of the multilevel analysis techniques tothe study of the abstention in the 2000 Spanish general election. Theinterest of the study is both, substantive and methodological. From thesubstantive point of view the article intends to explain the causes ofabstention and analyze the impact of associationism on it. From themethodological point of view it is intended to analyze the interaction betweenindividual and context with a modelisation that takes into account thehierarchical structure of data. The multilevel study of this paper validatesthe one level results obtained in previous analysis of the abstention andshows that only a fraction of the differences in abstention are explained bythe individual characteristics of the electors. Another important fraction ofthese differences is due to the political and social characteristics of thecontext. Relating to associationism, the data suggest that individualparticipation in associations decrease the probability of abstention. However,better indicators are needed in order to catch more properly the effect ofassociationism in electoral behaviour.

Design effect and interviewer effects: Evidence from the Spanish ESS3-2006 Data

In 2007 the first Quality Enhancement Meeting on sampling in the European SocialSurvey (ESS) took place. The discussion focused on design effects and inteviewereffects in face-to-face interviews. Following the recomendations of this meeting theSpanish ESS team studied the impact of interviewers as a new element in the designeffect in the response s variance using the information of the correspondent SampleDesign Data Files. Hierarchical multilevel and cross-classified multilevel analysis areconducted in order to estimate the amount of responses variation due to PSU and tointerviewers for different questions in the survey. Factor such as the age of theinterviewer, gender, workload, training and experience and respondent characteristicssuch as age, gender, renuance to participate and their possible interactions are alsoincluded in the analysis of some specific questions like trust in politicians and trustin legal system . Some recomendations related to future sampling designs and thecontents of the briefing sessions are derived from this initial research.

The influence of drinking pattern, at individual and aggregate levels, on alcohol-related negative consequences.

AIM: To determine the extent drinking patterns (at the individual and country level) are associated with alcohol-related consequences over and above the total alcohol the person consumes. METHODS: Hierarchical linear models were estimated based on general population surveys conducted in 18 countries participating in the GENACIS project. RESULTS: In general, the positive association between drinking pattern scores and alcohol-related consequences was found at both the individual and country levels, independent of volume of drinking. In addition, a significant interaction effect indicated that the more detrimental the country's drinking pattern, the less steep the association between the volume of drinking and its consequences. CONCLUSION: Drinking patterns have an independent impact on consequences over and above the relationship between volume and consequences.

Subtypes of adolescents with substance use disorders and psychiatric comorbidity using cluster and discriminant analysis of MMPI-A profiles

The main aim of this study was to replicate and extend previous results on subtypes of adolescents with substance use disorders (SUD), according to their Minnesota Multiphasic Personality Inventory for adolescents (MMPI-A) profiles. Sixty patients with SUD and psychiatric comorbidity (41.7% male, mean age = 15.9 years old) completed the MMPI-A, the Teen Addiction Severity Index (T-ASI), the Child Behaviour Checklist (CBCL), and were interviewed in order to determine DSMIV diagnoses and level of substance use. Mean MMPI-A personality profile showed moderate peaks in Psychopathic Deviate, Depression and Hysteria scales. Hierarchical cluster analysis revealed four profiles (acting-out, 35% of the sample; disorganized-conflictive, 15%; normative-impulsive, 15%; and deceptive-concealed, 35%). External correlates were found between cluster 1, CBCL externalizing symptoms at a clinical level and conduct disorders, and between cluster 2 and mixed CBCL internalized/externalized symptoms at a clinical level. Discriminant analysis showed that Depression, Psychopathic Deviate and Psychasthenia MMPI-A scales correctly classified 90% of the patients into the clusters obtained.

Subtypes of adolescents with substance use disorders and psychiatric comorbidity using cluster and discriminant analysis of MMPI-A profiles

The main aim of this study was to replicate and extend previous results on subtypes of adolescents with substance use disorders (SUD), according to their Minnesota Multiphasic Personality Inventory for adolescents (MMPI-A) profiles. Sixty patients with SUD and psychiatric comorbidity (41.7% male, mean age = 15.9 years old) completed the MMPI-A, the Teen Addiction Severity Index (T-ASI), the Child Behaviour Checklist (CBCL), and were interviewed in order to determine DSMIV diagnoses and level of substance use. Mean MMPI-A personality profile showed moderate peaks in Psychopathic Deviate, Depression and Hysteria scales. Hierarchical cluster analysis revealed four profiles (acting-out, 35% of the sample; disorganized-conflictive, 15%; normative-impulsive, 15%; and deceptive-concealed, 35%). External correlates were found between cluster 1, CBCL externalizing symptoms at a clinical level and conduct disorders, and between cluster 2 and mixed CBCL internalized/externalized symptoms at a clinical level. Discriminant analysis showed that Depression, Psychopathic Deviate and Psychasthenia MMPI-A scales correctly classified 90% of the patients into the clusters obtained.

Correlation, by multivariate statistical analysis, between the scavenging capacity against reactive oxygen species and the bioactive compounds from frozen fruit pulps

The contents of total phenolic compounds (TPC), total flavonoids (TF), and ascorbic acid (AA) of 18 frozen fruit pulps and their scavenging capacities against peroxyl radical (ROO•), hydrogen peroxide (H2O2), and hydroxyl radical (•OH) were determined. Principal Component Analysis (PCA) showed that TPC (total phenolic compounds) and AA (ascorbic acid) presented positive correlation with the scavenging capacity against ROO•, and TF (total flavonoids) showed positive correlation with the scavenging capacity against •OH and ROO• However, the scavenging capacity against H2O2 presented low correlation with TF (total flavonoids), TPC (total phenolic compounds), and AA (ascorbic acid). The Hierarchical Cluster Analysis (HCA) allowed the classification of the fruit pulps into three groups: one group was formed by the açai pulp with high TF, total flavonoids, content (134.02 mg CE/100 g pulp) and the highest scavenging capacity against ROO•, •OH and H2O2; the second group was formed by the acerola pulp with high TPC, total phenolic compounds, (658.40 mg GAE/100 g pulp) and AA , ascorbic acid, (506.27 mg/100 g pulp) contents; and the third group was formed by pineapple, cacao, caja, cashew-apple, coconut, cupuaçu, guava, orange, lemon, mango, passion fruit, watermelon, pitanga, tamarind, tangerine, and umbu pulps, which could not be separated considering only the contents of bioactive compounds and the scavenging properties.

La educación secundaria y sus dos dimensiones, efectos del barrio y del colegio sobre los resultados saber 11.

Resumen: Este trabajo estudia los resultados en matemáticas y lenguaje de 32000 estudiantes en la prueba saber 11 del 2008, de la ciudad de Bogotá. Este análisis reconoce que los individuos se encuentran contenidos en barrios y colegios, pero no todos los individuos del mismo barrio asisten a la misma escuela y viceversa. Con el fin de modelar esta estructura de datos se utilizan varios modelos econométricos, incluyendo una regresión jerárquica multinivel de efectos cruzados. Nuestro objetivo central es identificar en qué medida y que condiciones del barrio y del colegio se correlacionan con los resultados educacionales de la población objetivo y cuáles características de los barrios y de los colegios están más asociadas al resultado en las pruebas. Usamos datos de la prueba saber 11, del censo de colegios c600, del censo poblacional del 2005 y de la policía metropolitana de Bogotá. Nuestras estimaciones muestran que tanto el barrio como el colegio están correlacionados con los resultados en las pruebas; pero el efecto del colegio parece ser mucho más fuerte que el del barrio. Las características del colegio que están más asociadas con el resultado en las pruebas son la educación de los profesores, la jornada, el valor de la pensión, y el contexto socio económico del colegio. Las características de los barrios más asociadas con el resultado en las pruebas son, la presencia de universitarios en la UPZ, un clúster de altos niveles de educación y nivel de crimen en el barrio que se correlaciona negativamente. Los resultados anteriores fueron hallados teniendo en cuenta controles familiares y personales.

Small-area variation in health care affecting the choice of cesarean delivery: the case of a Colombian health insurer

In the midst of health care reform, Colombia has succeeded in increasing health insurance coverage and the quality of health care. In spite of this, efficiency continues to be a matter of concern, and small-area variations in health care are one of the plausible causes of such inefficiencies. In order to understand this issue, we use individual data of all births from a Contributory-Regimen insurer in Colombia. We perform two different specifications of a multilevel logistic regression model. Our results reveal that hospitals account for 20% of variation on the probability of performing cesarean sections. Geographic area only explains 1/3 of the variance attributable to the hospital. Furthermore, some variables from both demand and supply sides are found to be also relevant on the probability of undergoing cesarean sections. This paper contributes to previous research by using a hierarchical model and by defining hospitals as cluster. Moreover, we also include clinical and supply induced demand variables.

Regression Models with Heteroscedasticity using Bayesian Approach

In this paper, we compare the performance of two statistical approaches for the analysis of data obtained from the social research area. In the first approach, we use normal models with joint regression modelling for the mean and for the variance heterogeneity. In the second approach, we use hierarchical models. In the first case, individual and social variables are included in the regression modelling for the mean and for the variance, as explanatory variables, while in the second case, the variance at level 1 of the hierarchical model depends on the individuals (age of the individuals), and in the level 2 of the hierarchical model, the variance is assumed to change according to socioeconomic stratum. Applying these methodologies, we analyze a Colombian tallness data set to find differences that can be explained by socioeconomic conditions. We also present some theoretical and empirical results concerning the two models. From this comparative study, we conclude that it is better to jointly modelling the mean and variance heterogeneity in all cases. We also observe that the convergence of the Gibbs sampling chain used in the Markov Chain Monte Carlo method for the jointly modeling the mean and variance heterogeneity is quickly achieved.

Multivariate analysis of fresh-cut carambola slices stored under different temperatures

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

GC Fingerprints Coupled to Pattern-Recognition Multivariate SIMCA Chemometric Analysis for Brazilian Gasoline Quality Studies

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Screening Brazilian commercial gasoline quality by hydrogen nuclear magnetic resonance spectroscopic fingerprintings and pattern-recognition multivariate chemometric analysis

The identification of gasoline adulteration by organic solvents is not an easy task, because compounds that constitute the solvents are already in gasoline composition. In this work, the combination of Hydrogen Nuclear Magnetic Resonance ((1)H NMR) spectroscopic fingerprintings with pattern-recognition multivariate Soft Independent Modeling of Class Analogy (SIMCA) chemometric analysis provides an original and alternative approach to screening Brazilian commercial gasoline quality in a Monitoring Program for Quality Control of Automotive Fuels. SIMCA was performed on spectroscopic fingerprints to classify the quality of representative commercial gasoline samples selected by Hierarchical Cluster Analysis (HCA) and collected over a 6-month period from different gas stations in the São Paulo state, Brazil. Following optimized the (1)H NMR-SIMCA algorithm, it was possible to correctly classify 92.0% of commercial gasoline samples, which is considered acceptable. The chemometric method is recommended for routine applications in Quality-Control Monitoring Programs, since its measurements are fast and can be easily automated. Also, police laboratories could employ this method for rapid screening analysis to discourage adulteration practices. (C) 2010 Elsevier B.V. All rights reserved.

Comparative studies of the sample decomposition of green and roasted coffee for determination of nutrients and data exploratory analysis

The contents of some nutrients in 35 Brazilian green and roasted coffee samples were determined by flame atomic absorption spectrometry (Ca, Mg, Fe, Cu, Mn, and Zn), flame atomic emission photometry (Na and K) and Kjeldahl (N) after preparing the samples by wet digestion procedures using i) a digester heating block and ii) a conventional microwave oven system with pressure and temperature control. The accuracy of the procedures was checked using three standard reference materials (National Institute of Standards and Technology, SRM 1573a Tomato Leaves, SRM 1547 Peach Leaves, SRM 1570a Trace Elements in Spinach). Analysis of data after application of t-test showed that results obtained by microwave-assisted digestion were more accurate than those obtained by block digester at 95% confidence level. Additionally to better accuracy, other favorable characteristics found were lower analytical blanks, lower reagent consumption, and shorter digestion time. Exploratory analysis of results using Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) showed that Na, K, Ca, Cu, Mg, and Fe were the principal elements to discriminate between green and roasted coffee samples. ©2007 Sociedade Brasileira de Química.