Competitividade inter-regional de sistemas de produção de bovinocultura de corte

O trabalho apresenta uma metodologia de avaliação de sistemas de produção através da mensuração da competitividade interna na bovinocultura de corte. Durante o primeiro trimestre de 2010, foram aplicados 65 questionários com pecuaristas, sendo 36 entrevistas na Região Sul (Estado do Rio Grande do Sul) e 29 na Região Norte (Estados do Pará e Rondônia). Foram definidos os principais direcionadores que afetam a competitividade interna - tecnologia, gestão, relações de mercado e ambiente institucional, sendo atribuído um peso específico para cada direcionador, a fim de obter o índice de competitividade. Os resultados foram analisados estatisticamente pela teoria de resposta ao item e pela análise de correspondência (ANACOR) com o software SPSS®. A Região Sul apresentou uma maior competitividade que a Região Norte. Independente da região, os fatores críticos de competitividade foram: integração lavoura-pecuária, planejamento estratégico, cálculo de indicadores financeiros, formação de preços, acesso a inovações tecnológicas e organização dos produtores.

Criação de um banco de itens para avaliar o conhecimento do câncer de cabeça e pescoço

Pós-graduação em Odontologia Preventiva e Social - FOA

Construção de uma escala para avaliação da pro ciência em conteúdos matemáticos básicos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Atividades de educação em saúde sobre leishmaniose visceral para escolares

This study aimed at evaluating the knowledge on visceral leishmaniasis gained after the application of an educative project for the 6th and 7th grade students from three public schools of Birigui, SP, Brazil. A questionnaire before (Phase I) and after (Phase II) activities that comprehended one conference by a health agent, a comic contest and one crossword about VL was used to measure scholar’s knowledge. We interviewed 711 students in Phase I and 693 in Phase II. A criterion of VL knowledge was adopted as “Good”, “Medium” and “Bad” when, out of 10 questions analyzed by Item Response Theory, 10 to 8, 7 to 4, and 3 to 0 were right, respectively. We observed a statistically significant increase in the students’ knowledge level after the educational project, since the number of students with “Good” concept changed from 35.7% (Phase I) to 59.7% (Phase II). The educational activities carried out led to gains in knowledge among students suggesting that continuing education can bring good results to public health.

Parameter recovery for a skew-normal IRT model under a Bayesian approach: hierarchical framework, prior and kernel sensitivity and sample size

Item response theory (IRT) comprises a set of statistical models which are useful in many fields, especially when there is an interest in studying latent variables (or latent traits). Usually such latent traits are assumed to be random variables and a convenient distribution is assigned to them. A very common choice for such a distribution has been the standard normal. Recently, Azevedo et al. [Bayesian inference for a skew-normal IRT model under the centred parameterization, Comput. Stat. Data Anal. 55 (2011), pp. 353-365] proposed a skew-normal distribution under the centred parameterization (SNCP) as had been studied in [R. B. Arellano-Valle and A. Azzalini, The centred parametrization for the multivariate skew-normal distribution, J. Multivariate Anal. 99(7) (2008), pp. 1362-1382], to model the latent trait distribution. This approach allows one to represent any asymmetric behaviour concerning the latent trait distribution. Also, they developed a Metropolis-Hastings within the Gibbs sampling (MHWGS) algorithm based on the density of the SNCP. They showed that the algorithm recovers all parameters properly. Their results indicated that, in the presence of asymmetry, the proposed model and the estimation algorithm perform better than the usual model and estimation methods. Our main goal in this paper is to propose another type of MHWGS algorithm based on a stochastic representation (hierarchical structure) of the SNCP studied in [N. Henze, A probabilistic representation of the skew-normal distribution, Scand. J. Statist. 13 (1986), pp. 271-275]. Our algorithm has only one Metropolis-Hastings step, in opposition to the algorithm developed by Azevedo et al., which has two such steps. This not only makes the implementation easier but also reduces the number of proposal densities to be used, which can be a problem in the implementation of MHWGS algorithms, as can be seen in [R.J. Patz and B.W. Junker, A straightforward approach to Markov Chain Monte Carlo methods for item response models, J. Educ. Behav. Stat. 24(2) (1999), pp. 146-178; R. J. Patz and B. W. Junker, The applications and extensions of MCMC in IRT: Multiple item types, missing data, and rated responses, J. Educ. Behav. Stat. 24(4) (1999), pp. 342-366; A. Gelman, G.O. Roberts, and W.R. Gilks, Efficient Metropolis jumping rules, Bayesian Stat. 5 (1996), pp. 599-607]. Moreover, we consider a modified beta prior (which generalizes the one considered in [3]) and a Jeffreys prior for the asymmetry parameter. Furthermore, we study the sensitivity of such priors as well as the use of different kernel densities for this parameter. Finally, we assess the impact of the number of examinees, number of items and the asymmetry level on the parameter recovery. Results of the simulation study indicated that our approach performed equally as well as that in [3], in terms of parameter recovery, mainly using the Jeffreys prior. Also, they indicated that the asymmetry level has the highest impact on parameter recovery, even though it is relatively small. A real data analysis is considered jointly with the development of model fitting assessment tools. The results are compared with the ones obtained by Azevedo et al. The results indicate that using the hierarchical approach allows us to implement MCMC algorithms more easily, it facilitates diagnosis of the convergence and also it can be very useful to fit more complex skew IRT models.

Psychometric properties of the Beck Depression Inventory-II: a comprehensive review

OBJECTIVE: To review the psychometric properties of the Beck Depression Inventory-II (BDI-II) as a self-report measure of depression in a variety of settings and populations. METHODS: Relevant studies of the BDI-II were retrieved through a search of electronic databases, a hand search, and contact with authors. Retained studies (k = 118) were allocated into three groups: non-clinical, psychiatric/institutionalized, and medical samples. RESULTS: The internal consistency was described as around 0.9 and the retest reliability ranged from 0.73 to 0.96. The correlation between BDI-II and the Beck Depression Inventory (BDI-I) was high and substantial overlap with measures of depression and anxiety was reported. The criterion-based validity showed good sensitivity and specificity for detecting depression in comparison to the adopted gold standard. However, the cutoff score to screen for depression varied according to the type of sample. Factor analysis showed a robust dimension of general depression composed by two constructs: cognitive-affective and somatic-vegetative. CONCLUSIONS: The BDI-II is a relevant psychometric instrument, showing high reliability, capacity to discriminate between depressed and non-depressed subjects, and improved concurrent, content, and structural validity. Based on available psychometric evidence, the BDI-II can be viewed as a cost-effective questionnaire for measuring the severity of depression, with broad applicability for research and clinical practice worldwide.

L'impiego del modello di Rash per la valutazione della soddisfazione del paziente nell'ambito delle strutture sanitarie di ricovero

Hospitals and health service providers are use to collect data about patient’s opinion to improve patient health status and communication with them and to upgrade the management and the organization of the health service provided. A lot of survey are carry out for this purpose and several questionnaire are built to measure patient satisfaction. In particular patient satisfaction is a way to describe and assess the level of hospital service from the patient’s point of view. It is a cognitive and an emotional response to the hospital experience. Methodologically patient satisfaction is defined as a multidimensional latent variable. To assess patient satisfaction Item Response Theory has greater advantages compared to Classical Test Theory. Rasch model is a one-parameter model which belongs to Item Response Theory. Rasch model yield objective measure of the construct that are independent of the set of people interviewed and of set of items used. Rasch estimates are continuous and can be useful to “calibrate” the scale of the latent trait. This research attempt to investigate the questionnaire currently adopted to measure patient satisfaction in an Italian hospital, completed by a large sample of 3390 patients. We verify the multidimensional nature of the variable, the properties of the instrument and the level of satisfaction in the hospital. Successively we used Rasch estimates to describe the most satisfied and the less satisfied patients.

A bayesian model for mixed responses and skew laten variable to measure HRQoL in children

The aim of the thesi is to formulate a suitable Item Response Theory (IRT) based model to measure HRQoL (as latent variable) using a mixed responses questionnaire and relaxing the hypothesis of normal distributed latent variable. The new model is a combination of two models already presented in literature, that is, a latent trait model for mixed responses and an IRT model for Skew Normal latent variable. It is developed in a Bayesian framework, a Markov chain Monte Carlo procedure is used to generate samples of the posterior distribution of the parameters of interest. The proposed model is test on a questionnaire composed by 5 discrete items and one continuous to measure HRQoL in children, the EQ-5D-Y questionnaire. A large sample of children collected in the schools was used. In comparison with a model for only discrete responses and a model for mixed responses and normal latent variable, the new model has better performances, in term of deviance information criterion (DIC), chain convergences times and precision of the estimates.

A quality of life subscale for terminally ill cancer patients

A subscale was developed to assess the quality of life of cancer patients with a life expectancy of six months or less. Phase I of this study identified the major concerns of 74 terminally ill cancer patients (19 with breast cancer, 19 with lung cancer, 18 with colorectal cancer, 9 with renal cell cancer, 9 with prostate cancer), 39 family caregivers, and 20 health care professionals. Patients interviewed were being treated at the University of Texas M. D. Anderson Cancer Center or at the Hospice at the Texas Medical Center in Houston. In Phase II, 120 patients (30 with breast cancer, 30 with lung cancer, 30 with colorectal cancer, 15 with prostate cancer, and 15 with renal cell cancer) rated the importance of these concerns for quality of life. Items retained for the subscale were rated as "extremely important" or "very important" by at least 60% of the sample and were reported as being applicable by at least two-thirds of the sample. The 61 concerns that were identified were formatted as a questionnaire for Phase III. In Phase III, 356 patients (89 with breast cancer, 88 with lung cancer, 88 with colorectal cancer, 44 with prostate cancer, and 47 with renal cell cancer) were interviewed to determine the subscale's reliability and sensitivity to change in clinical status. Both factor analysis and item response theory supported the inclusion of the same 35 items for the subscale. Internal consistency reliability was moderate to high for the subscale's domains: spiritual (0.87), existential (0.76), medical care (0.68), symptoms (0.67), social/family (0.66), and emotional (0.61). Test-retest correlation coefficients also were high for the domains: social/family (0.86), emotional (0.83), medical care (0.83), spiritual (0.75), existential (0.75), and symptoms (0.81).^ In addition, concurrent validity was supported by the high correlation between the subscale's symptom domain and symptom items from the European Organization for Research and Treatment of Cancer (EORTC) scale (r = 0.74). Patients' functional status was assessed with the Eastern Cooperative Oncology Group (ECOG) Performance status rating. When ECOG categories were compared to subscale domains, patients who scored lower in functional status had lower scores in the spiritual, existential, social/family, and emotional domains. Patients who scored lower in physical well-being had higher scores in the symptom domain. Patient scores in the medical care domain were similar for each ECOG category. The results of this study support the subscale's use in assessing quality of life and the outcomes of palliative treatment for cancer patients in their last six months of life. ^

A Confirmatory Factor Analytic Study Examining the Dimensionality of Educational Achievement Tests

It is important to check the fundamental assumption of most popular Item Response Theory models, unidimensionality. However, it is hard for educational and psychological tests to be strictly unidimensional. The tests studied in this paper are from a standardized high-stake testing program. They feature potential multidimensionality by presenting various item types and item sets. Confirmatory factor analyses with one-factor and bifactor models, and based on both linear structural equation modeling approach and nonlinear IRT approach were conducted. The competing models were compared and the implications of the bifactor model for checking essential unidimensionality were discussed.

INTEGRATIVE BIOMARKER IDENTIFICATION AND CLASSIFICATION USING HIGH THROUGHPUT ASSAYS

It is well accepted that tumorigenesis is a multi-step procedure involving aberrant functioning of genes regulating cell proliferation, differentiation, apoptosis, genome stability, angiogenesis and motility. To obtain a full understanding of tumorigenesis, it is necessary to collect information on all aspects of cell activity. Recent advances in high throughput technologies allow biologists to generate massive amounts of data, more than might have been imagined decades ago. These advances have made it possible to launch comprehensive projects such as (TCGA) and (ICGC) which systematically characterize the molecular fingerprints of cancer cells using gene expression, methylation, copy number, microRNA and SNP microarrays as well as next generation sequencing assays interrogating somatic mutation, insertion, deletion, translocation and structural rearrangements. Given the massive amount of data, a major challenge is to integrate information from multiple sources and formulate testable hypotheses. This thesis focuses on developing methodologies for integrative analyses of genomic assays profiled on the same set of samples. We have developed several novel methods for integrative biomarker identification and cancer classification. We introduce a regression-based approach to identify biomarkers predictive to therapy response or survival by integrating multiple assays including gene expression, methylation and copy number data through penalized regression. To identify key cancer-specific genes accounting for multiple mechanisms of regulation, we have developed the integIRTy software that provides robust and reliable inferences about gene alteration by automatically adjusting for sample heterogeneity as well as technical artifacts using Item Response Theory. To cope with the increasing need for accurate cancer diagnosis and individualized therapy, we have developed a robust and powerful algorithm called SIBER to systematically identify bimodally expressed genes using next generation RNAseq data. We have shown that prediction models built from these bimodal genes have the same accuracy as models built from all genes. Further, prediction models with dichotomized gene expression measurements based on their bimodal shapes still perform well. The effectiveness of outcome prediction using discretized signals paves the road for more accurate and interpretable cancer classification by integrating signals from multiple sources.

Cyberbullying: The hidden side of college students

The purpose of this study was to investigate how university students perceive their involvement in the cyberbullying phenomenon, and its impact on their well-being. Thus, this study presents a preliminary approach of how college students’ perceived involvement in acts of cyberbullying can be measured. Firstly, Exploratory Factor Analysis (N = 349) revealed a unidimensional structure of the four scales included in the Cyberbullying Inventory for College Students. Then, Item Response Theory (N = 170) was used to analyze the unidimensionality of each scale and the interactions between participants and items. Results revealed good item reliability and Cronbach’s α for each scale. Results also showed the potential of the instrument and how college students underrated their involvement in acts of cyberbullying. Additionally, aggression types, coping strategies and sources of help to deal with cyberbullying were identified and discussed. Lastly, age, gender and course-related issues were considered in the analysis. Implications for researchers and practitioners are discussed.

Many Facet, Common Latent Trait Polytomous IRT Model and EM Algorithm

2000 Mathematics Subject Classification: 91E45.

Group Comparisons on Cognitive Attributes Using the Least Squares Distance Model of Cognitive Diagnosis

2010 Mathematics Subject Classification: 62P15.

Avaliando as escolas pelas suas competências: aplicação do DEA não discricionário para o ensino fundamental do Rio Grande do Norte

Formal education, understood by the gradual process that occurs at school, aims at learning and systematic knowledge is of great interest to society as it benefits its individuals and leads to many positive effects, such as increased productivity and welfare (Johnes, Johnes, 2007). Understanding what influences the educational outcome is as important as the result itself, because lets you manage these variables in order to obtain a better student performance. This work uses the data envelopment analysis (DEA) to compare the efficiency of Rio Grande do Norte schools. In this nonparametric method, an efficiency frontier was construct from the best schools that use the inputs set to generate educational products. Therefore, the data used were obtain by Test Brazil and year 2011 School Census to state and municipal schools of Rio Grande do Norte. Some of the variables considered as inputs and outputs have been obtain directly these bases - the other two were prepared, using the Item Response Theory (IRT) - they are the socioeconomic and school infrastructure indices. As a first step, we compared several DEA models, with changes of input variables. Then was chose the non-discretionary model for which was deep the analysis of results. The results showed that only seven schools were efficient in the 5th and 9th grades simultaneously; there were no significant differences between the efficiency of municipal and state schools; and there were no differences between large and small schools. Analyzing the municipalities, Mossoró excelled in both years with the highest proportion of efficient schools. Finally, the study suggests that using the projections provided by the DEA method, the most inefficient schools would be able to achieve the goal IDEB in 2011, in other words, it is possible to improve the education of significant state taking the efficient schools as a basis for too much.