Scoring Methods for Ordinal Multidimensional Forced-Choice Items

In most psychological tests and questionnaires, a test score is obtained by taking the sum of the item scores. In virtually all cases where the test or questionnaire contains multidimensional forced-choice items, this traditional scoring method is also applied. We argue that the summation of scores obtained with multidimensional forced-choice items produces uninterpretable test scores. Therefore, we propose three alternative scoring methods: a weak and a strict rank preserving scoring method, which both allow an ordinal interpretation of test scores; and a ratio preserving scoring method, which allows a proportional interpretation of test scores. Each proposed scoring method yields an index for each respondent indicating the degree to which the response pattern is inconsistent. Analysis of real data showed that with respect to rank preservation, the weak and strict rank preserving method resulted in lower inconsistency indices than the traditional scoring method; with respect to ratio preservation, the ratio preserving scoring method resulted in lower inconsistency indices than the traditional scoring method

Entering Data into Excel

Video on how to enter data into Excel. This includes setting up column headings, changing sheet names, using lists for categorical items, using validation on columns of data, plus how to check validation is operating correctly.

Empowerment: model i instrument de mesura basat en les variables primàries o efectives PIEQ (Primary Items Empowerment Questionnaire)

In this thesis a review of the state of the art on empowerment is found. A proposal is put forward for a model of the effective variables of empowerment, a new theoretical model that provides a distinct classification of individual variables. The model consists of four metavariables called reciprocal, unidirectional, shared and reflexive. Finally, a measurement tool for measuring and representing these variables graphically is described, including its process of elaboration through a preliminary pilot study that served to refine and improve the questionnaire. Both the model and the instrument proposed aim to determine the state of the primary variables involved in determining the predisposition and potential of a working group towards empowerment. With the measurement tool both organisational, departmental, working group and/or individual data can be obtained. This instrument can help companies and organisations to discover the limitations existing within a working group and act in consequence.

Understanding climate data through commentary metadata: the CHARMe project

We describe the CHARMe project, which aims to link climate datasets with publications, user feedback and other items of "commentary metadata". The system will help users learn from previous community experience and select datasets that best suit their needs, as well as providing direct traceability between conclusions and the data that supported them. The project applies the principles of Linked Data and adopts the Open Annotation standard to record and publish commentary information. CHARMe contributes to the emerging landscape of "climate services", which will provide climate data and information to influence policy and decision-making. Although the project focuses on climate science, the technologies and concepts are very general and could be applied to other fields.

An incremental space to visualize dynamic data sets

In Information Visualization, adding and removing data elements can strongly impact the underlying visual space. We have developed an inherently incremental technique (incBoard) that maintains a coherent disposition of elements from a dynamic multidimensional data set on a 2D grid as the set changes. Here, we introduce a novel layout that uses pairwise similarity from grid neighbors, as defined in incBoard, to reposition elements on the visual space, free from constraints imposed by the grid. The board continues to be updated and can be displayed alongside the new space. As similar items are placed together, while dissimilar neighbors are moved apart, it supports users in the identification of clusters and subsets of related elements. Densely populated areas identified in the incSpace can be efficiently explored with the corresponding incBoard visualization, which is not susceptible to occlusion. The solution remains inherently incremental and maintains a coherent disposition of elements, even for fully renewed sets. The algorithm considers relative positions for the initial placement of elements, and raw dissimilarity to fine tune the visualization. It has low computational cost, with complexity depending only on the size of the currently viewed subset, V. Thus, a data set of size N can be sequentially displayed in O(N) time, reaching O(N (2)) only if the complete set is simultaneously displayed.

Harmonization Of Vocabularies For Water Data

Observational data encodes values of properties associated with a feature of interest, estimated by a specified procedure. For water the properties are physical parameters like level, volume, flow and pressure, and concentrations and counts of chemicals, substances and organisms. Water property vocabularies have been assembled at project, agency and jurisdictional level. Organizations such as EPA, USGS, CEH, GA and BoM maintain vocabularies for internal use, and may make them available externally as text files. BODC and MMI have harvested many water vocabularies alongside others of interest in their domain, formalized the content using SKOS, and published them through web interfaces. Scope is highly variable both within and between vocabularies. Individual items may conflate multiple concerns (e.g. property, instrument, statistical procedure, units). There is significant duplication between vocabularies. Semantic web technologies provide the opportunity both to publish vocabularies more effectively, and achieve harmonization to support greater interoperability between datasets. - Models for vocabulary items (property, substance/taxon, process, unit-of-measure, etc) may be formalized OWL ontologies, supporting semantic relations between items in related vocabularies; - By specializing the ontology elements from SKOS concepts and properties, diverse vocabularies may be published through a common interface; - Properties from standard vocabularies (e.g. OWL, SKOS, PROV-O and VAEM) support mappings between vocabularies having a similar scope - Existing items from various sources may be assembled into new virtual vocabularies However, there are a number of challenges: - use of standard properties such as sameAs/exactMatch/equivalentClass require reasoning support; - items have been conceptualised as both classes and individuals, complicating the mapping mechanics; - re-use of items across vocabularies may conflict with expectations concerning URI patterns; - versioning complicates cross-references and re-use. This presentation will discuss ways to harness semantic web technologies to publish harmonized vocabularies, and will summarise how many of the challenges may be addressed.

Preliminary data on the feeding habits of the freshwater stingrays Potamotrygon falkneri and Potamotrygon motoro (Potamotrygonidae) from the Upper Paraná River basin, Brazil

As raias da família Potamotrygonidae representam um grupo singular da ictiofauna Neotropical. Apesar de serem antigos os relatos sobre o grupo, ainda são muitas as questões que permanecem sem resposta, sobretudo no que diz respeito à biologia das espécies que ocorrem na Bacia do Paraná-Paraguai. No presente trabalho foi analisada a dieta de Potamotrygon falkneri e Potamotrygon motoro, capturadas no Alto Rio Paraná, a jusante da Usina Hidrelétrica Engenheiro Souza Dias (UHE Jupiá). As duas espécies de raias apresentaram dieta diversificada, ingerindo 14 itens, entre moluscos, crustáceos, insetos e peixes, porém com predominância de insetos aquáticos em diversidade e abundância. Somente um indivíduo de cada espécie ingeriu peixe. Potamotrygon motoro consumiu principalmente Ephemeroptera, enquanto P. falkneri, principalmente Mollusca, Hemiptera e Trichoptera. Os dados aparentemente indicam uma dieta mais especializada de P. motoro, com maior consumo de Ephemeroptera (Baetidae), e uma dieta mais generalizada de P. falkneri. A análise dos indivíduos capturados em três micro-hábitats, que diferem quanto ao tipo de substrato e presença de vegetação marginal, sugere diferenças nos tipos de alimentos consumidos.

Clinical research on new drugs (Phase I). Profile of scientific publications: data from the pre-clinical phase and bioethical aspects

OBJETIVO: Traçar o perfil das publicações científicas de fase I e procurar saber se a publicação oferece dados da fase pré-clínica com ênfase nos aspectos bioéticos. MÉTODOS: Foram analisados 61 artigos científicos publicados no ano de 2007, que relatam pesquisas envolvendo seres humanos com novos fármacos, medicamentos ou vacinas em fase I. Foi elaborado um roteiro para coleta de dados, com o qual fosse possível analisar e avaliar os artigos científicos. O roteiro contempla itens referentes à fase pré-clínica (associados à fase clínica) e itens referentes às características da amostra. RESULTADOS: Nos artigos analisados, a maioria das pesquisas foi realizada nos EUA. Devido ao grande número de publicações destinadas às doenças oncológicas a maioria delas foi realizada com voluntários doentes. Quanto às informações sobre a fase pré-clínica presente nas publicações de fase I observamos que são pobres ou inexistentes. Mesmo que os autores julguem a pesquisa fase I como promissora e sugiram estudos futuros de fase II, ao leitor não é possível este mesmo julgamento pela escassez de informações da fase pré-clínica. CONCLUSÃO: O perfil das publicações levanta dados que merecem reflexão e análise para melhor avaliação do que está ocorrendo com as publicações de fase I.

Economic design of Shewhart control charts for monitoring autocorrelated data with skip sampling strategies

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

MR-Radix: a multi-relational data mining algorithm

Background: Once multi-relational approach has emerged as an alternative for analyzing structured data such as relational databases, since they allow applying data mining in multiple tables directly, thus avoiding expensive joining operations and semantic losses, this work proposes an algorithm with multi-relational approach. Methods: Aiming to compare traditional approach performance and multi-relational for mining association rules, this paper discusses an empirical study between PatriciaMine - an traditional algorithm - and its corresponding multi-relational proposed, MR-Radix. Results: This work showed advantages of the multi-relational approach in performance over several tables, which avoids the high cost for joining operations from multiple tables and semantic losses. The performance provided by the algorithm MR-Radix shows faster than PatriciaMine, despite handling complex multi-relational patterns. The utilized memory indicates a more conservative growth curve for MR-Radix than PatriciaMine, which shows the increase in demand of frequent items in MR-Radix does not result in a significant growth of utilized memory like in PatriciaMine. Conclusion: The comparative study between PatriciaMine and MR-Radix confirmed efficacy of the multi-relational approach in data mining process both in terms of execution time and in relation to memory usage. Besides that, the multi-relational proposed algorithm, unlike other algorithms of this approach, is efficient for use in large relational databases.

MR-Radix: a multi-relational data mining algorithm

Abstract Background Once multi-relational approach has emerged as an alternative for analyzing structured data such as relational databases, since they allow applying data mining in multiple tables directly, thus avoiding expensive joining operations and semantic losses, this work proposes an algorithm with multi-relational approach. Methods Aiming to compare traditional approach performance and multi-relational for mining association rules, this paper discusses an empirical study between PatriciaMine - an traditional algorithm - and its corresponding multi-relational proposed, MR-Radix. Results This work showed advantages of the multi-relational approach in performance over several tables, which avoids the high cost for joining operations from multiple tables and semantic losses. The performance provided by the algorithm MR-Radix shows faster than PatriciaMine, despite handling complex multi-relational patterns. The utilized memory indicates a more conservative growth curve for MR-Radix than PatriciaMine, which shows the increase in demand of frequent items in MR-Radix does not result in a significant growth of utilized memory like in PatriciaMine. Conclusion The comparative study between PatriciaMine and MR-Radix confirmed efficacy of the multi-relational approach in data mining process both in terms of execution time and in relation to memory usage. Besides that, the multi-relational proposed algorithm, unlike other algorithms of this approach, is efficient for use in large relational databases.

Multidimensional item response theory models with general and specific latent traits for ordinal data

The aim of the thesis is to propose a Bayesian estimation through Markov chain Monte Carlo of multidimensional item response theory models for graded responses with complex structures and correlated traits. In particular, this work focuses on the multiunidimensional and the additive underlying latent structures, considering that the first one is widely used and represents a classical approach in multidimensional item response analysis, while the second one is able to reflect the complexity of real interactions between items and respondents. A simulation study is conducted to evaluate the parameter recovery for the proposed models under different conditions (sample size, test and subtest length, number of response categories, and correlation structure). The results show that the parameter recovery is particularly sensitive to the sample size, due to the model complexity and the high number of parameters to be estimated. For a sufficiently large sample size the parameters of the multiunidimensional and additive graded response models are well reproduced. The results are also affected by the trade-off between the number of items constituting the test and the number of item categories. An application of the proposed models on response data collected to investigate Romagna and San Marino residents' perceptions and attitudes towards the tourism industry is also presented.

Multivariate modelling of responses to conditional items: New possibilities for latent class analysis

Questionnaire data may contain missing values because certain questions do not apply to all respondents. For instance, questions addressing particular attributes of a symptom, such as frequency, triggers or seasonality, are only applicable to those who have experienced the symptom, while for those who have not, responses to these items will be missing. This missing information does not fall into the category 'missing by design', rather the features of interest do not exist and cannot be measured regardless of survey design. Analysis of responses to such conditional items is therefore typically restricted to the subpopulation in which they apply. This article is concerned with joint multivariate modelling of responses to both unconditional and conditional items without restricting the analysis to this subpopulation. Such an approach is of interest when the distributions of both types of responses are thought to be determined by common parameters affecting the whole population. By integrating the conditional item structure into the model, inference can be based both on unconditional data from the entire population and on conditional data from subjects for whom they exist. This approach opens new possibilities for multivariate analysis of such data. We apply this approach to latent class modelling and provide an example using data on respiratory symptoms (wheeze and cough) in children. Conditional data structures such as that considered here are common in medical research settings and, although our focus is on latent class models, the approach can be applied to other multivariate models.