191 research outputs found

    Dimensions Underlying Student Ratings of Instruction: A Multidimensional Scaling Analysis

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    ↵MARTHA L. BANZ, Graduate Student, Department of Psychology, University of Oklahoma, Norman, OK 73019.Specializations:Quantitative methods, educational psychology.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

    What is the value of social values? The uselessness of assessing health-related quality of life through preference measures

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    BACKGROUND: The use of preference-based measures in the evaluation of health outcomes has extended considerably over the last decade. Their alleged advantage over other types of general instruments in the evaluation of health related quality of life (HRQOL), supposedly lies in the fact that preference measures incorporate values or utilities that reflects the value of social preferences through health states. The objective of this study was to determine whether the use of social preference weights or utilities makes any real difference when calculating scores for the Euroqol (EQ5-D) questionnaire, a HRQOL preference-based measure. METHODS: Responses to the EQ5-D of a sample of 10,972 patients from 10 countries enrolled in an observational study of the treatment of schizophrenia in Europe were used for this purpose. Two different methods of scoring the EQ-5D where compared: 'weighting the items' of the questionnaire through the UK official weight coefficients, and 'non-weighting the items'. Pearson's, Spearman's, and two-way mixed parametric intraclass correlation coefficients were used to estimate the association of the scores obtained in both ways. RESULTS: The association between weighted and unweighted Euroqol scores was extremely high (Pearson's r = 0.91), as was the association between their ranks (Spearman's ρ = 0.93). The intraclass correlation coefficient obtained (0.89) also suggested that the concordance between the score distributions was prominent. CONCLUSIONS: A non-weighted approach to score the EQ5-D is enough to explain a high proportion of variance in scores obtained through the use of utilities. The differential contribution of weights based on population preference values is therefore minimal and, in our opinion, negligible

    Visualising Evolution History in Multi- and Many-Objective Optimisation

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    Evolutionary algorithms are widely used to solve optimisation problems. However, challenges of transparency arise in both visualising the processes of an optimiser operating through a problem and understanding the problem features produced from many-objective problems, where comprehending four or more spatial dimensions is difficult. This work considers the visualisation of a population as an optimisation process executes. We have adapted an existing visualisation technique to multi- and many-objective problem data, enabling a user to visualise the EA processes and identify specific problem characteristics and thus providing a greater understanding of the problem landscape. This is particularly valuable if the problem landscape is unknown, contains unknown features or is a many-objective problem. We have shown how using this framework is effective on a suite of multi- and many-objective benchmark test problems, optimising them with NSGA-II and NSGA-III

    Evaluation of biases present in the cohort multiple randomised controlled trial design: a simulation study

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    Background The cohort multiple randomised controlled trial (cmRCT) design provides an opportunity to incorporate the benefits of randomisation within clinical practice; thus reducing costs, integrating electronic healthcare records, and improving external validity. This study aims to address a key concern of the cmRCT design: refusal to treatment is only present in the intervention arm, and this may lead to bias and reduce statistical power. Methods We used simulation studies to assess the effect of this refusal, both random and related to event risk, on bias of the effect estimator and statistical power. A series of simulations were undertaken that represent a cmRCT trial with time-to-event endpoint. Intention-to-treat (ITT), per protocol (PP), and instrumental variable (IV) analysis methods, two stage predictor substitution and two stage residual inclusion, were compared for various refusal scenarios. Results We found the IV methods provide a less biased estimator for the causal effect when refusal is present in the intervention arm, with the two stage residual inclusion method performing best with regards to minimum bias and sufficient power. We demonstrate that sample sizes should be adapted based on expected and actual refusal rates in order to be sufficiently powered for IV analysis. Conclusion We recommend running both an IV and ITT analyses in an individually randomised cmRCT as it is expected that the effect size of interest, or the effect we would observe in clinical practice, would lie somewhere between that estimated with ITT and IV analyses. The optimum (in terms of bias and power) instrumental variable method was the two stage residual inclusion method. We recommend using adaptive power calculations, updating them as refusal rates are collected in the trial recruitment phase in order to be sufficiently powered for IV analysis

    Interactive Visual Data Exploration with Subjective Feedback

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    Data visualization and iterative/interactive data mining are growing rapidly in attention, both in research as well as in industry. However, integrated methods and tools that combine advanced visualization and data mining techniques are rare, and those that exist are often specialized to a single problem or domain. In this paper, we introduce a novel generic method for interactive visual exploration of high-dimensional data. In contrast to most visualization tools, it is not based on the traditional dogma of manually zooming and rotating data. Instead, the tool initially presents the user with an ‘interesting’ projection of the data and then employs data randomization with constraints to allow users to flexibly and intuitively express their interests or beliefs using visual interactions that correspond to exactly defined constraints. These constraints expressed by the user are then taken into account by a projection-finding algorithm to compute a new ‘interesting’ projection, a process that can be iterated until the user runs out of time or finds that constraints explain everything she needs to find from the data. We present the tool by means of two case studies, one controlled study on synthetic data and another on real census data. The data and software related to this paper are available at http://​www.​interesting-patterns.​net/​forsied/​interactive-visual-data-exploration-with-subjective-feedback/​

    Simplification and Shift in Cognition of Political Difference: Applying the Geometric Modeling to the Analysis of Semantic Similarity Judgment

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    Perceiving differences by means of spatial analogies is intrinsic to human cognition. Multi-dimensional scaling (MDS) analysis based on Minkowski geometry has been used primarily on data on sensory similarity judgments, leaving judgments on abstractive differences unanalyzed. Indeed, analysts have failed to find appropriate experimental or real-life data in this regard. Our MDS analysis used survey data on political scientists' judgments of the similarities and differences between political positions expressed in terms of distance. Both distance smoothing and majorization techniques were applied to a three-way dataset of similarity judgments provided by at least seven experts on at least five parties' positions on at least seven policies (i.e., originally yielding 245 dimensions) to substantially reduce the risk of local minima. The analysis found two dimensions, which were sufficient for mapping differences, and fit the city-block dimensions better than the Euclidean metric in all datasets obtained from 13 countries. Most city-block dimensions were highly correlated with the simplified criterion (i.e., the left–right ideology) for differences that are actually used in real politics. The isometry of the city-block and dominance metrics in two-dimensional space carries further implications. More specifically, individuals may pay attention to two dimensions (if represented in the city-block metric) or focus on a single dimension (if represented in the dominance metric) when judging differences between the same objects. Switching between metrics may be expected to occur during cognitive processing as frequently as the apparent discontinuities and shifts in human attention that may underlie changing judgments in real situations occur. Consequently, the result has extended strong support for the validity of the geometric models to represent an important social cognition, i.e., the one of political differences, which is deeply rooted in human nature

    A novel series of compositionally biased substitution matrices for comparing Plasmodium proteins

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    <p>Abstract</p> <p>Background</p> <p>The most common substitution matrices currently used (BLOSUM and PAM) are based on protein sequences with average amino acid distributions, thus they do not represent a fully accurate substitution model for proteins characterized by a biased amino acid composition. This problem has been addressed recently by adjusting existing matrices, however, to date, no empirical approach has been taken to build matrices which offer a substitution model for comparing proteins sharing an amino acid compositional bias. Here, we present a novel procedure to construct series of symmetrical substitution matrices to align proteins from similarly biased <it>Plasmodium </it>proteomes.</p> <p>Results</p> <p>We generated substitution matrices by selecting from the BLOCKS database those multiple alignments with a compositional bias similar to that of <it>P. falciparum </it>and <it>P. yoelii </it>proteins. A novel 'fuzzy' clustering method was adopted to group sequences within these alignments, showing that this method retains more complete information on the amino acid substitutions when compared to hierarchical clustering. We assessed the performance against the BLOSUM62 series and showed that the usage of our matrices results in an improvement in the performance of BLAST database searches, greatly reducing the number of false positive hits. We then demonstrated applications of the use of novel matrices to improve the annotation of homologs between the two <it>Plasmodium </it>species and to classify members of the <it>P. falciparum </it>RIFIN/STEVOR family.</p> <p>Conclusion</p> <p>We confirmed that in the case of compositionally biased proteins, standard BLOSUM matrices are not suited for optimal alignments, and specific substitution matrices are required. In addition, we showed that the usage of these matrices leads to a reduction of false positive hits, facilitating the automatic annotation process.</p

    Teosinte Inflorescence Phytolith Assemblages Mirror Zea Taxonomy

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    Molecular DNA analyses of the New World grass (Poaceae) genus Zea, comprising five species, has resolved taxonomic issues including the most likely teosinte progenitor (Zea mays ssp. parviglumis) of maize (Zea mays ssp. mays). However, archaeologically, little is known about the use of teosinte by humans both prior to and after the domestication of maize. One potential line of evidence to explore these relationships is opaline phytoliths produced in teosinte fruit cases. Here we use multidimensional scaling and multiple discriminant analyses to determine if rondel phytolith assemblages from teosinte fruitcases reflect teosinte taxonomy. Our results indicate that rondel phytolith assemblages from the various taxa, including subspecies, can be statistically discriminated. This indicates that it will be possible to investigate the archaeological histories of teosinte use pending the recovery of appropriate samples

    Theoretical framework and methodological development of common subjective health outcome measures in osteoarthritis: a critical review

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    Subjective measures involving clinician ratings or patient self-assessments have become recognised as an important tool for the assessment of health outcome. The value of a health outcome measure is usually assessed by a psychometric evaluation of its reliability, validity and responsiveness. However, psychometric testing involves an accumulation of evidence and has recognised limitations. It has been suggested that an evaluation of how well a measure has been developed would be a useful additional criteria in assessing the value of a measure. This paper explored the theoretical background and methodological development of subjective health status measures commonly used in osteoarthritis research. Fourteen subjective health outcome measures commonly used in osteoarthritis research were examined. Each measure was explored on the basis of their i) theoretical framework (was there a definition of what was being assessed and was it part of a theoretical model?) and ii) methodological development (what was the scaling strategy, how were the items generated and reduced, what was the response format and what was the scoring method?). Only the AIMS, SF-36 and WHOQOL defined what they were assessing (i.e. the construct of interest) and no measure assessed was part of a theoretical model. None of the clinician report measures appeared to have implemented a scaling procedure or described the rationale for the items selected or scoring system. Of the patient self-report measures, the AIMS, MPQ, OXFORD, SF-36, WHOQOL and WOMAC appeared to follow a standard psychometric scaling method. The DRP and EuroQol used alternative scaling methods. The review highlighted the general lack of theoretical framework for both clinician report and patient self-report measures. This review also drew attention to the wide variation in the methodological development of commonly used measures in OA. While, in general the patient self-report measures had good methodological development, the clinician report measures appeared less well developed. It would be of value if new measures defined the construct of interest and, that the construct, be part of theoretical model. By ensuring measures are both theoretically and empirically valid then improvements in subjective health outcome measures should be possible
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