1,721,005 research outputs found
Using the Method of Paired Comparisons in Non-Designed Experiments
No full textIt is shown that a limitation of the various collation methods for paired comparison data currently available is their lack of validity when used in cases where the experiment is incomplete and particularly when the judgements are not replicated. Presented in this thesis is a reasonably thorough background to the method of paired comparisons and an overview of the existing methods for collating paired comparison data into a final ranking. As a result of the extensive review of existing collation methods, the thesis progresses logically to a new collation method that utilises all the available information from a set of pairwise preferences. The performance of the new collation method is extensively tested against existing methods by way of a simulation exercise which highlights the performance of the collation methods under different scenarios in terms of experiment size, experiment completeness and judgement consistency, as well as by considering the number of direct comparisons and the strength of competition. The new collation method and the existing collation method of Allen (1992) are applied to a set of real world data and the outcomes of the two methods are compared. The usefulness of paired comparisons in understanding the way judges use information to construct their own criteria when instructed to make preference decisions at a broad level is also considered and a real world application of this approach is performed. The main findings of this thesis are: „FƒnThe new methodology generally provides an improved performance when there are more than 10 objects to be ranked; „FƒnReplication of each pairwise judgement certainly improves the accuracy of the overall ranking, regardless of the level of judgement inconsistency; „FƒnIn the case of non-replication, the accuracy of the final ranking greatly improves as judgement consistency improves. In other words, if it is not possible to replicate individual pairwise judgements then high judgement consistency is important for a reasonable result; In the case of replication, the accuracy of the returned ranking improves with judgement consistency only in the case of the new method. For the existing methods, the accuracy actually decreases marginally with the improvement of judgement consistency, particularly if there is a low level of experiment completeness; In terms of experiment completeness, for non-replicated experiments, there is an increase in the accuracy of the returned ranking as the proportion of possible pairwise preferences completed increases, but not to the same extent as an increase in judgement consistency. That is, judgement consistency is actually more important than experiment completeness. This suggests that control over the design of the experiment (the extent of completeness and which pairwise preferences are completed) is less important than judgement consistency and replication ¡V certainly a finding not found reported in the literature; The new method outperforms the existing methods when there is perfect or very high judgement consistency.Thesis (PhD Doctorate)Doctor of Philosophy (PhD)School of Australian Environmental StudiesFull Tex
Factors Affecting the Power and Validity of Randomization-Based Multivariate Tests for Difference among Ecological Assemblages
No full textEcologists often want to determine whether there is a difference between the assemblage occupying one habitat, and that in another. While a number of studies have compared a variety of the multivariate techniques used in community ecology, few have considered the ability of different inferential multivariate techniques to detect differences among ecological assemblages. Those that have considered differences among various techniques have focused on model properties, giving little attention to the comparative power of such techniques when applied to ecological datasets. The primary aim of this study was to determine under what conditions different multivariate tests for difference succeed in detecting differences among ecological assemblages, and which conditions do they fail. The focus in this study was on the power of the various tests for difference between assemblages represented by raw species abundance counts, for small to moderate sample sizes. A possible explanation for the limited knowledge about the appropriateness of different tests for difference is the lack of a statistical framework for comparing multivariate tests. One of the problems in the power analysis of tests for comparing ecological assemblages arises from the difficulties in generating the ecologically realistic replicate datasets needed for such a comparison. The number of ways samples may differ in ecological assemblages datasets presents further complications. For example, a multivariate test might be powerful in detecting one type of ecological difference while being relatively insensitive to another type. There are a number of types of ecological difference that studies of ecological assemblages may address. These include: (1) species richness, the number of species occurring in each assemblage; (2) total abundance, the number of individuals (irrespective of species) that occur in each assemblage; (3) species composition, the actual species observed and their relative abundance; (4) the distribution of individuals of a species (or all individuals) across within-assemblage sites; and (5) the distribution of individuals among species. Two simulation methods capable of generating realistic multi- assemblage datasets portraying different levels and types of ecological difference among component assemblages are presented here. This study demonstrates that the empirically calibrated coenocline simulation method is capable of generating realistic artificial ecological datasets portraying simultaneous species richness, total abundance and compositional differences among assemblages. The resampling simulation method, another empirical method, was shown to be able to generate artificial multi- assemblage datasets where assemblages vary compositionally, while other types of ecological difference are held constant. This study compared five multivariate techniques used to tests for difference among ecological assemblages: (1) Parametric MANOVA; (2) CAP, a randomization-based canonical ordination test for difference; and (3) ANOSIM; (4) MRPP; and (5) NP-MANOVA, three variants of Mantel's randomization-based multivariate tests for difference. In the ecological conditions encompassed in this study, CAP was shown to be the most powerful test for compositional difference among assemblages, and ANOSIM, MRPP and NP-MANOVA were more powerful when other types of ecological difference (such as species richness and total abundance differences) were also present. There was little difference in the power of ANOSIM, MRPP and NP-MANOVA under any situation. Parametric MANOVA exhibited very low power in all of the situations encompassed in the power analysis.
Another factor shown to affect the power of a multivariate test for difference is the dissimilarity coefficient on which a test is based, whether this dissimilarity forms an implicit part of the test, or is left to the choice of the researcher (for tests that allows such a choice of dissimilarity coefficient). In this study the power of the four randomization tests (CAP, ANOSIM, MRPP and NP- MANOVA), which allow a choice of dissimilarity coefficient, were compared for the Bray-Curtis, Chi-Square and Euclidean dissimilarity coefficients. Both the Bray-Curtis and the Chi-square dissimilarity coefficients resulted in the most powerful tests, with the more powerful of the two varying with the test for difference under consideration, and/or the type of between assemblage-difference (compositional or general) contained in the dataset. For example, MRPP used in conjunction with the Bray-Curtis dissimilarity coefficient was the most powerful for detecting general differences among assemblages (the type of between-assemblage variation contained in coenocline-generated assemblages), whereas the same test using the Chi- square dissimilarity was more powerful when differences among assemblages were purely compositional (as in resampling simulations). The Euclidean dissimilarity measure almost always resulted in the least power, and never the most power, when used in conjunction with the multivariate tests for difference in realistic ecological assemblage data as represented by raw abundance counts. CAP was shown to be the least sensitive to dissimilarity coefficient choice. The current study has shown a number of the strengths and weaknesses of the tests considered. However, the wide range of ecological situations, and the complexities underlying ecological assemblages, means a lot more work needs to be done before there is a clearer understanding of the relationship between ecological data and appropriateness of multivariate tests. The protocols developed in this study provide a framework for assessing the ability of different multivariate tests for difference and other multivariate techniques used in the analysis of ecological assemblage data.Thesis (PhD Doctorate)Doctor of Philosophy (PhD)School of Australian Environmental StudiesScience, Environment, Engineering and TechnologyFull Tex
The Power of Categorical Goodness-Of-Fit Statistics
No full textThe relative power of goodness-of-fit test statistics has long been debated in the literature. Chi-Square type test statistics to determine 'fit' for categorical data are still dominant in the goodness-of-fit arena. Empirical Distribution Function type goodness-of-fit test statistics are known to be relatively more powerful than Chi-Square type test statistics for restricted types of null and alternative distributions. In many practical applications researchers who use a standard Chi-Square type goodness-of-fit test statistic ignore the rank of ordinal classes. This thesis reviews literature in the goodness-of-fit field, with major emphasis on categorical goodness-of-fit tests. The continued use of an asymptotic distribution to approximate the exact distribution of categorical goodness-of-fit test statistics is discouraged. It is unlikely that an asymptotic distribution will produce a more accurate estimation of the exact distribution of a goodness-of-fit test statistic than a Monte Carlo approximation with a large number of simulations. Due to their relatively higher powers for restricted types of null and alternative distributions, several authors recommend the use of Empirical Distribution Function test statistics over nominal goodness-of-fit test statistics such as Pearson's Chi-Square. In-depth power studies confirm the views of other authors that categorical Empirical Distribution Function type test statistics do not have higher power for some common null and alternative distributions. Because of this, it is not sensible to make a conclusive recommendation to always use an Empirical Distribution Function type test statistic instead of a nominal goodness-of-fit test statistic. Traditionally the recommendation to determine 'fit' for multivariate categorical data is to treat categories as nominal, an approach which precludes any gain in power which may accrue from a ranking, should one or more variables be ordinal. The presence of multiple criteria through multivariate data may result in partially ordered categories, some of which have equal ranking. This thesis proposes a modification to the currently available Kolmogorov-Smirnov test statistics for ordinal and nominal categorical data to account for situations of partially ordered categories. The new test statistic, called the Combined Kolmogorov-Smirnov, is relatively more powerful than Pearson's Chi-Square and the nominal Kolmogorov-Smirnov test statistic for some null and alternative distributions. A recommendation is made to use the new test statistic with higher power in situations where some benefit can be achieved by incorporating an Empirical Distribution Function approach, but the data lack a complete natural ordering of categories. The new and established categorical goodness-of-fit test statistics are demonstrated in the analysis of categorical data with brief applications as diverse as familiarity of defence programs, the number of recruits produced by the Merlin bird, a demographic problem, and DNA profiling of genotypes. The results from these applications confirm the recommendations associated with specific goodness-of-fit test statistics throughout this thesis.Thesis (PhD Doctorate)Doctor of Philosophy (PhD)Australian School of Environmental StudiesFull Tex
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
An Evaluation of the Thai Tsunami Victim Identification DNA Operation
No full textOn 26 December 2004, a 9.3 magnitude earthquake struck off the west of Sumatra, Indonesia triggering a tsunami that killed over 280,000 people in thirteen countries. The total energy released from the earthquake was equivalent to 550 million times that of the Hiroshima atomic bomb. It was one of the deadliest natural disasters in modern history, and, in terms of scale and number of victims, the largest ever disaster victim identification (DVI) operation. In response, teams of police and forensic experts from around the world united to form the Thai Tsunami Victim Identification (TTVI) operation in Phuket from 12 January 2005 in an unprecedented effort to identify 3,679 victims. Approximately half of the victims were foreign tourists who perished along the popular tourist strip in Thailand. Forensic evidence, including the primary identifiers dental, fingerprints and DNA, were used to compare ante-mortem (AM) and post-mortem (PM) data in accordance with INTERPOL DVI guidelines. The identification effort continues today at the Royal Thai Police Headquarters in Bangkok for approximately 370 unidentified victims.Thesis (PhD Doctorate)Doctor of Philosophy (PhD)School of Natural SciencesScience, Environment, Engineering and TechnologyFull Tex
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Statistics in Court - The Ultimate Communication Challenge
No full textGriffith Sciences, Griffith School of EnvironmentNo Full Tex
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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