1,720,956 research outputs found
GORIC Evidence Aggregation: Combining Statistical Evidence for a Central Theory from Diverse Studies using an AIC-type Criterion
No full textIn social and behavioral science, the gold standard for scientific evidence is finding results that are consistent across independent studies. To summarize results from multiple studies, parameter estimates are conventionally aggregated with meta-analysis. However, this method is limited to studies that share the same context and design, which often means that a wealth of information remains unexploited. This paper proposes evidence aggregation using GORIC(A) weights: an alternative and/or complementary statistical tool for the aggregation of evidence across studies. Rather than aggregating parameter estimates to come to an overall estimate, GORIC(A) evidence aggregation combines support for a shared central theory and quantifies the overall support. It does so using GORIC(A), an information criterion that can evaluate both equality and inequality/order restrictions. GORIC(A) can be applied to a single study, and this GORIC(A) evidence can be aggregated over multiple studies, irrespective of context or design. The method is validated with a simulation study that shows that GORIC(A) evidence aggregation is not affected by study heterogeneity and can be used for evidence synthesis. This implies that GORIC(A) evidence aggregation can successfully combine evidence for a central theory over a widely diverse set of studies. This increases the available information to investigate a theory. Furthermore, GORIC(A) evidence aggregation aids in robustness and confidence of results because it can take into account the results of all type of studies that examine the central theory
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
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
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
Aggregating evidence from conceptual replication studies using the product Bayes factor
No full textThe product Bayes factor (PBF) can synthesize evidence for an informative hypothesis across heterogeneous replication studies. It is particularly useful when the number of studies is relatively low and conventional assumptions about between-studies heterogeneity are likely violated. The present paper introduces a user-friendly implementation of the PBF in the bain R-package. The method was validated in a simulation study that manipulated sample size, number of replication samples, and reliability. Several tutorial examples demonstrate the use of the method in distinct use cases. Results of the simulation study show that PBF had a higher overall accuracy when benchmarked against other evidence synthesis methods, including random-effects meta-analysis (RMA). This was primarily due to PBF’s greater sensitivity in detecting a true effect. However, PBF had relatively lower specificity. The PBF showed increasing sensitivity and specificity with increasing sample size. With an increasing number of samples, lower sensitivity was traded for greater specificity. Although PBF's overall performance was less susceptible to reliability than the other algorithms, this masked a trade-off between reliability and specificity. PBF thus appears to be a promising method for meta-analysis of heterogeneous conceptual replication studies. Nonetheless, users should be aware of its lower specificity, and the fact that the Bayesian approach to inference addresses a qualitatively different research question than other evidence synthesis methods
Selecting relevant moderators with Bayesian regularized meta-regression
No full textWhen meta-analyzing heterogeneous bodies of literature, meta-regression can be used to account for potentially relevant between-studies differences. A key challenge is that the number of candidate moderators is often high relative to the number of studies. This introduces risks of overfitting, spurious results, and model non-convergence. To overcome these challenges, we introduce Bayesian Regularized Meta-Analysis (BRMA), which selects relevant moderators from a larger set of candidates by shrinking small regression coefficients towards zero with regularizing (LASSO or horseshoe) priors. This method is suitable when there are many potential moderators, but it is not known beforehand which of them are relevant. A simulation study compared BRMA against state-of-the-art random effects meta-regression using restricted maximum likelihood (RMA). Results indicated that BRMA outperformed RMA on three metrics: BRMA had superior predictive performance, which means that the results generalized better; BRMA was better at rejecting irrelevant moderators, and worse at detecting true effects of relevant moderators, while the overall proportion of Type I and Type II errors was equivalent to RMA. BRMA regression coefficients were slightly biased towards zero (by design), but its residual heterogeneity estimates were less biased than those of RMA. BRMA performed well with as few as 20 studies, suggesting its suitability as a small sample solution. We present free open source software implementations in the R-package pema (for penalized meta-analysis) and in the stand-alone statistical program JASP. An applied example demonstrates the use of the R-package
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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