186,283 research outputs found
Recensione di S. ISAGER-P. PEDERSEN (edd.) The Salmakis Inscription and Hellenistic Halikarnassos, Acts of the Symposium Tyrkiet 29.aug.-2.sept. 2000, Halikarnassian Studies 4, Odense 2004
No full textReview of S. ISAGER-P. PEDERSEN (edd.) The Salmakis Inscription and Hellenistic Halikarnassos, Acts of the Symposium Tyrkiet 29.aug.-2.sept. 2000, Halikarnassian Studies 4, Odense 200
Signe Isager et Jens Erik Skydsgaard, Ancient Greek Agriculture. An Introduction
No full textAmouretti Marie-Claire. Signe Isager et Jens Erik Skydsgaard, Ancient Greek Agriculture. An Introduction. In: L'antiquité classique, Tome 63, 1994. p. 530
Open Practices Disclosure, LakensOpenPracticesDisclosure – Equivalence Testing for Psychological Research: A Tutorial
No full textOpen Practices Disclosure, LakensOpenPracticesDisclosure for Equivalence Testing for Psychological Research: A Tutorial by Daniël Lakens, Anne M. Scheel, and Peder M. Isager in Advances in Methods and Practices in Psychological Science</p
Replication Value Usage and its Performance for Large SampleSizes - Commentary on Isager et al. (2021)
No full textTo help researchers determine what studies to replicate, Isager et al. (2021, p.1) introduced the Replication Value (RV), “a proxy for expected utility gain”. In this commentary, we point out that there are scenarios where people can opt to replicate studies with a lower RV compared to those with a higher RV. Methodologically, we highlight that the differences in the RV become very small when comparing studies with different large (e.g., over 500) samples. To adjust for this, we demonstrate that a modification to the RV-equation – by log transforming the sample size – leads to a RV that discriminates between studies with large sample sizes with greater precision
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe 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
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe 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
Commentary on Isager et al. (2021) Reflections on the Replication Value (RV) and a Proposal for Revision
No full textTo help researchers determine what studies to replicate, Isager et al. (2021, p.1) introduced the Replication Value (RV), “a proxy for expected utility gain”. In this commentary, we point out that there are scenarios where people can opt to replicate studies with a lower RV compared to those with a higher RV. Methodologically, we highlight that the differences in the RV become very small when comparing studies with different large (e.g., over 500) samples. To adjust for this, we demonstrate that a modification to the RV-equation – by log transforming the sample size – leads to a RV that discriminates between studies with large sample sizes with greater precision.<br/
Dispelling the Myths Behind First-author Citation Counts
No full textWe 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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