118,676 research outputs found
The Quest for Citations: Drivers of Article Impact
No full textWhy do some articles become building blocks for future scholars, while many others remain unnoticed? We aim to answer this question by contrasting, synthesizing and simultaneously testing three scientometric perspectives – universalism, social constructivism and presentation – on the influence of article and author characteristics on article citations. To do so, we study all articles published in a sample of five major journals in marketing from 1990 to 2002 that are central to the discipline. We count the number of citations each of these articles has received and regress this count on an extensive set of characteristics of the article (i.e. article quality, article domain, title length, the use of attention grabbers and expositional clarity), and the author (i.e. author visibility and author personal promotion). We find that the number of citations an article in the marketing discipline receives, depends upon “what one says†(quality and domain), on “who says it†(author visibility and personal promotion) and not so much on “how one says it†(title length, the use of attention grabbers, and expositional clarity). Our insights contribute to the marketing literature and are relevant to scientific stakeholders, such as the management of scientific journals and individual academic scholars, as they strive to maximize citations. They are also relevant to marketing practitioners. They inform practitioners on characteristics of the academic journals in marketing and their relevance to decisions they face. On the other hand, they also raise challenges towards making our journals accessible and relevant to marketing practitioners: (1) authors visible to academics are not necessarily visible to practitioners; (2) the readability of an article may hurt academic credibility and impact, while it may be instrumental in influencing practitioners; (3) it remains questionable whether articles that academics assess to be of high quality are also managerially relevant.Impact;Citation Analysis;Referencing;Scientometrics;Cite
Power and type I error of local fit statistics in multilevel latent class analysis
No full textIn the social and behavioral sciences, variables are often categorical and people are often nested in groups. Models for such data, such as multilevel logistic regression or the multilevel latent class model, should account for not only the categorical nature of the variables, but also the nested structure of the persons. To assess whether the model accomplishes this goal adequately, local fit measures for multilevel categorical data were recently introduced by Nagelkerke, Oberski, and Vermunt (2015). The BVR-group evaluates the variable–group fit, and the BVR-pair evaluates the person–person fit within groups. In this article, we evaluate the performance of these 2 measures for the multilevel latent class model (Vermunt, 2003). An extensive simulation study indicates that whenever multilevel latent class modeling itself is viable, Type I error is controlled and power is adequate for both fit statistics. Thus, the BVR-group and BVR-pair are useful measures to locate important sources of misfit in multilevel latent class analysis
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
Brain areas involved in object and location knowledge in rats: An immunohistochemical investigation using the immediate early genes Arc and cFos
No full textSquare Dancing with the Stars to Enhance Dynamic Hirschman Linkages?
No full textIn this Presidential Address, the author takes the reader on a reconnaissance of his life and time as a regional scientist. He points out scenery he found scintillating along the way, hoping that some may pick up the banner and chew on a few of the ideas for a while. He suggests a revisit to Albert O. Hirschman’s notion of key sectors and more empirical analysis related to Marcus Berliant’s and Masahisa Fujita’s notion of knowledge creation and transfer.Presidential Address, San Antonio, Texas, March 29, 2014 (53rd Meetings of the Southern Regional Science Association
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
Stepwise Latent Vector Autoregression
No full textThis repository contains the files to replicate the simulation study in the publication:
Rein, M. T., Vermunt, J. K., De Roover, K., & Vogelsmeier, L. V. D. E. (2024). Latent Vector Autoregressive Modeling: A Stepwise Estimation Approach. Structural Equation Modeling: A Multidisciplinary Journal, 1-12. https://doi.org/10.1080/10705511.2024.239803
Stepwise Latent Vector Autoregression
No full textThis repository contains the files to replicate the simulation study in the publication:
Rein, M. T., Vermunt, J. K., De Roover, K., & Vogelsmeier, L. V. D. E. (2024). Latent Vector Autoregressive Modeling: A Stepwise Estimation Approach. Structural Equation Modeling: A Multidisciplinary Journal, 1-12. https://doi.org/10.1080/10705511.2024.239803
Letter from unknown writer to Jesse L. Boyce
No full textLetter to Jesse L. Boyce from unknown author (possibly Jack) about the investigation into the powder magazine located in the Grand Canyon. Some personal news is included in the letter such as the writer's marriage to the daughter of C.A. Taylor, former Supervisor of Cochise County
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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