Search (150 results, page 8 of 8)

  • × theme_ss:"Informetrie"
  • × year_i:[2010 TO 2020}
  1. Orduña-Malea, E.; Torres-Salinas, D.; López-Cózar, E.D.: Hyperlinks embedded in twitter as a proxy for total external in-links to international university websites (2015) 0.00 
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  2. Ufen, F.: ¬Das System als Roman : Eine Regelmäßigkeit der Worthäufigkeit passt bemerkenswerterweise auch auf Unternehmensgrößen und die Größe von Städten (2015) 0.00 
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    Language
    d
  3. Kozak, M.; Iefremova, O.; Szkola, J.; Sas, D.: Do researchers provide public or institutional E-mail accounts as correspondence E-mails in scientific articles? (2015) 0.00 
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  4. Arora, S.K.; Li, Y.; Youtie, J.; Shapira, P.: Using the wayback machine to mine websites in the social sciences : a methodological resource (2016) 0.00 
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    Abstract
    Websites offer an unobtrusive data source for developing and analyzing information about various types of social science phenomena. In this paper, we provide a methodological resource for social scientists looking to expand their toolkit using unstructured web-based text, and in particular, with the Wayback Machine, to access historical website data. After providing a literature review of existing research that uses the Wayback Machine, we put forward a step-by-step description of how the analyst can design a research project using archived websites. We draw on the example of a project that analyzes indicators of innovation activities and strategies in 300 U.S. small- and medium-sized enterprises in green goods industries. We present six steps to access historical Wayback website data: (a) sampling, (b) organizing and defining the boundaries of the web crawl, (c) crawling, (d) website variable operationalization, (e) integration with other data sources, and (f) analysis. Although our examples draw on specific types of firms in green goods industries, the method can be generalized to other areas of research. In discussing the limitations and benefits of using the Wayback Machine, we note that both machine and human effort are essential to developing a high-quality data set from archived web information.
  5. Zhang, Y.; Zhang, G.; Zhu, D.; Lu, J.: Scientific evolutionary pathways : identifying and visualizing relationships for scientific topics (2017) 0.00 
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  6. Mutz, R.; Wolbring, T.; Daniel, H.-D.: ¬The effect of the "very important paper" (VIP) designation in Angewandte Chemie International Edition on citation impact : a propensity score matching analysis (2017) 0.00 
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  7. Farys, R.; Wolbring, T.: Matched control groups for modeling events in citation data : an illustration of nobel prize effects in citation networks (2017) 0.00 
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    Abstract
    Bibliometric data are frequently used to study the effects of events, such as the honoring of a scholar with an award, and to investigate changes of citation impact over time. However, the number of yearly citations depends upon time for multiple reasons: a) general time trends in citation data, b) changing coverage of databases, c) individual citation life-cycles, and d) selection on citation impact. Hence, it is often ill-advised to simply compare the average number of citations before and after an event to estimate its causal effect. Using a recent publication in this journal on the potential citation chain reaction of a Nobel Prize, we demonstrate that a simple pre-post comparison can lead to biased and misleading results. We propose using matched control groups to improve causal inference and illustrate that the inclusion of a tailor-made synthetic control group in the statistical analysis helps to avoid methodological artifacts. Our results suggest that there is neither a Nobel Prize effect as regards citation impact of the Nobel laureate under investigation nor a related chain reaction in the citation network, as suggested in the original study. Finally, we explain that these methodological recommendations extend far beyond the study of Nobel Prize effects in citation data.
  8. Aledo, J.A.; Gámez, J.A.; Molina, D.; Rosete, A.: Consensus-based journal rankings : a complementary tool for bibliometric evaluation (2018) 0.00 
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  9. Park, H.; You, S.; Wolfram, D.: Informal data citation for data sharing and reuse is more common than formal data citation in biomedical fields (2018) 0.00 
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  10. Stuart, D.: Web metrics for library and information professionals (2014) 0.00 
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