Search (5 results, page 1 of 1)

  • × theme_ss:"Literaturübersicht"
  • × theme_ss:"Citation indexing"
  1. Nicolaisen, J.: Citation analysis (2007) 0.06
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    Date
    13. 7.2008 19:53:22
    Type
    a
  2. Bensman, S.J.: Garfield and the impact factors (2007) 0.00
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    Type
    a
  3. Osareh, F.: Bibliometrics, citation analysis and co-citation analysis : a review of literature II (1996) 0.00
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    Abstract
    Part 2 of a 2 part article reviewing the technique of bibliometrics and one of its most widely used methods, citation analysis. Reports on studies of author co-citation, periodical by periodical citation analysis and country by country citation analysis in addition to the mapping of science as an application of citation analysis. Considers the limitations, problems and reliability of citation analysis
    Type
    a
  4. Osareh, F.: Bibliometrics, citation analysis and co-citation analysis : a review of literature I (1996) 0.00
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    Abstract
    Part 1 of a 2 part article reviewing the technique of bibliometrics and one of its most widely used methods, citation analysis. Traces the history and development of bibliometrics, including its definition, scope, role in scholarly communication and applications. Treats citation analysis similarly with particular reference to bibliographic coupling and cocitation coupling
    Type
    a
  5. Thelwall, M.; Vaughan, L.; Björneborn, L.: Webometrics (2004) 0.00
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    Abstract
    Webometrics, the quantitative study of Web-related phenomena, emerged from the realization that methods originally designed for bibliometric analysis of scientific journal article citation patterns could be applied to the Web, with commercial search engines providing the raw data. Almind and Ingwersen (1997) defined the field and gave it its name. Other pioneers included Rodriguez Gairin (1997) and Aguillo (1998). Larson (1996) undertook exploratory link structure analysis, as did Rousseau (1997). Webometrics encompasses research from fields beyond information science such as communication studies, statistical physics, and computer science. In this review we concentrate on link analysis, but also cover other aspects of webometrics, including Web log fle analysis. One theme that runs through this chapter is the messiness of Web data and the need for data cleansing heuristics. The uncontrolled Web creates numerous problems in the interpretation of results, for instance, from the automatic creation or replication of links. The loose connection between top-level domain specifications (e.g., com, edu, and org) and their actual content is also a frustrating problem. For example, many .com sites contain noncommercial content, although com is ostensibly the main commercial top-level domain. Indeed, a skeptical researcher could claim that obstacles of this kind are so great that all Web analyses lack value. As will be seen, one response to this view, a view shared by critics of evaluative bibliometrics, is to demonstrate that Web data correlate significantly with some non-Web data in order to prove that the Web data are not wholly random. A practical response has been to develop increasingly sophisticated data cleansing techniques and multiple data analysis methods.
    Type
    a