کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
523365 868341 2016 17 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Are the discretised lognormal and hooked power law distributions plausible for citation data?
ترجمه فارسی عنوان
آیا توزیع لگ نرمال گسسته و قانون توان قابل قبول برای استناد است؟
کلمات کلیدی
توزیع استناد؛ قانون توان . توزیع لگ نرمال گسسته
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی


• Neither the discretised lognormal nor the hooked power law are plausible for citation counts from a single Scopus category and year.
• Parameter variations in fitted citation distributions may be due to mixed sub-fields within single subject areas.
• Large sample sizes and simplifying assumptions are needed to test whether a distribution is mixed.

There is no agreement over which statistical distribution is most appropriate for modelling citation count data. This is important because if one distribution is accepted then the relative merits of different citation-based indicators, such as percentiles, arithmetic means and geometric means, can be more fully assessed. In response, this article investigates the plausibility of the discretised lognormal and hooked power law distributions for modelling the full range of citation counts, with an offset of 1. The citation counts from 23 Scopus subcategories were fitted to hooked power law and discretised lognormal distributions but both distributions failed a Kolmogorov–Smirnov goodness of fit test in over three quarters of cases. The discretised lognormal distribution also seems to have the wrong shape for citation distributions, with too few zeros and not enough medium values for all subjects. The cause of poor fits could be the impurity of the subject subcategories or the presence of interdisciplinary research. Although it is possible to test for subject subcategory purity indirectly through a goodness of fit test in theory with large enough sample sizes, it is probably not possible in practice. Hence it seems difficult to get conclusive evidence about the theoretically most appropriate statistical distribution.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Informetrics - Volume 10, Issue 2, May 2016, Pages 454–470
نویسندگان
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