The use and misuse of citation analysis in research evaluation

1998 ◽  
Vol 43 (1) ◽  
pp. 27-43 ◽  
Author(s):  
R. N. Kostoff
Keyword(s):  
Citation Analysis ◽  
Research Evaluation

scholarly journals In - text citation function s of self ­ - citations: Implications for research evaluation practice

2018 ◽  
Author(s):  
Dangzhi Zhao ◽  
Alicia Cappello
Keyword(s):  
Citation Analysis ◽  
Research Evaluation ◽  
Evaluation Practice ◽  
Citation Function ◽  
Individual Self

Self-citations have long been noted as a problem in citation analysis and are often excluded from the analyses based on the notion that self-citations may be included for egoistic or self-serving reasons. The present study, however, found that self-citations are less likely to function as nonessential citations than foreign citations, suggesting that self-citations should not be discounted in citation analysis, and should in fact be given more weight than foreign citations in weighted citation analysis. This study fills a gap in research on self-citations by examining the function of individual self-citation occurrences inciting articles as compared to foreign citations.

Intellectual structure of information science 2011–2020: an author co-citation analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dangzhi Zhao ◽  
Andreas Strotmann
Keyword(s):  
Citation Analysis ◽  
Information Science ◽  
Research Evaluation ◽  
Information Behavior ◽  
Intellectual Structure ◽  
Knowledge Domain ◽  
Science Field ◽  
Content Type ◽  
History Of ◽  
Time Periods

PurposeThis study continues a long history of author co-citation analysis of the intellectual structure of information science into the time period of 2011–2020. It also examines changes in this structure from 2006–2010 through 2011–2015 to 2016–2020. Results will contribute to a better understanding of the information science research field.Design/methodology/approachThe well-established procedures and techniques for author co-citation analysis were followed. Full records of research articles in core information science journals published during 2011–2020 were retrieved and downloaded from the Web of Science database. About 150 most highly cited authors in each of the two five-year time periods were selected from this dataset to represent this field, and their co-citation counts were calculated. Each co-citation matrix was input into SPSS for factor analysis, and results were visualized in Pajek. Factors were interpreted as specialties and labeled upon an examination of articles written by authors who load primarily on each factor.FindingsThe two-camp structure of information science continued to be present clearly. Bibliometric indicators for research evaluation dominated the Knowledge Domain Analysis camp during both fivr-year time periods, whereas interactive information retrieval (IR) dominated the IR camp during 2011–2015 but shared dominance with information behavior during 2016–2020. Bridging between the two camps became increasingly weaker and was only provided by the scholarly communication specialty during 2016–2020. The IR systems specialty drifted further away from the IR camp. The information behavior specialty experienced a deep slump during 2011–2020 in its evolution process. Altmetrics grew to dominate the Webometrics specialty and brought it to a sharp increase during 2016–2020.Originality/valueAuthor co-citation analysis (ACA) is effective in revealing intellectual structures of research fields. Most related studies used term-based methods to identify individual research topics but did not examine the interrelationships between these topics or the overall structure of the field. The few studies that did discuss the overall structure paid little attention to the effect of changes to the source journals on the results. The present study does not have these problems and continues the long history of benchmark contributions to a better understanding of the information science field using ACA.

Data sources for performing citation analysis: an overview

2008 ◽  
Vol 64 (2) ◽  
pp. 193-210 ◽  
Author(s):  
Christoph Neuhaus ◽  
Hans‐Dieter Daniel
Keyword(s):  
Citation Analysis ◽  
Design Methodology ◽  
Research Evaluation ◽  
Data Sources ◽  
Google Scholar ◽  
Content Type ◽  
Citation Indexes ◽  
Data Source

PurposeThe purpose of this paper is to provide an overview of new citation‐enhanced databases and to identify issues to be considered when they are used as a data source for performing citation analysis.Design/methodology/approachThe paper reports the limitations of Thomson Scientific's citation indexes and reviews the characteristics of the citation‐enhanced databases Chemical Abstracts, Google Scholar and Scopus.FindingsThe study suggests that citation‐enhanced databases need to be examined carefully, with regard to both their potentialities and their limitations for citation analysis.Originality/valueThe paper presents a valuable overview of new citation‐enhanced databases in the context of research evaluation.

scholarly journals Functions of Uni- and Multi-citations: Implications for Weighted Citation Analysis

2017 ◽  
Vol 2 (1) ◽  
pp. 51-69 ◽  
Author(s):  
Dangzhi Zhao ◽  
Alicia Cappello ◽  
Lucinda Johnston
Keyword(s):  
Citation Analysis ◽  
Information Science ◽  
Research Evaluation ◽  
Error Rates ◽  
Knowledge Network ◽  
Low Degree ◽  
Significant Step ◽  
Degree Of Confidence ◽  
The Impact ◽  
First Time

AbstractPurpose(1) To test basic assumptions underlying frequency-weighted citation analysis: (a) Uni-citations correspond to citations that are nonessential to the citing papers; (b) The influence of a cited paper on the citing paper increases with the frequency with which it is cited in the citing paper. (2) To explore the degree to which citation location may be used to help identify nonessential citations.Design/methodology/approachEach of the in-text citations in all research articles published in Issue 1 of the Journal of the Association for Information Science and Technology (JASIST) 2016 was manually classified into one of these five categories: Applied, Contrastive, Supportive, Reviewed, and Perfunctory. The distributions of citations at different in-text frequencies and in different locations in the text by these functions were analyzed.FindingsFiltering out nonessential citations before assigning weight is important for frequency-weighted citation analysis. For this purpose, removing citations by location is more effective than re-citation analysis that simply removes uni-citations. Removing all citation occurrences in the Background and Literature Review sections and uni-citations in the Introduction section appears to provide a good balance between filtration and error rates.Research limitationsThis case study suffers from the limitation of scalability and generalizability. We took careful measures to reduce the impact of other limitations of the data collection approach used. Relying on the researcher’s judgment to attribute citation functions, this approach is unobtrusive but speculative, and can suffer from a low degree of confidence, thus creating reliability concerns.Practical implicationsWeighted citation analysis promises to improve citation analysis for research evaluation, knowledge network analysis, knowledge representation, and information retrieval. The present study showed the importance of filtering out nonessential citations before assigning weight in a weighted citation analysis, which may be a significant step forward to realizing these promises.Originality/valueWeighted citation analysis has long been proposed as a theoretical solution to the problem of citation analysis that treats all citations equally, and has attracted increasing research interest in recent years. The present study showed, for the first time, the importance of filtering out nonessential citations in weighted citation analysis, pointing research in this area in a new direction.

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