JOURNAL ARTICLE

Research methods and the use of visual representation in library and information science research.

  • Published In: Journal of the Association for Information Science & Technology, 2025, v. 76, n. 3. P. 527 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Matusiak, Krystyna K.; Osinska, Veslava; Organisciak, Peter; Thomas Pitts, Robyn 3 of 3

Abstract

The increasing variety of research strategies and data collection techniques in information science, the access to large secondary data sets, and the ubiquity of information visualization call for expanding the classification of research methods and exploring how research is communicated visually. This study examined the relationship between types of data used in empirical research, visualizations, and research methods applied in information science studies. It analyzed 751 research articles published in the Journal of the Association for Information Science and Technology (JASIST) using content analysis and machine learning techniques. The study finds that most empirical studies adopted a quantitative design with data mining, bibliometrics, experiments, and surveys as dominant strategies. The substantial use of secondary data points to the shift in how data are collected in empirical research. The JASIST articles used a variety of visualizations to present research designs and findings, with quantitative and mixed methods studies employing primarily tables and charts and qualitative studies relying more on tables and diagrams. This study uniquely explores the relationship between research methods and visualization. It contributes to the classification of the methods in information science by expanding the range of strategies within the quantitative, qualitative, and mixed methods designs. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of the Association for Information Science & Technology. 2025/03, Vol. 76, Issue 3, p527
  • Document Type:Article
  • Subject Area:Library and Information Science
  • Publication Date:2025
  • ISSN:2330-1635
  • DOI:10.1002/asi.24945
  • Accession Number:183854326
  • Copyright Statement:Copyright of Journal of the Association for Information Science & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.