Laying the Foundations for Scientometric Research: A Data Science Approach

Brian E. Perron, Bryan G. Victor, David R. Hodge, Christopher P. Salas-Wright, Michael G. Vaughn, Robert Joseph Taylor

Research output: Contribution to journalArticle

12 Citations (Scopus)


Objective: Scientometric studies of social work have stagnated due to problems with the organization and structure of the disciplinary literature. This study utilized data science to produce a set of research tools to overcome these methodological challenges. Method: We constructed a comprehensive list of social work journals for a 25-year time period and searched for all available article records using 35 different databases. Customized software was developed to restructure article records into a single analyzable database. We then computed the annual journal growth from the database. Results: A population of 90 disciplinary journals was established, and 33,330 article records were retrieved from 80 of these journals. Rapid and consistent growth in the number of social work journals was observed, particularly from 1997 up to 2005. Conclusions: The population list of social work journals, database of article records, and customized software builds the foundation for future scientometric studies in social work.

Original languageEnglish
Pages (from-to)802-812
Number of pages11
JournalResearch on Social Work Practice
Issue number7
Publication statusPublished - 2017 Nov 1

All Science Journal Classification (ASJC) codes

  • Social Sciences (miscellaneous)
  • Sociology and Political Science
  • Psychology(all)

Fingerprint Dive into the research topics of 'Laying the Foundations for Scientometric Research: A Data Science Approach'. Together they form a unique fingerprint.

  • Cite this

    Perron, B. E., Victor, B. G., Hodge, D. R., Salas-Wright, C. P., Vaughn, M. G., & Taylor, R. J. (2017). Laying the Foundations for Scientometric Research: A Data Science Approach. Research on Social Work Practice, 27(7), 802-812.