Laying the Foundations for Scientometric Research: A Data Science Approach

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

    Research output: Contribution to journalArticlepeer-review

    20 Scopus citations


    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 (US)
    Pages (from-to)802-812
    Number of pages11
    JournalResearch on Social Work Practice
    Issue number7
    StatePublished - Nov 1 2017


    • bibliometrics
    • data science
    • database
    • scientometrics
    • social work history

    ASJC Scopus subject areas

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


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