Using Qualitative Data-Mining for Practice Research in Child Welfare

Qualitative data-mining (QDM), using the narrative data contained in child welfare case records, enables researchers to examine child welfare practice using relatively non-intrusive methods. QDM can increase our understanding of client populations and problems, child welfare worker actions, and case...

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Bibliographic Details
Published in:Child welfare 2014-01, Vol.93 (6), p.7-26
Main Authors: Henry, Colleen, Carnochan, Sarah, Austin, Michael J.
Format: Article
Language:English
Subjects:
Academic Achievement
Adoption
Case Records
Child Abuse
Child Welfare
Children
Client Characteristics (Human Services)
Confidentiality
Consortia
Content analysis
Data mining
Expertise
Families & family life
Federal Legislation
Foster care
Organizational change
Practice research
Qualitative Research
Research Needs
Researchers
Resource Allocation
Social Services
Social work
Teaching Methods
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Summary:Qualitative data-mining (QDM), using the narrative data contained in child welfare case records, enables researchers to examine child welfare practice using relatively non-intrusive methods. QDM can increase our understanding of client populations and problems, child welfare worker actions, and case complexity. This paper reports on experiences from the Child Welfare Qualitative Data-Mining Project; outlines a seven-step guide to QDM methods; and describes how QDM can be used to enhance child welfare practice, research, and education.
ISSN:0009-4021