Academic journal article Child Welfare

Using Qualitative Data-Mining for Practice Research in Child Welfare

Academic journal article Child Welfare

Using Qualitative Data-Mining for Practice Research in Child Welfare

Article excerpt

In their daily practice, social service professionals routinely collect and record large quantities of data about client characteristics, practice interventions, and client outcomes (Epstein, 2002, 2009). While documentation of service activities are not new to child welfare (CW), over the last 30 years, federal legislation, including the Adoption Assistance and Child Welfare Act (P.L. 96-272) and the Adoption and Safe Families Act (P.L. 105-89), has promoted increased documentation in CW. Consequently, administrative CW data has proliferated and administrative data systems (ADS) have made these data more accessible to researchers.

To date, the majority of studies using administrative CW data have focused on the quantitative categorical data stored in ADS (see Conn et al., 2013; Putnam-Hornstein & Needell, 2011). Quantitative data help researchers and CW administrators identify rates of reported and substantiated child maltreatment, detect corresponding risk factors, or categorize service responses. The mining of these data teaches us about the kinds of maltreatment, placements, and services children referred to CW systems experience; identifies the frequency of these experiences; and can be used to make predictions about which children will return home and which will remain in care. However, these quantitative data tell us little about how CW workers define maltreatment, why children referred to CW systems are placed in specific settings, or how children and families engage in services. These latter questions are better answered through the mining and analysis of qualitative data stored in ADS.

Qualitative Data-Mining (QDM), the mining of the narrative text contained in documents stored in ADS (e.g., risk assessments, investigative narratives, court reports, and contact notes), provides CW researchers with a unique opportunity to use existing data to examine CW practice (Epstein, 2002, 2009). Use of QDM to improve CW has received limited attention (Epstein, 2002; Tice, 1998), as few CW studies have focused on the qualitative data stored in CW ADS or described how qualitative data is used by CW researchers (for exceptions see Coohey, 2007; Cordero, 2004; Cross, Koh, Rolock, & Eblen-Manning, 2013; Henry, 2014). This paper seeks to fill this gap by describing how researchers can use QDM techniques to create rich databases for qualitative CW research and answer unique questions about CW clients and practice. In a seven-step guide, the paper summarizes QDM strategies and methods, and reports on the work of the Child Welfare Qualitative Data-Mining (CWQDM) Project to illustrate these methods and strategies. The paper concludes with a discussion of how QDM can be used to enhance CW practice, research, and education.

Project Background

The CWQDM Project developed in the context of a longstanding practice- research partnership between a university-based research center and a regional social services consortium involving the directors of 11 county social service agencies, the deans and directors of four graduate social work programs, and executive staff representing a local foundation (Austin et al., 1999). The CWQDM Project was designed in response to agency interests in developing their capacity to engage in QDM in CW. One county agency agreed to participate as the pilot site for the project. With our agency partner, the CWQDM Project sought to (1) create a CW database that could be used to examine CW practice, client needs, and emerging issues in the field; and (2) develop QDM techniques that could be replicated by CW agencies and research partners.

In the next section, we describe the specific actions and processes that we developed to carry out the CWQDM Project and, in seven steps, outline how CW researchers can use QDM to create retrospective databases for practice research. The description of each step includes a summary of major lessons learned, and the relevant literature is discussed throughout. …

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