It's not as simple as it sounds: Problems and solutions in accessing and using administrative child welfare data for evaluating the impact of early childhood interventions

Green, Beth L.; Ayoub, Catherine; Bartlett, Jessica Dym; Furrer, Carrie; Ende, Adam Von; Chazan‐Cohen, Rachel; Klevens, Joanne; Nygren, Peggy

doi:10.1016/j.childyouth.2015.07.015

Cited by 32 publications

(39 citation statements)

References 30 publications

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“…Numerous problems exist in using child welfare administrative data sets to address research questions about the effectiveness of intervention programs (Chaffin et al, 2012; Chamberlain et al, 2016; Green et al, 2015, Lucenko et al, 2015). For example, official records may misrepresent the actual incidence of abuse and neglect.…”

Section: Discussionmentioning

confidence: 99%

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Summary of comparison between FFT-CW ® and Usual Care sample from Administration for Children's Services

Turner

Robbins

Rowlands³

et al. 2017

Child Abuse & Neglect

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This evaluation compared the efficiency and effectiveness of Functional Family Therapy-Child Welfare (FFT-CW®, n=1625) to Usual Care (UC: n=2250) in reducing child maltreatment. FFT-CW® is a continuum of care model based on the family’s risk status. In a child welfare setting, families received either UC or FFT-CW® in a quasi-experimental, stepped wedge design across all five boroughs of New York City. The families were matched using stratified propensity scoring on their pre-service risk status and followed for 16 months. The ethnically diverse sample included African American (36%), Asian (4%); Hispanic (49%), and Non-Hispanic White (6%) or Other (6%) participants. Referral reasons included abuse or neglect (57.4%), child service needs (56.9%) or child health and safety concerns (42.8%). Clinical process variables included staff fidelity, service duration, and number of contacts. Positive outcomes included whether all clinical goals were met and negative outcomes included transfers, outplacement, recurring allegations and service participation within 16 months of the case open date. Families receiving FFT-CW® completed treatment more quickly than UC and they were significantly more likely to meet all of the planned service goals. Higher treatment fidelity was associated with more favorable outcomes. Fewer FFT-CW® families were transferred to another program at closing, and they had fewer recurring allegations. FFT-CW® had fewer out-of-home placements in families with higher levels of risk factors. The FFT-CW® program was more efficient in completing service, and more effective than UC in meeting treatment goals while also avoiding adverse outcomes.

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Section: Discussionmentioning

confidence: 99%

“…Prior investigations (e.g., Chaffin, Hecht, Bard, Silovsky, & Beasley, 2012; Chamberlain, Feldman, Wulczyn, Saldana, & Forgatch, 2016; Green et al 2015) have identified a number of pitfalls concerning the use of child welfare administrative sets. We have attempted to address these concerns in this report.…”

Section: Introductionmentioning

confidence: 99%

Summary of comparison between FFT-CW ® and Usual Care sample from Administration for Children's Services

Turner

Robbins

Rowlands³

et al. 2017

Child Abuse & Neglect

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“…This step is clearly essential to the process, yet can prove more convoluted than expected. What follows is a brief description of the major steps taken and lessons learned, as a more in-depth review of this process is available through Green et al (2015).…”

Section: The Spark Projectmentioning

confidence: 99%

“…Further, these files include the conclusions of these hotline calls, specifying whether a given report was investigated and if the allegation was ultimately substantiated. Previous research has supported the use of case file data and demonstrated the level of rigor that can be achieved in studies that utilize such records (Brownell & Jutte, 2013; Green et al, 2015). One argument that has been made is that case file records are not subject to the social desirability bias likely to be present in self-report of parenting behaviors (MacMillan, Jamieson, & Walsh, 2003), nor are they affected by forgetting, misremembering, or failure to disclose, all of which may be present in child self-report (Greenhoot, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Although numerous studies have used case file reports to measure maltreatment exposure (Fisher, Burraston, & Pears, 2005; Mennen, Kim, Sang, & Trickett, 2010; Merrick, Litrownik, Everson, & Cox, 2008; Taussig, Culhane, Garrido, Knudston, & Petrenko, 2013), and many have used comprehensive coding systems (e.g., Modified Maltreatment Classification System [MMCS; English & the LONGSCAN Investigators, 1997]), lack of information on how researchers code case file data, particularly when such data falls outside the scope of the coding system, prevents the field from comparing and interpreting results (Green et al, 2015). For example, a parent hitting a child would be coded as physical abuse within every coding system, but some coding systems do not clarify whether hitting a child in the arm multiple times should be coded as one instance of physical abuse or multiple.…”

Section: Introductionmentioning

confidence: 99%

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Case file coding of child maltreatment: Methods, challenges, and innovations in a longitudinal project of youth in foster care

Huffhines

Tunno

Cho

et al. 2016

Children and Youth Services Review

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State social service agency case files are a common mechanism for obtaining information about a child’s maltreatment history, yet these documents are often challenging for researchers to access, and then to process in a manner consistent with the requirements of social science research designs. Specifically, accessing and navigating case files is an extensive undertaking, and a task that many researchers have had to maneuver with little guidance. Even after the files are in hand and the research questions and relevant variables have been clarified, case file information about a child’s maltreatment exposure can be idiosyncratic, vague, inconsistent, and incomplete, making coding such information into useful variables for statistical analyses difficult. The Modified Maltreatment Classification System (MMCS) is a popular tool used to guide the process, and though comprehensive, this coding system cannot cover all idiosyncrasies found in case files. It is not clear from the literature how researchers implement this system while accounting for issues outside of the purview of the MMCS or that arise during MMCS use. Finally, a large yet reliable file coding team is essential to the process, however, the literature lacks training guidelines and methods for establishing reliability between coders. In an effort to move the field toward a common approach, the purpose of the present discussion is to detail the process used by one large-scale study of child maltreatment, the Studying Pathways to Adjustment and Resilience in Kids (SPARK) project, a longitudinal study of resilience in youth in foster care. The article addresses each phase of case file coding, from accessing case files, to identifying how to measure constructs of interest, to dealing with exceptions to the coding system, to coding variables reliably, to training large teams of coders and monitoring for fidelity. Implications for a comprehensive and efficient approach to case file coding are discussed.

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Mutual Information Scoring: Increasing Interpretability in Categorical Clustering Tasks with Applications to Child Welfare Data

Sankhe

Hall

Sage

et al. 2022

Lecture Notes in Computer Science

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Youth in the American foster care system are significantly more likely than their peers to face a number of negative life outcomes, from homelessness to incarceration. Administrative data on these youth have the potential to provide insights that can help identify ways to improve their path towards a better life. However, such data also suffer from a variety of biases, from missing data to reflections of systemic inequality. The present work proposes a novel, prescriptive approach to using these data to provide insights about both data biases and the systems and youth they track. Specifically, we develop a novel categorical clustering and cluster summarization methodology that allows us to gain insights into subtle biases in existing data on foster youth, and to provide insight into where further (often qualitative) research is needed to identify potential ways of assisting youth.

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It's not as simple as it sounds: Problems and solutions in accessing and using administrative child welfare data for evaluating the impact of early childhood interventions

Cited by 32 publications

References 30 publications

Summary of comparison between FFT-CW ® and Usual Care sample from Administration for Children's Services

Summary of comparison between FFT-CW ® and Usual Care sample from Administration for Children's Services

Case file coding of child maltreatment: Methods, challenges, and innovations in a longitudinal project of youth in foster care

Mutual Information Scoring: Increasing Interpretability in Categorical Clustering Tasks with Applications to Child Welfare Data

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