April 16, 2019
Directed acyclic graphs (DAGs) are useful tools when studying research questions related to child maltreatment, as they allow researchers to graphically depict relationships among variables and ensure strong results for informing prevention and intervention strategies.
Anna Austin, MPH, a doctoral candidate in the Department of Maternal and Child Health at the UNC Gillings School of Global Public Health, is lead author of the paper, “Directed acyclic graphs: An under-utilized tool for child maltreatment research,” published in the May 2019 issue of Child Abuse & Neglect. Other Gillings School authors on the paper are Tania Desrosiers, PhD, assistant professor in the Department of Epidemiology, and Meghan Shanahan, PhD, assistant professor in the Department of Maternal and Child Health.
“For child maltreatment researchers, DAGs are a way to visually represent a research question and all of the variables that are important to consider when studying this question,” Austin said. “They are a quick way to communicate the assumptions you are making about relationships among variables, and they help researchers to select the most appropriate analytic strategy.”
DAGs, which are commonly used in epidemiology, can be hand-drawn or modeled in various computer-based programs. They can depict many different kinds of information and help researchers visually display assumptions about the relationship between variables. Austin’s paper serves as a practical and accessible resource for child maltreatment and other maternal and child health researchers to learn about DAGs because it offers a variety of concrete examples and specific scenarios in which DAGs can be particularly useful.
Austin says disentangling complex relationships among multiple variables is a common challenge in child maltreatment research, and doing it well is critical to identifying targets for evidence-based prevention and intervention.
“In the child maltreatment field, we are interested in understanding the causes and consequences of experiencing childhood abuse and neglect in order to inform our prevention and intervention strategies,” Austin explained. “In doing this research, we often have to model multiple interrelated variables and the complex relationships between them. DAGs help us select appropriate analytic strategies that identify and minimize bias and enhance the interpretability of results for translation to evidence-based practice.”
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