Leveraging data to demonstrate program effectiveness, inform decision making, and support program implementation is an ongoing need for social and human service organizations, and is especially true in early childhood service settings. Unfortunately, early childhood service organizations often lack capacity and processes for harnessing data to these ends. While existing literature suggests the Active Implementation Drivers Framework (AIF Drivers) provides a theoretical basis for data-driven decision-making (DDDM), there are no practical applications or measurement tools which support an understanding of readiness or capacity for DDDM in early childhood settings. This study sought to address this gap through the development and initial validation of the Data-Driven Decision-Making Questionnaire (DDDM-Q) based on the nine core factors in the AIF Drivers. The study piloted the 54-item questionnaire with 173 early childhood program administrators. Findings from this study suggest using the AIF Drivers as a theoretical basis for examining DDDM supports three of five categories of validity evidence proposed by Goodwin (2002), including (1) evidence based on test content, (2) evidence based on internal structure, and (3) evidence based on relationships to other variables. This study may inform future research seeking to develop theoretically based instruments, particularly as it pertains to expanding use of the AIF Drivers. Practice-wise, the study findings could enhance and complement early childhood programs as well as other social and humans service implementations by presenting the DDDM-Q as a platform for understanding organizational readiness for DDDM and identifying strengths as well as areas for improvement.
Child poverty, child maltreatment, and child health and development are major public policy issues. By the end of the Great Recession in 2009, as many as one in four children under the age of five were living in poverty and six million children were subject to child maltreatment reports in America. For decades, evidence-based home visiting (EBHV) programs have provided effective early interventions for preventing child maltreatment and promoting child and family outcomes to pregnant women and families with children age birth to five years old. Despite this, no widespread
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.