Implementation Drivers as Practical Measures of Data-Driven Decision-Making: An Initial Validation Study in Early Childhood Programs

Abstract

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.