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dc.contributor.advisorAkin, Becci
dc.contributor.authorBarton, Jared Lee
dc.date.accessioned2019-10-14T16:49:07Z
dc.date.available2019-10-14T16:49:07Z
dc.date.issued2019-08-31
dc.date.submitted2019
dc.identifier.otherhttp://dissertations.umi.com/ku:16799
dc.identifier.urihttp://hdl.handle.net/1808/29622
dc.description.abstractHarnessing the use of data to demonstrate program effectiveness, establish lines of accountability, and implement evidence-based programs is a present demand of social welfare and human service organizations. Early childhood service organizations, in particular, face requirements to use data to support decision-making, while having little research that offers best practices for data use in early childhood and limited programmatic capacity to collect and process data in ways that enhance decision-making. While literature promotes utilizing the Active Implementation Drivers Framework (AIF Drivers) as a theoretically-based strategy for data-driven decision-making (DDDM), there has yet to be an application of this idea in early childhood practice. To this end, this study sought to increase understanding of how early childhood programs use data and what factors drive program readiness for DDDM. The study involved the development and initial validation of the Early Childhood Data-Driven Decision-Making (EC-DDDM) survey based on the nine core AIF Drivers. Three key questions were posed: 1) How do early childhood program administrators rate their organizations’ readiness for DDDM? 2) Is the AIF Drivers an effective guide for understanding organizational readiness for DDDM? 3) How are demographic characteristics of program administrators and characteristics of early child programs related to factors of readiness for data-driven decision-making? To answer these questions, 173 early childhood program administrators responded to the EC-DDDM. Findings from this study inform understanding of early childhood programs’ data use and readiness for DDDM in three ways. First, the study provided a deeper and theoretically-grounded description of program administrators’ perspectives on data use. Second, through confirmatory factor analysis and an evaluation of EC-DDDM based on Goodwin’s (2002) measurement validity recommendations, it established initial evidence supporting the validity of the EC-DDDM and confirming the AIF Drivers as a fitting underlying factor structure for understanding readiness for DDDM. And third, the study found no evidence of relationships between administrator demographics and program characteristics and readiness for DDDM. These findings may inform future research attempts to develop theoretically-based measurement tools, especially as they pertain to developments that apply the AIF Drivers. Moreover, findings may advance early childhood practice as the EC-DDDM could serve as a platform for early childhood programs to understand their own readiness for DDDM and identify areas of strength or opportunities for improvement within their own practice. Future research is needed to accumulate validity evidence for the EC-DDDM and to understand the patterns and relationships between the nine AIF Drivers as well as what other external variables influence DDDM.
dc.format.extent183 pages
dc.language.isoen
dc.publisherUniversity of Kansas
dc.rightsCopyright held by the author.
dc.subjectSocial work
dc.subjectData Driven Decision Making
dc.subjectEarly Childhood
dc.subjectEvidence
dc.subjectImplementation Drivers
dc.subjectImplementation Science
dc.subjectSocial and human services
dc.titleDevelopment and Initial Validation of a Measure for Early Childhood Program Readiness for Data Driven Decision
dc.typeDissertation
dc.contributor.cmtememberAkin, Becci
dc.contributor.cmtememberMendenhall, Amy
dc.contributor.cmtememberCarlson, Juliana
dc.contributor.cmtememberLieberman, Alice
dc.contributor.cmtememberBranscombe, Nyla
dc.thesis.degreeDisciplineSocial Welfare
dc.thesis.degreeLevelPh.D.
dc.identifier.orcidhttps://orcid.org/0000-0002-7387-9680
dc.rights.accessrightsopenAccess


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