What teachers think about what they can do with data: Development and validation of the data driven decision-making efficacy and anxiety inventory

Classroom level data driven decision-making (DDDM) involves the use of data to identify patterns of performance that reveal students’ academic strengths and weaknesses relative to established learning goals, and the planning of instructional practices to support academic success for all students. Although DDDM is not a new paradigm in education, little is known about what variables facilitate teacher adoption of DDDM practices. The aim of this work was to introduce two such variables, DDDM efficacy and DDDM anxiety, and a measure of these constructs, the DDDM efficacy and anxiety (3D-MEA) inventory. The 1728 participants in this study were K-12 teachers who had experienced varying levels of DDDM professional development in a Pacific Northwestern state. Exploratory factor analysis (EFA) (n = 864) and confirmatory factor analysis (CFA) (n = 864) were utilized to evaluate the psychometric properties of the 3D-MEA Inventory. Results supported a five-factor model. A discussion of the results and their implications ensue.

► Presented new latent construct—teacher data driven decision-making (DDDM) efficacy. ► Developed and validated DDDM Efficacy Survey with 1728 teachers. ► Five-factor model supported by EFA, CFA, and discriminant validity results.