Confirmation of the Factor Structure of the Data Driven Decision-Making Efficacy and Anxiety Inventory (3D-MEA) Among Teachers in the Midwest

• In Event: 58.062 - Data-Driven Decision-Making Instrumentation and Methodology

Abstract

The implementation of data-driven decision making practices (DDDM) is a key component of contemporary teachers’ professional practice. As such, the measurement of DDDM and related constructs is important for multiple purposes in both research and practice (e.g., identifying teacher needs around DDDM, and monitoring teacher change in response to DDDM interventions). The present study examined the score factor structure and reliability of the Data Driven Decision-Making Efficacy and Anxiety Inventory (3D-MEA), an existing measure of data driven decision-making-related self-efficacy and anxiety. Prior work with this instrument has only provided validity evidence in the context of teachers from the Pacific Northwest. Confirmatory factor analysis of 3D-MEA scores from a sample of Midwestern teachers replicates the initially-hypothesized five-factor internal score structure.

Authors

• David A. Walker, Northern Illinois University

• Todd Reeves, Northern Illinois University

• Thomas J. Smith, Northern Illinois University