Tag Archives: Data Use

DaSy Framework: Self-Assessment Tool

Self Assessment Tile

The Self-Assessment for the DaSy Framework is an Excel-based tool that provides a structure for state Part C and Section 619/Preschool programs to record the current status of their state system and set priorities for improvement, and track progress over time.

SPP/APR Basics, What You Need to Know

The "SPP/APR Basics, What You Need to Know" training series is a collection of self-directed modules that provide a basic understanding of the IDEA Part C and Part B 619 State Performance Plan/Annual Performance Report (SPP/APR) indicators and their requirements.

No Longer Invisible: Addressing Equity Through Data Use

Conference: STATS-DC Data Conference Date: August 17, 2021 Presenters: Grace Kelley and Cindy Weigel Equity issues exist in multiple places throughout early childhood special education service delivery from pre-referral though transition – but they will remain invisible unless committed leaders identify and address equity issues. Program leaders need to understand data are perceived differently by […]

Data Meeting Toolkit

This toolkit is a suite of tools that groups can use to guide conversation around data and support databased decisionmaking. The toolkit provides resources to support success before, during, and after data meetings. Data Meeting Toolkit

Data Culture Toolkit

Data Culture Tile

The DaSy toolkit is a resource containing information, guidance, and templates to help Part C and Part B 619 program staff build effective data teams and support conditions for a culture of data use at the state and local levels. The toolkit is organized around key steps to building a culture of data use in your state or local team. Each step includes an overview, considerations and data team resources (e.g., videos, infographics, templates, and blogs), and tips for how to use resources included in each section. As agencies use the data culture tools contained here, they will be able to 1) increase the participation and focus of their data teams and 2) review and make improvements to data quality and the processes used to gather, monitor, analyze, and use data.