Using Data To Guide Continuous Improvement: Data Analysis OSEP National Early Childhood Conference February 7, 2005 Accountability and System Improvement Work Group

Purpose of the Workshop To provide states and lead agencies with an experience in systematically analyzing data from multiple sources, in order to make informed decisions in identifying strategies for improving outcomes for infants, toddlers, preschoolers and their families and compliance.

Outcomes for Participants Gain a deeper understanding of the role of data analysis in continuous improvement planning Learn how to turn data into meaningful information that can be used to improve services and results for infants, toddlers, and preschoolers with disabilities and their families

OSEP’s Accountability Strategy High Risk Low Performance Continuous Improvement Verification Focused Intervention

OSEP’s Accountability Strategy System Verification Continuous Improvement High Risk Focused Monitoring Inquiry Level I: Information Review Level II: SEA/LA State Visit Level III: SEA/LA and Local Level State Visit Intervention Technical Assistance Revision to Annual Performance Report Required Targeted Corrective Action Plan Sanctions

Merging Reporting Requirements Monitoring Priorities and Timelines (Clusters, Probes) Targets Improvement Activities Timelines, Resources State Performance Plan APR Performance on Targets Explanation of Progress and Slippage Revisions to Activities, Timelines, Resources and Targets Annual Performance Report Context for Ruth: Do we need to go back that far? People have seen this before and it may be a little dated.

Elements of a Continuous Improvement Process State Performance Planning Self-Assessing Improvement Planning Data Analysis Target Setting Future Activities/Strategies Improvement Plan Implementation Reporting-Annual Performance Report Emphasize the cyclical nature of this process and the nonlinear nature of it. Continual sharpening of the PG & Indicators. Keep; needs revisions?

Data Analysis:

Data Analysis Compare present levels of system performance to baseline and targets to formulate educated “guesses” (hypotheses), to identify strengths and weaknesses, and to determine areas for improvement through a systematic examination of performance data.

Why is systematic data analysis important? Check Assumptions Target Limited Resources Set High Expectations Heighten Accountability Focus on Continuous Improvement Examine Results

Data analysis & use - considerations Get ready – what do you know already? Identify areas for comparison. Examine trends and relationships. Identify and define (possible) problem areas. Review and evaluate data to determine hypotheses. Develop potential improvement strategies. Evaluate – how did the interventions work? What are alternate hypotheses?

EXERCISE Part 1 – Indicators & Measures Part 2 – Baseline/Trend Data Part 3 – Analysis Part 4 – Future Targets

Wrap-up

Why is this important? Focus improvement efforts Target scarce resources Improve results for children with disabilities

Thanks to the Accountability and System Improvement Work Group Especially—Marsha Brauen, Lynne Kahn, Jane Nell Luster, Kristen Reedy, Jim Henson, and Dick Zeller and OSEP Staff—Larry Ringer, Rex Shipp, Rhonda Spence, and Maral Taylor