Using Data To Guide Continuous Improvement Data Analysis

Using Data To Guide Continuous Improvement: Data Analysis OSEP National Early Childhood Conference February 7, 2005 Accountability and System Improvement Work Group 1

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. 2

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 3

OSEP’s Accountability Strategy High Low Risk Performance 4

OSEP’s Accountability Strategy System Continuous Verification 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 5

Merging Reporting Requirements State Performance Plan • Monitoring Priorities and Timelines (Clusters, Probes) • Targets • Improvement Activities • Timelines, Resources APR Annual Performance Report • Performance on Targets • Explanation of Progress and Slippage • Revisions to Activities, Timelines, Resources and Targets 6

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 Keep; needs revisions? 7

Data Analysis: 8

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. 9

Why is systematic data analysis important? • • • Check Assumptions Target Limited Resources Set High Expectations Heighten Accountability Focus on Continuous Improvement Examine Results 10

Data analysis & use - considerations Ø Ø Ø 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. Ø Develop potential improvement strategies. Ø Evaluate – how did the interventions work? Ø What are alternate hypotheses? 11

EXERCISE Part 1 2 3 4 – – Indicators & Measures Baseline/Trend Data Analysis Future Targets 12

Wrap-up 13

Why is this important? • Focus improvement efforts • Target scarce resources • Improve results for children with disabilities 14

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 15