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

2. 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.

3. 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

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

5. 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

6. 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.

7. 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?

8. Data Analysis:

9. 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.

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

11. 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?

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

13. Wrap-up

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

15. 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