Taking the Fast Lane to High-Quality Data Sarah Bardack and Stephanie Lampron

Provide an overview of the importance of data quality. Discuss the role of coordinators in relation to data quality. Present ways of approaching processes efficiently so that you are on the fast lane to data quality! 2 Session Goals

You need to TRUST your data as it informs: – Data-driven decisionmaking – Technical assistance (TA) needs – Federal budget justifications Furthermore, students deserve to have their accomplishments accurately demonstrated. 3 Why Is Data Quality Important?

4 What Is “high data quality”? If data quality is high, the data can be used in the manner intended because they are:  Accurate  Consistent  Unbiased  Understandable  Transparent

5 Individual Programs: Where Data Quality Begins Individual Programs SA or LEA SEA USED If data quality is not a priority at the local level, the problems become harder to identify as the data are rolled up— problems can become hidden. If data issues are recognized late in the process, it is more difficult (and less cost-effective) to identify where the issues are and rectify them in time.

Ultimately, coordinators cannot “make” the data be of high quality, but you can implement systems that make it a good possibility:  Understand the collection process.  Provide TA in advance.  Develop relationships.  Develop multilevel verification processes.  Track problems over time.  Use the data. 6 Role of the Part D Coordinator

7 Don’t give up—it does not have to happen all at once, and there are several ways to make the process more efficient…

1.The fastest way to motivate for data quality  Use the data programs provide. 2.The best way to increase data quality  Promote usage at the local level. 8 Method # 1: Use the Data!!!

9 Should you use data that has lower quality data? YES!! You can use these data to… Become familiar with the data and readily ID problems Know when the data are ready to be used or how they can be used Incentivize and motivate others

10 Method #2: Incentivize and Motivate 1.Know who is involved in the process and their roles. 2.Identify what is important to you and your data coordinators. 3.Select motivational strategies that align with your priorities (and ideally encourage teamwork). RewardProvide Control BelongCompareLearnPunish Provide bonus/ incentives for good data quality (individual or team level) Set goals, but allow freedom of how to get there Communicate vision and goals at all levels Publish rankings, and make data visible (to individuals or to everyone) Provide training and tools on data quality and data usage Withhold funding

Consider targeting only: Top problem areas among all subgrantees Most crucial data for the State Struggling programs 11 Method #3: Prioritize

12 Method #4: Know the Data Quality Pitfalls Recognize and respond proactively to the things that can hinder progress: Changes to indicators Staff turnover Funding availability

13 Method #5: Renew, Reuse, Recycle Develop materials upfront Look to existing resources and make them your own Where to look: NDTAC ED Your ND community The Web

1.Consolidated State Performance Report (CSPR) Guide Text resources Sample CSPR tables, indepth instructions, and data quality checklists Visual tools for walking through the more difficult aspects of the CSPR 2.CSPR Frequently Asked Questions 3.EDFacts File Specifications 14 NDTAC: Tools for Proactive TA

EDFacts summary reports (reviewing) Reviewing handout (reviewing and prioritizing) Data quality reports (motivating) 15 Tools for Reviewing Data and Motivating Providers

Activity: Understanding Common Data Problems and Thinking About Future Technical Assistance

The goal of this activity is to: Review common data quality issues Walk through scenarios and calculations so that you have a better understanding of the issues and can communicate them to subgrantees Help you think about ways to provide TA and display data quality information 17 Data Quality Activity

18 Activity Instructions This activity has four handouts—each group will be responsible for one. Organize yourselves in groups of two or three, and work through the problems or scenarios on your handout. Elect someone to be a spokesperson for your group. After 10–15 minutes, we will ask you to share and walk through the worksheets, answers, and suggestions as a group.

19 Calculating Average Length of Stay FacilityAverage Length of Stay (in days) Alligator Correctional School100 Cajun Central School350 Magnolia Academy50 Total Sum at SA Level 500 Average (total / 3) 167 days Regular Average Weighted Average Facility Number of Students (Duplicated Count) Average Length of Stay (in days) Weighted Average Length of Stay (students X stay) Alligator Correctional School ,500 Cajun Central School103503,500 Magnolia Academy Total Sum for at SA Level536,900 Total Weighted Average at SA Level (Student x Stay/Total Students 6,900/53 = 130 days

20 Calculating the Below-Grade-Level Indicator Type of Data Number of Students With Data Number of Long-Term Students With Data Students who took only a pretest in reading (no posttest) 4538 Students who took BOTH a pretest and a posttest in reading 3325 Students who took only a posttest as they were leaving (no prettest data available) 2512 Students without either a pretest or a posttest (no data) 105 Total11380 If you wanted to determine how many LONG-TERM students tested BELOW grade level when they entered the facility, how many students would have data available for you to use? Number of students: = 63 students with data available

21 Age-Eligibility Indicators Outcome-Specific Age Ranges Calculation (# achieving outcome/ # of age-eligible students) Final Percent Outcome measures calculated by ED for your State High school course credits13–21 years old 61 students earning outcome/ 82 age-eligible students74% Obtained employment14–21 years old 82 students with outcome/ 77 age-eligible students 106%