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!

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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.

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Why Is Data Quality Important?

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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

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Individual Programs: Where Data Quality Begins

• 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

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Role of the Part D Coordinator

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.

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Method # 1: Use the Data!!!

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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

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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).

Reward Provide Control

Belong Compare Learn Punish

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

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Method #3: Prioritize

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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

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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 Specifications14

NDTAC: Tools for Proactive TA

• EDFacts summary reports

(reviewing)

• Reviewing handout

(reviewing and prioritizing)

• Data quality reports

(motivating)

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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

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Data Quality Activity

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Activity Instructions

This activity has four handouts—each group will beresponsible 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 spokespersonfor your group.

• After 10–15 minutes, we will ask you to share and walk through the worksheets, answers, and suggestions as a group.

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Calculating Average Length of Stay

Facility Average Length of Stay (in days)Alligator Correctional School 100Cajun Central School 350Magnolia Academy 50

Total Sum at SA Level 500Average (total / 3) 167 days

Regular Average

Weighted Average

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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)

45 38

Students who took BOTH a pretest and a posttest in reading

33 25

Students who took only a posttest as they were leaving (no prettest data available)

25 12

Students without either a pretest or a posttest (no data)

10 5

Total 113 80

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: 38+25 = 63 students with data available

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Age-Eligibility

Indicators Outcome-Specific Age Ranges

Calculation(# achieving outcome/

# of age-eligible students)

Final Percent

Outcome measures calculated by ED for your StateHigh schoolcourse credits 13–21 years old

61 students earning outcome/ 82 age-eligible students 74%

Obtained employment 14–21 years old

82 students with outcome/ 77 age-eligible students

106%