Transcript Taking the Fast Lane to High

Reporting & Evaluation Workshop
Lauren Amos, Liann Seiter, and Dory Seidel
Workshop Objectives
• Learn how to use a data dashboard to support your
Title I, Part D, reporting and evaluation
responsibilities.
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Agenda: Part I
1. Presentation: Data Quality Overview and Common
Issues
2. Dashboard Demonstration: Exploring National CSPR
Data With the NDTAC Dashboard
3. Activity: Use the Dashboard To Conduct a CSPR Data
Quality Review of Your State Data
4. Small-Group Discussion
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Agenda: Part II
1. Dashboard Demonstration: GPRA Measures Used To
Inform Federal Decisionmaking
2. Activity: Use the Dashboard To Drive Decisionmaking
With Your State Data
3. Whole-Group Discussion
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Presentation
Data Quality Overview and
Common Issues
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Why Is Data Quality Important?
You need to trust your data because it informs:
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Data-driven decision-making
Technical assistance (TA) needs
Subgrantee monitoring
Application reviews
Program evaluation
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What Makes “High Quality Data”?
• Accuracy
• Consistency
• Unbiased
• Understandable
• Transparency
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Individual Programs: Where Data Quality Begins
Individual
Programs
SA or LEA
SEA
ED
• 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.
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Role of the Part D Coordinator
Ultimately, coordinators cannot “make” the data be of
high quality, but they can implement systems that
make it likely:
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Understand the collection process.
Provide TA in advance.
Develop relationships.
Develop multilevel verification processes.
Track problems over time.
Use the data.
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Method # 1: Use the Data!!!
The fastest way to motivate for data
quality is to use the data that programs
provide.
The best way to increase data quality is
to promote usage at the local level.
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Should you use data that has data quality problems?
YES!!
You can use these data to…
• Become familiar with the data and readily identify
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
Provide bonus/
incentives for
good data
quality
Set goals, but
allow freedom
for how to get
there
(individual or
team level)
Belong
Communicate
vision and goals
at all levels
Compare
Publish
rankings, and
make data
visible
Learn
Punish
Provide training Withhold
and tools on
funding
data quality
and data usage
(to individuals or
to everyone)
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Method #3: Prioritize
Consider targeting only:
• Top problem areas among all subgrantees
• Most crucial data for the State
• Struggling programs
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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
• Changes to submission
processes
• 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
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NDTAC: Tools for Proactive TA
• Consolidated State Performance Report (CSPR)
Guide
– Sample CSPR tables
– In-depth instructions
– Data quality checklists
• Data Collection List
• CSPR Tools and Other Resources
• EDFacts File Specifications
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Tools for Reviewing Data and Motivating Providers
• EDFacts summary reports
– Review the quality of data entered via
EDFacts
• ED Data Express
– Compare data at the SA and LEA level to the
nation or similar states
• Data Dashboard
– Conduct data quality reviews or performance
evaluation
– Make data driven decisions
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Common CSPR Data Quality Issues
• Academic and vocational outcomes are over the age-eligible ranges.
• The sum of students by race, age, or gender does not equal the
unduplicated count of students.
• The number of long-term students testing below grade level is more
than the number of long-term students for reading and math.
• The number of students demonstrating results (sum of rows 3–7)
does not equal the number of students with complete pre- and
posttests (row 2) for reading and math.
• Average length of stay is 0 or more than 365 days.
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Dashboard Demonstration
Exploring National CSPR Data
with the NDTAC Dashboard
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Activity
Use the Dashboard To Conduct a
CSPR Data Quality Review of
Your State Data
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Activity: CSPR Data Quality Review
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Strategies for Improving Data Quality
• Technical assistance: newsletters, webinars, phone
calls, one-pagers
• Monitoring: on-site verification (e.g., document
reviews, demo data collection process/system)
• Interim data collection and QC reviews
• Data collection templates
• Annual CSPR data show
• Other ideas?
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Whole-Group Discussion: Data Quality
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Is your current data collection tool sound?
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Do your subgrantees know how to use your data
collection tool?
− How do you know?
− What TA do you provide to support their use of the
tool?
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Do your facilities have a data collection system, process,
or tool that allows them to track student-level data?
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How do your current subgrantee monitoring activities
support CSPR data quality?
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Dashboard Demonstration
GPRA Measures Used
To Inform Federal
Decisionmaking
(or the Federal Perspective on
State Performance)
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Activity: GPRA Measures
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Guiding Questions
1. GRPA Indicators
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In what areas is your state underperforming relative to the
nation?
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What contextual factors may explain why?
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What questions do you have about your state’s
performance in this area?
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Is this cause for concern?
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If so, what are your next steps?
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Activity
Use the Dashboard To Drive
Decisionmaking With Your State
Data
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Activity: Decisionmaking
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Guiding Questions
2. Program Performance
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Are certain demographic groups overrepresented in a particular program
and if so, are those programs taking measures to serve the unique needs
of those populations?
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Are all students being served equally well or are certain program types
outperforming (or underperforming) their peers?
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Which program type(s) appear to be effective? Why might it/they appear
to outperform the other program types (e.g., higher student performance
upon entry, more highly qualified teachers, smaller student-teacher ratio,
low teacher attrition)?
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What questions do you have about your state’s performance in this area?
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Any causes for concern?
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If so, what are your next steps?
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Whole-Group Discussion
1. Did these activities inspire any surprises for? Any major takeaways?
2. Are your subgrantees meeting the needs of your State’s youth
who are neglected and delinquent?
3. Are youth needs being consistently met across program
types?
4. What did the dashboard activity suggest about how well TIPD
funds are being used in your State?
5. Are the funds you’ve allocated to your subgrantees reflected
in program outcomes?
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Whole-Group Discussion (cont.)
1. "It's Complicated": What's your Facebook status with
your EDFacts Coordinator?
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What might you do to forge better ties with your
State data person?
2. What are your next steps?
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What might you do differently moving forward to
improve data quality and data-driven decisionmaking
in your State?
3. How can NDTAC support you in these next steps?
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