Transcript Taking the Fast Lane to High

Taking the Fast Lane to High-Quality Data
Sarah Bardack and Stephanie Lampron
Session Goals
• 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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Why Is Data Quality Important?
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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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
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.
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Role of the Part D Coordinator
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.
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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…
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Method # 1: Use the Data!!!
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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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
Provide
bonus/
incentives for
good data
quality
Set goals, but
allow
freedom of
how to get
there
Communicate
vision and goals
at all levels
(individual or
team level)
Compare
Publish
rankings, and
make data
visible
Learn
Provide
training and
tools on data
quality and
data usage
Punish
Withhold
funding
(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
• 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
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
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Tools for Reviewing Data and Motivating
Providers
• EDFacts summary reports
(reviewing)
• Reviewing handout
(reviewing and prioritizing)
• Data quality reports
(motivating)
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Activity:
Understanding Common Data Problems and
Thinking About Future Technical Assistance
Data Quality Activity
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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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.
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Calculating Average Length of Stay
Regular Average
Facility
Average Length of Stay (in days)
Alligator Correctional School
Cajun Central School
Magnolia Academy
Total Sum at SA Level
100
350
50
500
167 days
Average (total / 3)
Weighted Average
Facility
Alligator Correctional
School
Cajun Central School
Magnolia Academy
Total Sum for at SA Level
Total Weighted Average
at SA Level
(Student x Stay/Total
Students
Number of Students
(Duplicated Count)
Average Length of
Stay (in days)
Weighted Average Length of
Stay (students X stay)
25
100
2,500
10
18
53
350
50
3,500
900
6,900
6,900/53 =
130 days
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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
113
80
Total
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 State
High school
61 students earning outcome/
course credits
13–21 years old
82 age-eligible students
74%
Obtained
employment
106%
14–21 years old
82 students with outcome/
77 age-eligible students
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