Transcript Education Data - University of Utah

Modeling Approaches to
Education Planning
[email protected]
www.schools.utah.gov/finance
URBPL 5020 / April 4, 2006
Where We Are & What We Do
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Utah State Office of Education
Data and Business Services Division
Finance and Statistics Section
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Accounting Standards & Financial Audits
Budget & Enrollment Projections
Data Management & Federal Reporting
Facilities & Safety
Property Tax
Transportation
What We Do (2)
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Administrative Rulemaking
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Board, Legislative, Training Presentations
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examples below
Guidance & Interpretations
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Utah Association of School Business Officials
Fiscal Impact Analysis
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R277-419 Pupil Accounting
No set of rules cover every contingency
Program Administration
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Necessarily Existent Small Schools
Data Management Cycle (1)
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DEFINE
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Objects (student)
Events (dropping out)
Attributes (race/ethnicity)
Classifications (homelessness)
COLLECT
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File format specifications (Data Clearinghouse — student,
course taking data)
Database applications (CACTUS — educator licensing and
assignment data)
Large scale surveys (CRT — state sponsored test
administration)
Data Management Cycle (2)
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VERIFY
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ORGANIZE
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Automated edit checks of data points
Automated and human review of summary statistics
Independent compliance auditing of local documentation
and practices
Normalized data warehouse to integrate operational data
sources
Summary tables and denormalized views of data
warehouse
USE
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Decision support
Exercises
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When is a student who leaves school before
graduating not a dropout?
Who decides the racial/ethnic identity of a
student?
If you live among the Navajo nation in a
hogan, are you homeless?
Kanab is on the “urban fringe” of this city.
Decision Support Continuum
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Stand alone mandated report
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Menu of prespecified reports
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OLAP data cubes with Cognos
Statistical analysis (in process)
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U-PASS / School Performance Report as interactive
website
Ad hoc reporting capability (limited)
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NCLB / Adequate Yearly Progress as pdf
Microdata sets for SPSS
Data mining (hypothetical)
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Cluster analysis for setting performance standards
District Size (2006 HB 77):
Methodology (USOE Fiscal Note)
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Models
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Cost function (multiple regression)
Differential equation (optimization)
What if scenario
Characteristics (Show your work!)
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Explicit statement of assumptions
Step by step presentation of calculation
Evidence to support validity of model
Tuition Tax Credit (2004 HB 271):
Politics (SL Tribune 2/23/04)
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Who(m) to believe?
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Columbia University professor’s critique: “I’d be happy to go
with the [USOE] analysis rather than the fiscal analyst’s,
which is opaque to the point of incomprehensibility”
Fiscal analyst’s defense: “Anybody’s guess is as good as
the next person’s”
Not speaking truth to power
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Opponents’ critique: “Foes have long accused the fiscal
analyst’s office of working the numbers to achieve a
favorable outcome”
Fiscal analyst’s concession: “At the outset, the intention is to
have it come out in a positive way so there’s not a cost”