Modeling Approaches to Education Planning

1. Modeling Approaches to Education Planning randy.raphael@schools.utah.gov www.schools.utah.gov/finance URBPL 5020 / April 4, 2006

2. Where We Are & What We Do • Utah State Office of Education • Data and Business Services Division • Finance and Statistics Section • Accounting Standards & Financial Audits • Budget & Enrollment Projections • Data Management & Federal Reporting • Facilities & Safety • Property Tax • Transportation

3. What We Do (2) • Administrative Rulemaking • R277-419 Pupil Accounting • Board, Legislative, Training Presentations • Utah Association of School Business Officials • Fiscal Impact Analysis • examples below • Guidance & Interpretations • No set of rules cover every contingency • Program Administration • Necessarily Existent Small Schools

4. Data Management Cycle (1) • DEFINE • Objects (student) • Events (dropping out) • Attributes (race/ethnicity) • Classifications (homelessness) • COLLECT • 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)

5. Data Management Cycle (2) • VERIFY • Automated edit checks of data points • Automated and human review of summary statistics • Independent compliance auditing of local documentation and practices • ORGANIZE • Normalized data warehouse to integrate operational data sources • Summary tables and denormalized views of data warehouse • USE • Decision support

6. Exercises • 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.

7. Decision Support Continuum • Stand alone mandated report • NCLB / Adequate Yearly Progress as pdf • Menu of prespecified reports • U-PASS / School Performance Report as interactive website • Ad hoc reporting capability (limited) • OLAP data cubes with Cognos • Statistical analysis (in process) • Microdata sets for SPSS • Data mining (hypothetical) • Cluster analysis for setting performance standards

8. District Size (2006 HB 77):Methodology (USOE Fiscal Note) • Models • Cost function (multiple regression) • Differential equation (optimization) • What if scenario • Characteristics (Show your work!) • Explicit statement of assumptions • Step by step presentation of calculation • Evidence to support validity of model

9. Tuition Tax Credit (2004 HB 271):Politics (SL Tribune 2/23/04) • Who(m) to believe? • 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 • 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”