Ensuring data quality in collections
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Ensuring Data Quality in Collections. Monday, October 29, 2012 Brian Townsend, VT Department of Education. Vermont. Vermont. Background CURRENT: Method of Data Collection Online: Oracle Forms/Reports Data Collections Mostly data entry with pre-populated data Some batch file uploads

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Ensuring Data Quality in Collections

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Ensuring data quality in collections

Ensuring Data Quality in Collections

Monday, October 29, 2012

Brian Townsend, VT Department of Education


Ensuring data quality in collections

Vermont


Vermont

Vermont

Background

  • CURRENT: Method of Data Collection

    • Online: Oracle Forms/Reports Data Collections

      • Mostly data entry with pre-populated data

      • Some batch file uploads

    • Distributed: Microsoft Access

  • FUTURE: FY12 SLDS Grant (Vermont’s 1st)

    • Vermont Automated Data Reporting (VADR) Project

      • Deliverable 1: Statewide Vertical Reporting


Vermont cont

VERMONT, CONT.

Communication & Training

  • Application Specific Documentation

    • On Data Collection website & Inside Oracle applications

  • Data Collection Trainings

    • Role-specific based on collection (e.g. Registrar, Business Manager, etc.)

    • In-person trainings

    • Online Trainings

      • Learning Network of Vermont (LNV)

      • GoTo Suite

  • Weekly Field Memo

  • Helpdesk

4


Vermont cont1

Vermont, cont.

Data Validation & Corrections

  • Application/Database-level validation rules

  • Error-checking procedures

    • Backend: Oracle Database procedures

    • External: SPSS (e.g. frequency checks, auto-fixes, flags to fix manually)

    • EdFacts reporting: Built in edit checks to ensure EdFacts rules aren’t violated.

  • Administrator Sign-Off of Data Collection Indicators


Vermont cont2

VERMONT, CONT.

Data Validation & Corrections, cont.

  • Post-hoc Validation & Correction

    • Return Error Reports

    • Disputed Students

    • Perm Checking (record former last names)

  • 3-year Revision Window

    • Can lead to new errors

      Data Use

  • High Stakes Data

    • Membership = Money

    • Visibility => Data Quality

6


Vermont cont3

VERMONT, CONT.

Data Use, cont.

  • Town Meeting Reports

    • Spending & Assessment Results

  • Public/Legislature/Parents

    • Compare Assessment Results across schools => Choice

      Wrap Up

  • Small State

    • Demographics make it easier to spot large % change

    • Anomalies stand out more

  • SLDS will improve data quality

    • Timeliness & Availability

7


Contacts additional resources

Contacts & Additional Resources

Contact information:

Brian Townsend, [email protected]

Corey Chatis, [email protected]

For more information on Data Quality:

Statewide Standardized Course Codes: SLDS Best Practices Brief

Traveling Through Time: Forum Guide to LDSs, Book IV: Advanced LDS Usage


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