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Ensuring Data Quality in Collections

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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  1. Ensuring Data Quality in Collections Monday, October 29, 2012 Brian Townsend, VT Department of Education

  2. Vermont

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. Contacts & Additional Resources Contact information: Brian Townsend, brian.townsend@state.vt.us Corey Chatis, corey.chatis@sst-slds.org 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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