1. Nuts and Bolts: The Importance of Data Quality 2003 Presentation to SAISD Administrators Kathy O’Neill, Larry Reddell – Office of PEIMS and Data Services Pat Schmitz – Office of Testing Iris Amon – Office of Research and Evaluation

2. What areas of daily campus operations are affected by data quality?  With a partner, list ways you feel data quality could affect your campus.

3. PEIMS - Public Education and Information Management: Official Information  PEIMS encompasses all data requested and received by TEA about public education, including student demographic and academic performance, personnel, financial, and organizational information.* Source: PEIMS DATA Standards

4. PEIMS - Public Education and Information Management: Official Information PEIMS is classified into two broad categories:  Data collected through the PEIMS electronic collection method, utilizing a standard set of definitions, codes, formats, procedures and dates for the collection of data (Data Standards).  Any other collections, calculations, and analyses of data used for evaluating, monitoring, or auditing public education (such as state assessment, federal funding, and Foundation School Program data). Source: PEIMS Data Standards

5. Important Points  If it says so in PEIMS….that’s how it is! PEIMS is the source TEA goes to for data about our district.  When TEA identifies discrepancies with PEIMS data, the affected program/campus has to file a corrective action plan.  Codes and decisions about data entry are the responsibility of campus administrators.  Data clerks should be devoted to rapid and accurate data entry.

6. What happens when PEIMS data quality is not at a high level?  We risk a TEA desk audit.  We must file a Corrective Action Plan.  We put funding at risk.  We put test data at risk.  We put research information at risk.  We take the chance that students may not receive the instructional services they require  We take the chance that TEA and others will make decisions about our district based on inaccurate or incomplete data

7. Special Data Inquiry Unit Investigations Can Result In:  An Automatic loss of Recognized or Exemplary status  An Academically Unacceptable: Data Quality accountability rating, through negligence  A report of fraud to the District Attorney  A Public Disclosure Hearing  A Commissioner’s Hearing  An Agency Monitor  An Agency Master

8. Examples of Critical Issues in PEIMS  Attendance  Discipline  At-Risk  Leavers  Special Programs  PID (SSNs and student demographic data)  Finance  Staff (Payroll, Permits, Responsibilities)

9. Examples of Critical Issues in Testing  Gridding wrong score code can affect:  Campus accountability  Student results  Student graduation  Student promotion  Gridding wrong information can cause:  Mismatch with state historical file  Other Issues affecting campus accountability  Voids  Giving wrong exam

10. Examples of Critical Issues in Research and Evaluation Incomplete or inaccurate data entry leads to: Inaccurate reports used for  planning  allocation of funds  grant requests  inaccurate evaluations, comparisons

11. Continuous Data Collection

12. The Need for a Continuous Feedback Loop  Individuals want interim reports…reports are not of value if the data has not been entered.  PEIMS data is the source of district data and the NCS testing tapes are the source of performance data.  Inaccurate entries mean we live with the results if it hurts the district…results are changed if we erred in our own favor (investigations sometimes occur).

13. Closing Activity  Individually please respond:  Something I knew  Something I learned  Something I want to act on immediately

14. Questions and Answers  If we do not answer your question in this part of our presentation, please write it on a post it note and put it on our “Bin” poster.

15. Eight Characteristics of Data Quality  Security – protected against unauthorized access  Availability – data is present and ready for use  Integrity – the extent to which rules are followed  Accuracy – the extent to which data value is close to the real value  Completeness – sufficient, but not more than necessary  Clarity – readily understood and not open to more than one interpretation  Consistency –yield the same or compatible results as a particular time, at different times, and longitudinally  Timeliness – reflects a time that is appropriate for a particular activity or use