Download presentation
Presentation is loading. Please wait.
Published byNeil Floyd Modified over 9 years ago
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
Similar presentations
© 2025 SlidePlayer.com. Inc.
All rights reserved.