Data Quality Component 2: Data Quality
The term data quality generally refers to the trustworthiness of the data being used, which includes the completeness, accuracy, consistency, availability, validity, integrity, security, and timeliness of the data. Data quality can be challenging to control given the number of groups and individuals often involved with data collection. Therefore, SEA plans for data quality should contemplate data-quality controls, such as processes for validity checks and explanations for variants in the data. Data-quality controls should be designed to assess the usefulness and trustworthiness of the data. When planning for data quality, it is also important to consider how best to align the data with its intended use (e.g., for operations, decision making, and planning).
The following considerations outline how state educational agency (SEA) staff may consider how new or revised data-quality controls play a role in the larger data-quality system.
- Establishing data checks: The SEA staff will need to establish data checks and specifications to ensure the completeness, validity, and accuracy of any new data, and ensure these practices are integrated into the broader schedule and system for data cleaning and auditing. The SEA and districts will also need to establish processes for measuring the level of confidence in the quality of data as well.
- Defining data reporting and adjustment processes: The SEA will need to communicate to districts how to add explanations or other notes regarding errors, such as missing data or duplicative counts. The SEA will also need to communicate about the timeline for error corrections and consequences for late data reporting with districts.
- Developing an access plan: The SEA will need to establish a plan for both how the data will be used (e.g., report cards, public use, research) as well as which groups will have individual access to the data. SEA staff may wish to ensure that the appropriate data systems and reports are available based on how and when it will be used. SEA staff may also wish to gather information or feedback on the relevance of the data to users’ needs.
- Establishing data-quality supports and infrastructure: SEAs can support data quality by enacting policy, providing professional learning, and establishing the necessary processes and infrastructure to ensure accuracy and mitigate technical issues. For example, high-quality data that can be trusted for driving decision-making requires business rules for the extraction, transformation, and uploading of data. When timely, reliable, high-quality data are used to drive decision-making, schools and districts can achieve greater efficiency. SEAs and districts can use professional learning and established processes to help promote consistency in data quality over time regardless of changes in staff or technologies.
SEAs can use the Data Systems Self-Reflection Checklist to consider how their current actions promote quality data systems. To download the Data Systems Self-Reflection Checklist, click here.
For more information on how Washington state is currently approaching data quality, please see the vignette below.
|Data Quality Vignette: Module Series in Washington State Informs Data Quality|
In Washington state, the Office of Superintendent of Public Instruction (OSPI) administers a longitudinal data warehouse of educational data called the Comprehensive Education Data and Research System (CEDARS). Through this system districts report data on courses, students, and teachers. To address issues of data quality, OSPI convened a workgroup that developed a series of modules for school, district, and state staff to learn about data quality. Each module is designed to be used by a facilitator and includes a slide deck, a presenter guide and notes, as well as a brief assessment and a key. All of the modules are available online on the OSPI website. OSPI staff believe these modules have been instrumental in increasing data quality.
The introductory module addresses the roles and responsibilities related to data submission, the factors affecting accuracy of data collection and reporting and how these issues relate to decision-making at the school and district level. The next module, titled “Why are Data Collected,” focuses on the policy and reporting requirements that drive data collection in the state. A third module teaches audiences strategies, best practices, tips, and available tools for improving and promoting data quality. A fourth module instructs audiences on the validation process CEDARS employs and the other data sets used for the validation. A final module focuses on the data files required for highly qualified teachers and methods for resolving data quality issues. To learn more about the CEDARS systems and the supporting modules, click here.
Data Quality Resources:
Common Education Data Standards (CEDS) Program:
Common Education Data Standards. The Status of State Data Dictionaries. Washington, DC: Common Education Data Standards, 2017. https://ceds.ed.gov/pdf/status-of-state-data-dictionaries.pdf.
Data Quality Campaign (DQC):
Data Quality Campaign. Roadmap for Foster Care and K–12 Data Linkages: Key Focus Areas to Ensure Quality Implementation. Washington, DC: Data Quality Campaign, 2017. https://dataqualitycampaign.org/resource/roadmap-for-foster-care/.
State Educational Agencies:
Massachusetts Department of Elementary and Secondary Education and Massachusetts Department of Early Education and Care. Data Quality Program Handbook: Establishing a Culture of Data Quality – Pre-Work. Malden, MA: Massachusetts Department of Elementary and Secondary Education and Massachusetts Department of Early Education and Care, 2012. http://www.doe.mass.edu/infoservices/data/quality/handbook-extract.docx.
North Carolina Department of Public Instruction. Data Quality Management: Best Practices. Raleigh, NC: Public Schools of North Carolina, State Board of Education, NCDPI, 2016. http://www.dpi.state.nc.us/docs/data/management/resources/quality/2010/best-practices.pdf.
State Longitudinal Data Systems (SLDS) Grant Program:
McGroarty, Michael, Chuck Murphy, Meghann Omo, John Sabel, Kate Akers, and Kathy Gosa. SLDS Webinar: Processes for Handling Multiple IDs to Ensure Data Quality. Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, 2015. https://slds.grads360.org/services/PDCService.svc/GetPDCDocumentFile?fileId=16363.