Data Quality Assurance in Statewide Longitudinal Education Data Systems: A CALPADS Case Study

Abstract

Statewide Longitudinal Education Data Systems (SLDS) play an important role in collecting, integrating, and maintaining education data across multiple academic years and institutions. This case study examines data quality assurance practices in the California Longitudinal Pupil Achievement Data System (CALPADS), with particular emphasis on data accuracy, completeness, consistency, validity, and timeliness. The study explores the challenges associated with collecting data from diverse educational institutions and integrating information across different systems, reporting periods, and student records. It also examines quality assurance techniques such as automated validation rules, data reconciliation, error detection, standardized reporting procedures, and continuous monitoring. Particular attention is given to the role of data governance and systematic quality checks in identifying and reducing inaccurate or incomplete records. The case study demonstrates how a structured data quality assurance framework can improve the reliability and usability of longitudinal education data for reporting, accountability, research, and educational planning. The findings highlight the importance of combining automated validation with well-defined data standards, stakeholder coordination, and continuous quality improvement to maintain trustworthy statewide education information.