A data quality checking guide has been developed to facilitate undergraduates on the critical aspects of data quality assessment. The goal is to become competent data consumers and data generators as well. This checking guide encompasses six fundamental dimensions: Data Credibility, Timeliness, Information Completeness, Data Accuracy, Data Consistency, and Data Deduplication. These dimensions are frequently cited in scholarly references on data quality. Each dimension plays a crucial role in ensuring the reliability and usability of data. Scenarios of students' work are also discussed.
KeywordsData literacy, Data quality, Checking guide
Date of this Version2023
Recommended CitationThis Data Quality Checking Guide is available under a CC BY 4.0 license, with attribution to Wei Zakharov, Purdue Libraries and School of Information Studies.
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