a tailor made data quality approach for higher educational data

• DOI: 10.5281/zenodo.4646957
• Publication Date: 2020-07-09
• Authors: Daraio C.; Bruni R.; Catalano G.; Daraio A.; Matteucci G.; Scannapieco M.; Wagner-Schuster D.; Lepori B.
• Keywords: higher education institutions; Higher education institutions; 4. Education; development of data and information services; 05 social sciences; information quality; Cross-sectional and multiannual quality checks; Information technology; QA75.5-76.95; Knowledge organization; T58.5-58.64; Cross-sectional and multiannual quality checks; Development of data and information services; Higher education institutions; Information quality; Knowledge organization; knowledge organization; Electronic computers. Computer science; Development of data and information services; ETER; European Tertiary Education Register; Knowledge organization; Development of data and information services; Crosssectional and multiannual quality checks; Higher education institutions; Information quality; 0509 other social sciences; Information quality; cross-sectional and multiannual quality checks

Abstract Purpose This paper relates the definition of data quality procedures for knowledge organizations such as Higher Education Institutions. The main purpose is to present the flexible approach developed for monitoring the data quality of the European Tertiary Education Register (ETER) database, illustrating its functioning and highlighting the main challenges that still have to be faced in this domain. Design/methodology/approach The proposed data quality methodology is based on two kinds of checks, one to assess the consistency of cross-sectional data and the other to evaluate the stability of multiannual data. This methodology has an operational and empirical orientation. This means that the proposed checks do not assume any theoretical distribution for the determination of the threshold parameters that identify potential outliers, inconsistencies, and errors in the data. Findings We show that the proposed cross-sectional checks and multiannual checks are helpful to identify outliers, extreme observations and to detect ontological inconsistencies not described in the available meta-data. For this reason, they may be a useful complement to integrate the processing of the available information. Research limitations The coverage of the study is limited to European Higher Education Institutions. The cross-sectional and multiannual checks are not yet completely integrated. Practical implications The consideration of the quality of the available data and information is important to enhance data quality-aware empirical investigations, highlighting problems, and areas where to invest for improving the coverage and interoperability of data in future data collection initiatives. Originality/value The data-driven quality checks proposed in this paper may be useful as a reference for building and monitoring the data quality of new databases or of existing databases available for other countries or systems characterized by high heterogeneity and complexity of the units of analysis without relying on pre-specified theoretical distributions.