Article

Systematic Data Validation: Improving Research by Improving Data Quality

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Abstract

Researchers presenting quantitative studies commonly justify their design and analytic choices. There is, however, a crucial part of the research method that is commonly overlooked: validating the data under analysis. Valid data is necessary to derive valid inferences from research and can only be guaranteed through data validation. Despite the importance of this process, a survey of education researchers indicates data validation is used inconsistently. Additionally, a review of journal and grant submission guidelines revealed little attention to data processing and validation. To facilitate wider adoption of data validation, we identify key principles of data validation, identify specific aspects of data sets that should be checked, and make recommendations for how to address identified issues. Additionally, we provide practical strategies for tracking and managing data validation and discuss how these practices should be reported. Both of this should be considered by those developing new AI tools to assist data validation. We end by considering four strategies to encourage data validation among researchers.

Keywords: Data validation, Data quality, Data checking, Research methods

How to Cite:

DeCoster, J., Francis, M. K., Lee, G. & Rubinstein, E., (2026) “Systematic Data Validation: Improving Research by Improving Data Quality”, Practical Assessment, Research, and Evaluation 31(1): 10. doi: https://doi.org/10.7275/pare.3586