Master Data Quality Assessment with the KODAQS Toolbox
Equip your research with tools to assess and improve data quality and ensure the validity of your findings.
Make Use of Learning Resources
Access the KODAQS Toolbox for practical coding examples of key quality indicators and step-by-step tutorials.
Tailored Insights for Diverse Data Types
Navigate the unique data quality challenges of survey, digital behavioral, and linked data with applied data quality assessments.
The KODAQS Data Quality Toolbox is an educational resource from the Competence Center for Data Quality (KODAQS), aimed at assisting researchers in developing skills for data quality assessment. It provides tutorials and practical coding examples of key quality indicators to evaluate data across various types—survey, digital behavioral, and linked data—focusing on improving validity and ensuring unbiased research outcomes. Whether handling survey data, digital behavioral data, or linked data, the KODAQS Toolbox guides users in data quality assessments tailored to diverse research interests.
Tools are categorized by Data Type or Error Source.
Find by Data Type
In social science research, data capture essential information about behaviors, attitudes, and demographics, providing the basis for analyzing social patterns and relationships. The different data types—whether self-reported, observed, or combined from multiple sources—enable a comprehensive understanding of complex social dynamics and support a wide range of research questions.
Find by Error Source
In social science data, error sources can impact the reliability and validity of research findings. Biases and inaccuracies—whether in data collection, measurement, sampling—can distort insights and reduce generalizability. Addressing these errors is essential to maintain the integrity of data analysis and ensure meaningful interpretations of social behaviors and trends
