Design and implementation of data quality controls in the EQ-DAPHNIE study: insights from the pilot phase and 15-country analysis

Authors

• Fatima Al Sayah, University of Alberta
• Hilary Short, University of Alberta
• Juan M. Ramos-Goñi, Warsaw School of Economics
• Rosalie Viney, University of Technology Sydney
• Erica I. Lubetkin, CUNY School of MedicineFollow
• Mathieu F. Janssen, EuroQol Research Foundation
• Jeffrey A. Johnson, University of Alberta

Document Type

Article

Publication Date

11-2025

Abstract

Objective

The EQ-DAPHNIE (EuroQol Data for Assessment of Population Health Needs and Instrument Evaluation) project is a large, multi-country survey initiative designed to generate population norms and enable comparative research using self-reported health measures. This paper describes the quality control processes and summarizes data quality metrics from the United Kingdom (UK) pilot and full implementation across 15 countries.

Methods

Representative samples were recruited via Dynata, an online survey panel provider, using quota sampling by age, sex, income, community setting, and language (where applicable). The UK pilot (n = 3012) informed survey refinements ahead of full rollout (n = 68,411). Quality metrics included completion rates, bot detection, speeding, missing data, outliers, and quota achievement.

Results

Across countries, response rates ranged from 80.1 to 100%, with completion rates varying widely (22.9% in Brazil to 60.8% in Japan; average 42.4%). Bot exclusions averaged 3.0%, peaking in China (11.7%). Speeding was low (0.3% average), and duplicate records were rare. Completion times ranged from 18.3 (France) to 31.4 min (New Zealand). Missing data varied substantially (0.0–48.7%), with Japan and Spain showing the least. Quota fulfillment ranged from 68.7 to 98.6%. Consistency checks showed strong agreement for repeated items—marital status (92.8–98.9%) and age (92.3–98.7%).

Conclusions

The quality control measures implemented throughout the EQ-DAPHNIE project effectively addressed common issues such as bot responses, speeding, and missing data, resulting in generally high-quality and representative datasets. However, variability across countries underscores the need to account for quality indicators when using the data for norm-setting or cross-country comparisons.

Comments

This article was originally published in Quality of Life Research, available at https://doi.org/10.1007/s11136-025-04074-y 

Recommended Citation

Al Sayah, Fatima; Short, Hilary; Ramos-Goñi, Juan M.; Viney, Rosalie; Lubetkin, Erica I.; Janssen, Mathieu F.; and Johnson, Jeffrey A., "Design and implementation of data quality controls in the EQ-DAPHNIE study: insights from the pilot phase and 15-country analysis" (2025). CUNY Academic Works.
https://academicworks.cuny.edu/cm_pubs/9