Total Survey Error in Biomeasure Data Collection

Background The integration of biomeasures into social surveys has grown rapidly, enabling new insights into biosocial processes by combining probability-based survey data with biological indicators. However, biomeasure collection outside clinical settings introduces unique sources of error that are not fully addressed by traditional survey methodology frameworks. This paper adapts the Total Survey Error (TSE) framework to account for the distinctive challenges of biomeasure data collection in surveys. Methods We review the stages of biomeasure data collection in social surveys and systematically map them onto the TSE framework. We extend the framework to identify new error sources, including biomeasure nonresponse, data loss during transmission, laboratory and batch effects, and interviewer-related biases. The discussion draws on evidence from large-scale biosocial surveys and methodological studies, highlighting trade-offs between different sources of error and the implications for study design, data processing, and analysis. Results Our extended framework demonstrates that biomeasure collection adds multiple new opportunities for error across both selection and measurement processes. On the selection side, additional nonresponse mechanisms—such as refusal to consent to biological collection or ineligibility due to physical characteristics—can create systematic biases. On the measurement side, issues such as interviewer variability, shipment and storage conditions, laboratory practices, and batch effects can significantly influence data quality. Trade-offs emerge between maximizing measurement precision and minimizing participation bias, between standardization in clinical contexts and ecological validity in household settings, and between cost constraints and optimal data integrity. Conclusions The adapted TSE framework provides a structured approach to evaluating and minimizing errors in biomeasure data collection in social surveys. Recognizing and addressing new sources of error is essential for producing valid and generalizable biosocial research. Future work should focus on developing standardized protocols, improving interviewer and nurse training, expanding feasible self-collection methods, and quantifying the relative magnitude of different error sources. By systematically incorporating biomeasures into probability-based surveys while accounting for these challenges, researchers can strengthen the reliability and policy relevance of biosocial findings.