ESRA 2027 sessions by theme

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Session Details

Data linkage has become an increasingly valuable component of survey research, enabling survey data to be enriched with administrative records, digital trace data and other external sources. Linked data can improve measurement, reduce respondent burden, expand substantive analytical opportunities and support methodological approaches, including addressing non-response and missing data in surveys. However, linkage typically relies on participant consent, which not all respondents provide, meaning that identifying effective, ethical and practical approaches to maximise consent has become a key methodological challenge.

Obtaining consent is particularly challenging in self-administered surveys, such as web surveys, where consent rates are consistently lower than in interviewer-administered modes. Evidence suggests this reflects a genuine mode effect, rather than differences in the types of respondents who participate online. As surveys increasingly adopt web-first or web-only designs, identifying ways to improve consent rates while ensuring that consent remains informed has become a priority.

The growing use of linked data has also raised broader methodological questions regarding data quality, consent bias and the strengths and limitations of using linked data for research. This session welcomes theoretical, methodological and empirical contributions on:

- predictors of consent and assessment of consent bias;

- the cognitive and decision-making processes underlying consent;

- explanations for mode differences in respondents' willingness to consent;

- effects of consent question wording, format, timing and presentation on consent decisions;

- survey design innovations that increase consent in web surveys;

- re-approaching respondents who initially refused consent;

- within-person changes in consent behaviour over time;

- the use of incentives to encourage consent;

- consent to linkage across multiple data domains legal and ethical issues including alternative approaches such as opt-out models;

- assessment of quality and completeness of linked data;

- methodological applications of linked survey and administrative data, including assessment and adjustment of bias, confounding and measurement error.