ESRA 2025 sessions by theme

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Sample selection bias, item non-response, and dropouts (a form of censoring) are common challenges in large-scale population surveys. In longitudinal surveys, selection bias occurs at the start, item non-response during the survey, and dropout (censoring) at the end. In cross-sectional surveys, selection bias and non-response are the primary sources of missing data. These issues can severely impact the quality of analysis and the validity of inferences if not properly addressed.

A special challenge for analysis occurs when the mechanism driving one of those phenomena depends (additionally) on unobserved information, making the missing data not random and potentially leading to non-ignorable selection bias, informative censoring or non-ignorable missing data. Consequently, it is crucial to assess the robustness of results under different plausible assumptions about the missing-data, selection, or censoring mechanisms, when it seems plausible that standard assumptions may not hold.

In this session, we welcome research on novel and innovative methods to prevent misleading inference under one or several of the described challenges related to incomplete, biasedly selected, or censored survey data. This research might cover:

1. Use cases showing the harm of non-ignorable selection bias, informative censoring, or non-ignorable missing data.
2. Novel approaches for detecting (non-ignorable) selection bias in traditional surveys and non-probability samples.
3. Novel imputation procedures, likelihood-based approaches, machine learning and tree-based methods and Bayesian estimation techniques to address (non-ignorable) missing data and/or informative censoring.
4. Methods for conducting sensitivity analyses in cases where deviations from missing at random mechanisms are realistic.