Replication data for: Improving studies of sensitive topics using prior evidence: An informative Bayesian approach for list experiments
| Item Type: | Dataset |
|---|---|
| Title: | Replication data for: Improving studies of sensitive topics using prior evidence: An informative Bayesian approach for list experiments |
| Date: | 22 December 2025 |
| Creator: |
Lu, Xiao  ORCID: 0000-0003-1175-4687 ; Traunmueller, Richard ORCID: 0000-0001-9487-091X
|
| Divisions: | School of Social Sciences > Political Science, Empirical Democracy Research (Traunmüller 2017-) |
| DDC Classification: |
320 Political science |
|---|---|
| Abstract: | Estimates of sensitive questions from list experiments are often much less precise than desired. We address this well-known inefficiency problem by presenting an informative Bayesian approach which combines indirect measures with prior information. Specifying informed priors amounts to a principled combination of information which increases the efficiency of model estimates. This framework generalizes a range of different modeling approaches for list experiments, such as the inclusion of direct items, auxiliary information, the double list experiment, and the combination of list experiments with other indirect questioning techniques. As we demonstrate in real-world examples from political science, the informative Bayesian approach not only improves the utility but also changes the substantive implications drawn from list experiments. (English) |
| External Identifier for Data: | https://doi.org/10.7910/DVN/QE1XNM |
| URL: | https://madata.bib.uni-mannheim.de/688/ |
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| Access (Controlled): | Only Metadata |
| License (Controlled): | Creative Commons: CC-BY-NC | Attribution-Noncommercial 4.0 |
| Related Publication(s) in MADOC: | Lu, Xiao und Traunmüller, Richard (2026), Improving studies of sensitive topics using prior evidence: an informative Bayesian approach for list experiments |
Full text not available from this repository.
| Date Deposited: | 10 Apr 2026 11:20 |
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| Last Modified: | 10 Apr 2026 11:20 |
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