Robinson, Thomas

Number of items: 11.
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  • Robinson, Thomas S., Duch, Raymond, Loewen, Peter, Zakharov, Alexei (2025). Replication Data for: "Governing in the face of a global crisis: when do voters punish and reward incumbents?". [Dataset]. Harvard Dataverse. https://doi.org/10.7910/dvn/hf8ydz
  • Robinson, Thomas, Duch, Raymond (2023). Replication Data for: How to detect heterogeneity in conjoint experiments. [Dataset]. Harvard Dataverse. https://doi.org/10.7910/dvn/cg9vpe
    • Public
  • Thomas, Rhys, Rooper, Laurence, Duch, Raymond, Robinson, Thomas, Zhakarov, Alexei, Clarke, Philip (2026). Lottery or triage? Controlled experimental evidence from the COVID-19 pandemic on public preferences for allocation of scarce medical resources. Medical Decision Making, 46(1), 102 - 115. https://doi.org/10.1177/0272989X251367777 picture_as_pdf
  • Sorace, Miriam, Robinson, Thomas, Frese, Joris, Hix, Simon (10 November 2025) Nano-targeting or mass appeal, what makes persuasive climate communications? Impact of Social Sciences Blog. picture_as_pdf
  • Robinson, Thomas, Adamson, Matthew, Barrett, Gordon, Jacobsen, Lif (29 April 2025) Can science diplomacy keep up with a world in crisis? Impact of Social Sciences Blog. picture_as_pdf
  • Duch, Raymond, Loewen, Peter, Robinson, Thomas S., Zakharov, Alexei (2025). Governing in the face of a global crisis when do voters punish and reward incumbent governments? Proceedings of the National Academy of Sciences of the United States of America, 122(4). https://doi.org/10.1073/pnas.2405021122 picture_as_pdf
  • Robinson, Thomas (20 September 2024) How is generative AI changing social science? Impact of Social Sciences Blog. picture_as_pdf
  • Robinson, Thomas, Tax, Niek, Mudd, Richard, Guy, Ido (2024). Active learning with biased non-response to label requests. Data Mining and Knowledge Discovery, 38(4), 2117 - 2140. https://doi.org/10.1007/s10618-024-01026-x picture_as_pdf
  • Robinson, Thomas, M. Duch, Raymond (2024). How to detect heterogeneity in conjoint experiments. Journal of Politics, 86(2), 412 - 427. https://doi.org/10.1086/727597 picture_as_pdf
  • Lall, Ranjit, Robinson, Thomas (2023). Efficient multiple imputation for diverse data in Python and R: MIDASpy and rMIDAS. Journal of Statistical Software, 107(9), 1-38. https://doi.org/10.18637/jss.v107.i09 picture_as_pdf
  • Lall, Ranjit, Robinson, Thomas (2022). The MIDAS touch: accurate and scalable missing-data imputation with deep learning. Political Analysis, 30(2), 179 - 196. https://doi.org/10.1017/pan.2020.49 picture_as_pdf