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How Multiple Imputation Makes a Difference*

* This paper is a replication of an original study

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  • Lall, Ranjit

Abstract

Political scientists increasingly recognize that multiple imputation represents a superior strategy for analyzing missing data to the widely used method of listwise deletion. However, there has been little systematic investigation of how multiple imputation affects existing empirical knowledge in the discipline. This article presents the first large-scale examination of the empirical effects of substituting multiple imputation for listwise deletion in political science. The examination focuses on research in the major subfield of comparative and international political economy (CIPE) as an illustrative example. Specifically, I use multiple imputation to reanalyze the results of almost every quantitative CIPE study published during a recent five-year period in International Organization and World Politics, two of the leading subfield journals in CIPE. The outcome is striking: in almost half of the studies, key results “disappear†(by conventional statistical standards) when reanalyzed.

Suggested Citation

  • Lall, Ranjit, 2016. "How Multiple Imputation Makes a Difference," Political Analysis, Cambridge University Press, vol. 24(4), pages 414-433.
  • Handle: RePEc:cup:polals:v:24:y:2016:i:04:p:414-433_01
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    Cited by:

    1. Tobias Heinrich & Yoshiharu Kobayashi, 2022. "Evaluating explanations for poverty selectivity in foreign aid," Kyklos, Wiley Blackwell, vol. 75(1), pages 30-47, February.
    2. Jennings, Will & Lewis-Beck, Michael & Wlezien, Christopher, 2020. "Election forecasting: Too far out?," International Journal of Forecasting, Elsevier, vol. 36(3), pages 949-962.
    3. Brown, Annette N. & Wood, Benjamin Douglas Kuflick, 2018. "Which tests not witch hunts: A diagnostic approach for conducting replication research," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 12, pages 1-26.
    4. Minakshi Nayak & Karen Wills & Megan Teychenne & Jo Salmon & Verity Cleland, 2021. "Patterns and Predictors of Sitting among Women from Disad-Vantaged Neighbourhoods over Time: A 5-Year Prospective Cohort Study," IJERPH, MDPI, vol. 18(9), pages 1-13, April.
    5. Kris‐Stella Trump, 2023. "Income inequality is unrelated to perceived inequality and support for redistribution," Social Science Quarterly, Southwestern Social Science Association, vol. 104(2), pages 180-188, March.
    6. Freese, Jeremy & Peterson, David, 2017. "Replication in Social Science," SocArXiv 5bck9, Center for Open Science.
    7. Håvard Hegre & Kristina Petrova & Nina von Uexkull, 2020. "Synergies and Trade-Offs in Reaching the Sustainable Development Goals," Sustainability, MDPI, vol. 12(20), pages 1-24, October.
    8. Zhong, Hua & Hu, Wuyang & Penn, Jerrod M., 2018. "Application of Multiple Imputation in Dealing with Missing Data in Agricultural Surveys: The Case of BMP Adoption," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 43(1), January.
    9. Bernhard Reinsberg & Oliver Westerwinter, 2023. "Institutional Overlap in Global Governance and the Design of Intergovernmental Organizations," The Review of International Organizations, Springer, vol. 18(4), pages 693-724, October.
    10. Albert Chiu & Xingchen Lan & Ziyi Liu & Yiqing Xu, 2023. "What To Do (and Not to Do) with Causal Panel Analysis under Parallel Trends: Lessons from A Large Reanalysis Study," Papers 2309.15983, arXiv.org.
    11. Iemmi, Valentina, 2021. "Global collective action in mental health financing: Allocation of development assistance for mental health in 142 countries, 2000–2015," Social Science & Medicine, Elsevier, vol. 287(C).
    12. Silvia Marchesi & Tania Masi, 2021. "Delegation of implementation in project aid," The Review of International Organizations, Springer, vol. 16(3), pages 655-687, July.
    13. Gómez-Fernández, Nerea & Mediavilla, Mauro, 2021. "Exploring the relationship between Information and Communication Technologies (ICT) and academic performance: A multilevel analysis for Spain," Socio-Economic Planning Sciences, Elsevier, vol. 77(C).
    14. Freire, Danilo & Uzonyi, Gary, 2018. "What Drives State-Sponsored Violence?: Evidence from Extreme Bounds Analysis and Ensemble Learning Models," SocArXiv pzx3q, Center for Open Science.

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