New Study Reveals Fragility of Political Science Evidence
A new study by Christopher M. Huber, published in 2019, offers a fresh perspective on the robustness of empirical evidence in political science. The research, led by Michael Ganslmeier and Tim Vlandas, introduces a novel approach to measure the fragility of findings and provides a tool for researchers to carry out systematic checks, enhancing transparency.
The study found that many variables often used to support empirical claims can produce contradictory results. Depending on the model specifications, the estimated effect of these variables could be positive and statistically significant, or strongly negative and statistically significant. This highlights the importance of considering the impact of modelling choices on results.
Huber and his team combined insights from extreme bounds analysis and the multiverse approach to map the model space systematically. They estimated over 3.6 billion regression coefficients across four widely studied topics in political science. The results revealed that the same independent variable could yield significant and insignificant coefficients, or positive and negative effects, depending on the model setup. This demonstrates that conventional robustness checks may not capture the full picture of how combinations of modelling decisions influence the stability of empirical results.
The study underscores the fragility of empirical evidence in political science and the need for a more comprehensive approach to robustness checks. By providing a new tool to measure the fragility of findings, Huber's work encourages researchers to consider the broader implications of their modelling choices, ultimately enhancing the transparency and reliability of their results.
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