Published online by Cambridge University Press: 27 September 2017
Due to the crucial role of political knowledge in democratic participation, the measurement of political knowledge has been a major concern in the discipline of political science. Common formats used for political knowledge questions include multiple-choice items and open-ended identification questions. The conventional wisdom holds that multiple-choice items induce guessing behavior, which leads to underestimated item-difficulty parameters and biased estimates of political knowledge. This article examines guessing behavior in multiple-choice items and argues that a successful guess requires certain levels of knowledge conditional on the difficulties of items. To deal with this issue, we propose a Bayesian IRT guessing model that accommodates the guessing components of item responses. The proposed model is applied to analyzing survey data in Taiwan, and the results show that the proposed model appropriately describes the guessing components based on respondents’ levels of political knowledge and item characteristics. That is, in general, partially informed respondents are more likely to have a successful guess because well-informed respondents do not need to guess and barely informed ones are highly seducible by the attractive distractors. We also examine the gender gap in political knowledge and find that, even when the guessing effect is accounted for, men are more knowledgeable than women about political affairs, which is consistent with the literature.
Authors’ note: The authors would like to thank Chia-yi Lee, Kevin A. Clarke, T. Y. Wang, Simon Jackman, Michelle Torres, Chi Huang, Dean Lacy, Brett Benson, Emerson Niou, Chung-li Wu, and Ching-ping Tang for helpful comments. Earlier versions of this manuscript were presented at the 2014 APSA Annual Meeting, 2015 Asian Political Methodology Conference, and 2015 APSA Annual Meeting. The research is financed by the Ministry of Science and Technology, R.O.C. under grant MOST 104-2410-H-004-091-MY2. We are also grateful to the editor, Michael Alvarez, and two anonymous reviewers for helpful suggestions. The replication materials are available at doi:10.7910/DVN/80WUQB (Tsai and Lin 2017).
Contributing Editor: R. Michael Alvarez.