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Hierarchical Item Response Models for Analyzing Public Opinion

  • Xiang Zhou (a1)
Abstract

Opinion surveys often employ multiple items to measure the respondent’s underlying value, belief, or attitude. To analyze such types of data, researchers have often followed a two-step approach by first constructing a composite measure and then using it in subsequent analysis. This paper presents a class of hierarchical item response models that help integrate measurement and analysis. In this approach, individual responses to multiple items stem from a latent preference, of which both the mean and variance may depend on observed covariates. Compared with the two-step approach, the hierarchical approach reduces bias, increases efficiency, and facilitates direct comparison across surveys covering different sets of items. Moreover, it enables us to investigate not only how preferences differ among groups, vary across regions, and evolve over time, but also levels, patterns, and trends of attitude polarization and ideological constraint. An open-source R package, hIRT, is available for fitting the proposed models.

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Author’s note: The author thanks Ken Bollen, Bryce Corrigan, Max Goplerud, Gary King, Jonathan Kropko, Jie Lv, Barum Park, Yunkyu Sohn, Yu-Sung Su, Dustin Tingley, Yu Xie, Teppei Yamamoto, and two anonymous reviewers for helpful comments on previous versions of this work. Replication data are available in Zhou (2018b).

Contributing Editor: Jeff Gill

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