QUANTITATIVE RESEARCH METHODS WORKSHOP
Abstract: In recent years, political scientists have increasingly used conjoint survey experiments to analyze preferences about objects that vary in multiple attributes. The dominant approach in these studies has been to apply the regression-based estimator for the average marginal component effect (AMCE) proposed by Hainmueller, Hopkins and Yamamoto (2014). Although the standard approach enables model-free inference about preferences underlying conjoint survey data, it has important limitations for analyzing heterogeneity in respondents’ preferences about attributes and investigating how attributes are related to each other in the formation of preference about profiles as a whole. In this paper, we propose an item response theory (IRT) model for conjoint survey data to analyze respondents’ heterogeneous preferences about attributes, building upon a canonical spatial theory of voting to model preferences as a function of respondents’ ideal points on a latent space capturing taste variation. The model also incorporates a set of valence parameters to identify the dimension of preference about attributes that is common to all respondents. We discuss identification conditions, inference via a Bayesian algorithm, and how to map model parameters to substantive quantities of interest. We illustrate the utility of the proposed approach through Monte Carlo simulations as well as a validation analysis of an original online conjoint experiment on presidential candidate choice. (This is joint work with Devin Caughey and Hiroto Katsumata.)
Teppei Yamamoto is an Associate Professor of Political Science at Massachusetts Institute of Techology (MIT) and a Faculty Affiliate of the Center for Statistics at the Institute for Data, Systems, and Society. He also directs the Political Methodology Lab (PML) at MIT’s political science department.