Item Response Theory for Conjoint Survey Data

Dartmouth Events

Item Response Theory for Conjoint Survey Data

Teppei Yamamoto Director, MIT Political Methodology Lab (PML)

Friday, April 27, 2018
Silsby Hall 119
Intended Audience(s): Public

Abstract: In recent years, there has been an increasing use of conjoint survey experiments in political science 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 Effects (AMCE) proposed by Hainmueller, Hopkins and Yamamoto (2014). While 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.

Teppei Yamamoto

Director, MIT Political Methodology Lab (PML)
Associate Professor of Political Science
Alfred Henry and Jean Morrison Hayes Chair
Massachusetts Institute of Technology

For more information, contact:
Laura Mitchell

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