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Improving the External Validity of Conjoint Analysis: The Essential Role of Profile Distribution

Published online by Cambridge University Press:  14 January 2021

Brandon de la Cuesta Open the ORCID record for Brandon de la Cuesta [Opens in a new window] ,
Naoki Egami Open the ORCID record for Naoki Egami [Opens in a new window]  and
Kosuke Imai Open the ORCID record for Kosuke Imai [Opens in a new window]
Brandon de la Cuesta
Affiliation:
Postdoctoral Research Fellow, King Center on Global Development, Stanford University, Palo Alto, CA94305, USA. Email: brandon.delacuesta@stanford.edu, URL: https://brandondelacuesta.com
Naoki Egami*
Affiliation:
Assistant Professor, Department of Political Science, Columbia University, New York, NY10027, USA. Email: naoki.egami@columbia.edu, URL: https://naokiegami.com
Kosuke Imai
Affiliation:
Professor, Department of Government and Department of Statistics, Harvard University, 1737 Cambridge Street, Institute for Quantitative Social Science, Cambridge, MA02138, USA. Email: imai@harvard.edu, URL: https://imai.fas.harvard.edu
*
Corresponding author Naoki Egami
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Abstract

Conjoint analysis has become popular among social scientists for measuring multidimensional preferences. When analyzing such experiments, researchers often focus on the average marginal component effect (AMCE), which represents the causal effect of a single profile attribute while averaging over the remaining attributes. What has been overlooked, however, is the fact that the AMCE critically relies upon the distribution of the other attributes used for the averaging. Although most experiments employ the uniform distribution, which equally weights each profile, both the actual distribution of profiles in the real world and the distribution of theoretical interest are often far from uniform. This mismatch can severely compromise the external validity of conjoint analysis. We empirically demonstrate that estimates of the AMCE can be substantially different when averaging over the target profile distribution instead of uniform. We propose new experimental designs and estimation methods that incorporate substantive knowledge about the profile distribution. We illustrate our methodology through two empirical applications, one using a real-world distribution and the other based on a counterfactual distribution motivated by a theoretical consideration. The proposed methodology is implemented through an open-source software package.

Keywords

causal inferenceconjoint analysisfactorial experimentsexternal validity

Information

Type
Article
Information
Political Analysis , Volume 30 , Issue 1 , January 2022 , pp. 19 - 45
Copyright
© The Author(s) 2021. Published by Cambridge University Press on behalf of the Society for Political Methodology

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Footnotes

Edited by Jeff Gill

Authors’ note: The proposed methodology is implemented via an open-source software R package factorEx, available through the Comprehensive R Archive Network (https://cran.r-project.org/package=factorEx).

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