How to Detect Heterogeneity in Conjoint Experiments.
Conjoint experiments are fast becoming one of the dominant experimental methods within the social sciences. Despite recent efforts to model heterogeneity within this type of experiment, the relationship between the conjoint design and lower-level causal estimands is underdeveloped. In this article,...
| Publicado en: | Journal of Politics Vol. 86; no. 2; pp. 412 - 428 |
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| Autores principales: | , |
| Formato: | Artículo |
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University of Chicago Press
Apr2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=177204588&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 177204588 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00223816 JPO jtl: Journal of Politics issn: 00223816 maglogo: N pubinfo: dt: Apr2024 vid: 86 iid: 2 pid: 415 pub: University of Chicago Press artinfo: ui: 177204588 10.1086/727597 ppf: 412 ppct: 16 formats: tig: atl: How to Detect Heterogeneity in Conjoint Experiments. aug: au: Robinson, Thomas S. Duch, Raymond M. affil: London School of Economics and Political Science University of Oxford su: Heterogeneity Voting research Social science research methods Conjoint analysis New right (Politics) Machine learning Parallel algorithms sug: subj: Heterogeneity Voting research Social science research methods Research and Development in the Social Sciences and Humanities Conjoint analysis New right (Politics) Machine learning Parallel algorithms keyword: AMCE BART conjoint experiment heterogeneity AMCE BART conjoint experiment heterogeneity ab: Conjoint experiments are fast becoming one of the dominant experimental methods within the social sciences. Despite recent efforts to model heterogeneity within this type of experiment, the relationship between the conjoint design and lower-level causal estimands is underdeveloped. In this article, we clarify how conjoint heterogeneity can be construed as a set of nested, causal parameters that correspond to the levels of the conjoint design. We then use this framework to propose a new estimation strategy, using machine learning, that better allows researchers to evaluate treatment effect heterogeneity. We also provide novel tools for classifying and analyzing heterogeneity postestimation using partitioning algorithms. Replicating two conjoint experiments, we demonstrate our theoretical argument and show how this method helps estimate and detect substantive patterns of heterogeneity. To accompany this article, we provide new a R package, cjbart, that allows researchers to model heterogeneity in their experimental conjoint data. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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