marcdotson / conjoint-ensembles

Using clever randomization and ensembling strategies to accommodate multiple data pathologies in conjoint studies.

Accommodating Data Pathologies in Conjoint Studies

Abstract

Respondent behavior in conjoint studies often deviates from the assumptions of random utility theory. We refer to deviations from normative choice behavior as data pathologies. A variety of models have been developed that attempt to correct for specific pathologies (i.e., screening rules, respondent quality, attribute non-attendance, etc.). While useful, these approaches tend to be both conceptually complex and computational intensive. As such, these approaches have not widely diffused into the practice of marketing research. In this paper we draw on innovations in machine learning to develop a practical approach that relies on (clever) randomization strategies and ensembling to simultaneously accommodate multiple data pathologies in a single model. We provide tips and tricks on how to implement this approach in practice.

Project Organization

• /code Scripts with prefixes (e.g., 01_import-data.R, 02_clean-data.R) and functions in /code/src.
• /data Simulated and real data, the latter not pushed.
• /figures PNG images and plots.
• /output Output from model runs, not pushed.
• /presentations Presentation slides, without knitted PDFs pushed.
• /private A catch-all folder for miscellaneous files, not pushed.
• /renv Project library, once initialized (see below).
• /writing Case studies and the paper, without its knitted PDF pushed.
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Reproducible Environment

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