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Mapping Hedonic Data: A Process Perspective
Author(s) -
Ennis Daniel M.,
Ennis John M.
Publication year - 2013
Publication title -
journal of sensory studies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.61
H-Index - 53
eISSN - 1745-459X
pISSN - 0887-8250
DOI - 10.1111/joss.12048
Subject(s) - multivariate statistics , perspective (graphical) , computer science , probabilistic logic , preference , process (computing) , variety (cybernetics) , multivariate analysis , data science , econometrics , data mining , management science , psychology , machine learning , artificial intelligence , statistics , mathematics , economics , operating system
Multivariate analyses are commonly used to study differences among items in a multidimensional space and to relate these findings to hedonic assessments of the same items. But there are numerous methods in use and the purpose of this article is to review these methods from a process standpoint. Specifically, this article considers the process assumptions behind several of the popular methods for multivariate mapping of hedonic data and argues that experimenters should consider how their data arise so that they can correctly interpret their findings. Among the methods considered in this article are models based on the hedonic continuum, internal and external preference mapping, and deterministic and probabilistic unfolding of preference and liking. Practical Applications Multivariate mapping of hedonic data has led to improved consumer products and a better understanding of consumer liking and choice. In this article, practitioners will find guidance in their choice of methods through a consideration of the processes that generate their data. Without a process‐based perspective, practitioners will not be able to optimally interpret results from the wide variety of available multivariate mapping methods.