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Deriving Ultrametric Tree Structures from Proximity Data Confounded by Differential Stimulus Familiarity

Published online by Cambridge University Press:  01 January 2025

Wayne S. DeSarbo,
Rabikar Chatterjee and
Juyoung Kim
Wayne S. DeSarbo*
Affiliation:
School of Business Administration, The University of Michigan
Rabikar Chatterjee
Affiliation:
School of Business Administration, The University of Michigan
Juyoung Kim
Affiliation:
School of Business Administration, The University of Michigan
*
Requests for reprints should be directed to Wayne S. DeSarbo, School of Business Administration, The University of Michigan, Ann Arbor, MI 48109-1234.
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Abstract

This paper presents a new procedure called TREEFAM for estimating ultrametric tree structures from proximity data confounded by differential stimulus familiarity. The objective of the proposed TREEFAM procedure is to quantitatively “filter out” the effects of stimulus unfamiliarity in the estimation of an ultrametric tree. A conditional, alternating maximum likelihood procedure is formulated to simultaneously estimate an ultrametric tree, under the unobserved condition of complete stimulus familiarity, and subject-specific parameters capturing the adjustments due to differential unfamiliarity. We demonstrate the performance of the TREEFAM procedure under a variety of alternative conditions via a modest Monte Carlo experimental study. An empirical application provides evidence that the TREEFAM outperforms traditional models that ignore the effects of unfamiliarity in terms of superior tree recovery and overall goodness-of-fit.

Keywords

hierarchical clusteringmaximum likelihood estimationfamiliarityconsumer psychology

Information

Type
Original Paper
Information
Psychometrika , Volume 59 , Issue 4 , December 1994, pp. 527 - 566
Copyright
Copyright © 1994 The Psychometric Society

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