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

Psychometrika, Volume 59, Issue 4, pp 527-566

40 Pages Posted: 6 Jun 2016

See all articles by Wayne S. DeSarbo

Wayne S. DeSarbo

Pennsylvania State University

Rabikar Chatterjee

University of Pittsburgh

Juyoung Kim

Michigan State University

Date Written: December 1994

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 clustering, maximum likelihood estimation, familiarity, consumer psychology

Suggested Citation

DeSarbo, Wayne S. and Chatterjee, Rabikar and Kim, Juyoung, Deriving Ultrametric Tree Structures from Proximity Data Confounded by Differential Stimulus Familiarity (December 1994). Psychometrika, Volume 59, Issue 4, pp 527-566, Available at SSRN: https://ssrn.com/abstract=2789678

Wayne S. DeSarbo (Contact Author)

Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

Rabikar Chatterjee

University of Pittsburgh ( email )

135 N Bellefield Ave
Pittsburgh, PA 15260
United States

Juyoung Kim

Michigan State University ( email )

Agriculture Hall
East Lansing, MI 48824-1122
United States

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