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Does nature have joints worth carving? A discussion of taxometrics, model-based clustering and latent variable mixture modeling

Published online by Cambridge University Press:  19 August 2014

G. H. Lubke  and
P. J. Miller
G. H. Lubke*
Affiliation:
Department of Psychology, University of Notre Dame, IN, USA Department of Biological Psychology, VU University Amsterdam, The Netherlands
P. J. Miller
Affiliation:
Department of Psychology, University of Notre Dame, IN, USA
*
*Address for correspondence: G. H. Lubke, Ph.D., Department of Psychology, University of Notre Dame, 111 Haggar Hall, Notre Dame, IN 46556, USA. (Email: glubke@nd.edu)
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Abstract

Taxometric procedures, model-based clustering and latent variable mixture modeling (LVMM) are statistical methods that use the inter-relationships of observed symptoms or questionnaire items to investigate empirically whether the underlying psychiatric or psychological construct is dimensional or categorical. In this review we show why the results of such an investigation depend on the characteristics of the observed symptoms (e.g. symptom prevalence in the sample) and of the sample (e.g. clinical, population sample). Furthermore, the three methods differ with respect to their assumptions and therefore require different types of a priori knowledge about the observed symptoms and their inter-relationships. We argue that the choice of method should optimally match and make use of the existing knowledge about the data that are analyzed.

Keywords

Factor mixture modelingheterogeneitytaxometrics
Type
Review Articles
Information
Psychological Medicine , Volume 45 , Issue 4 , March 2015 , pp. 705 - 715
Copyright
Copyright © Cambridge University Press 2014 

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