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Network Trees: A Method for Recursively Partitioning Covariance Structures

Published online by Cambridge University Press:  01 January 2025

Payton J. JonesOpen the ORCID record for Payton J. Jones[Opens in a new window],
Patrick MairOpen the ORCID record for Patrick Mair[Opens in a new window],
Thorsten SimonOpen the ORCID record for Thorsten Simon[Opens in a new window] and
Achim ZeileisOpen the ORCID record for Achim Zeileis[Opens in a new window]
Payton J. Jones*
Affiliation:
Harvard University
Patrick Mair
Affiliation:
Harvard University
Thorsten Simon
Affiliation:
Universität Innsbruck
Achim Zeileis
Affiliation:
Universität Innsbruck
*
Correspondence should be made to Payton J. Jones, Harvard University, Cambridge, MA, USA. Email: paytonjjones@gmail.com, payton_jones@g.harvard.edu; URL: https://scholar.harvard.edu/paytonjones/home
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Abstract

In many areas of psychology, correlation-based network approaches (i.e., psychometric networks) have become a popular tool. In this paper, we propose an approach that recursively splits the sample based on covariates in order to detect significant differences in the structure of the covariance or correlation matrix. Psychometric networks or other correlation-based models (e.g., factor models) can be subsequently estimated from the resultant splits. We adapt model-based recursive partitioning and conditional inference tree approaches for finding covariate splits in a recursive manner. The empirical power of these approaches is studied in several simulation conditions. Examples are given using real-life data from personality and clinical research.

Keywords

network analysiscorrelation networksrecursive partitioningdecision treesconditional inference

Information

Type
Theory and Methods
Information
Psychometrika , Volume 85 , Issue 4 , December 2020, pp. 926 - 945
Copyright
Copyright © 2020 The Psychometric Society

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