Network Trees: A Method for Recursively Partitioning Covariance Structures

Payton J. Jones (), Patrick Mair, Thorsten Simon and Achim Zeileis
Additional contact information
Payton J. Jones: Harvard University
Patrick Mair: Harvard University
Thorsten Simon: Universität Innsbruck
Achim Zeileis: Universität Innsbruck

Psychometrika, 2020, vol. 85, issue 4, No 5, 926-945

Abstract: 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 analysis; correlation networks; recursive partitioning; decision trees; conditional inference (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s11336-020-09731-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:psycho:v:85:y:2020:i:4:d:10.1007_s11336-020-09731-4

Ordering information: This journal article can be ordered from
http://www.springer. ... gy/journal/11336/PS2

DOI: 10.1007/s11336-020-09731-4

Access Statistics for this article

Psychometrika is currently edited by Irini Moustaki

More articles in Psychometrika from Springer, The Psychometric Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().