Network trees: a method for recursively partitioning covariance structures

DOI10.1007/s11336-020-09731-4zbMath1477.62344OpenAlexW3095673925WikidataQ101220680 ScholiaQ101220680MaRDI QIDQ2065251

Patrick Mair, Thorsten Simon, Achim Zeileis, Payton J. Jones

Publication date: 7 January 2022

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11336-020-09731-4

zbMATH Keywords

decision treesrecursive partitioningnetwork analysisconditional inferencecorrelation networks

Mathematics Subject Classification ID

Applications of statistics to psychology (62P15)

Related Items (2)

Guest editors' introduction to the special issue ``Network psychometrics in action: methodological innovations inspired by empirical problems ⋮ A note on the structural change test in highly parameterized psychometric models

Uses Software

• R
• glasso
• GeneNet
• partykit
• qgraph
• lvnet
• Onyx
• aspect
• networktree

Cites Work

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• Sparse inverse covariance estimation with the graphical lasso
• Testing for measurement invariance with respect to an ordinal variable
• Rasch trees: a new method for detecting differential item functioning in the Rasch model
• Implementing a class of structural change tests: an econometric computing approach
• Generalized network psychometrics: combining network and latent variable models
• The asymptotic theory of permutation statistics.
• Tests of measurement invariance without subgroups: a generalization of classical methods
• Detecting structural changes in longitudinal network data
• Tests for Parameter Instability and Structural Change With Unknown Change Point
• Generalized M‐fluctuation tests for parameter instability
• Tests For Constancy Of Model Parameters Over Time

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