Main content

  1. Thorsten Simon

Date created: | Last Updated:


Creating DOI. Please wait...

Create DOI

Category: Project

Description: 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.

License: CC-By Attribution 4.0 International

Has supplemental materials for Network Model Trees on OSF Preprints


Loading files...



Recent Activity

Loading logs...

OSF does not support the use of Internet Explorer. For optimal performance, please switch to another browser.
This website relies on cookies to help provide a better user experience. By clicking Accept or continuing to use the site, you agree. For more information, see our Privacy Policy and information on cookie use.

Start managing your projects on the OSF today.

Free and easy to use, the Open Science Framework supports the entire research lifecycle: planning, execution, reporting, archiving, and discovery.