Bayes Factor Tests for Group Differences in Ordinal and Binary Graphical Models
Maarten Marsman, Lourens Jan Waldorp, Nikola Sekulovski, Jonas Haslbeck
TL;DR
This paper introduces Bayes factor tests to compare network structures between groups using binary or ordinal data, with an implementation in an R package.
Contribution
The paper introduces Bayes factor tests for assessing group differences in networks of binary or ordinal variables.
Findings
The proposed Bayes factor tests are implemented in the R package bgms.
Numerical illustrations demonstrate the correctness and performance of the methods in realistic scenarios.
Abstract
Multivariate analysis of psychological variables using graphical models has become a standard analysis in the psychometric literature. Most cross-sectional measures are either binary or ordinal, and the methodology for inferring the structure of networks of binary and ordinal variables is developing rapidly. In practice, however, research questions often focus on whether and how networks differ between observed groups. While Bayes factor methods for inferring network structure are well established, a similar methodology for assessing group differences in networks of binary or ordinal variables is currently lacking. In this article, we extend the Bayesian framework for the analysis of ordinal Markov random fields, a network model for binary and ordinal variables, and develop Bayes factor tests for assessing parameter differences in the networks of two independent groups. The proposed…
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Taxonomy
TopicsMental Health Research Topics · Psychometric Methodologies and Testing · Sensory Analysis and Statistical Methods