A Note on Ising Network Analysis with Missing Data

Siliang Zhang () and Yunxiao Chen ()
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Siliang Zhang: East China Normal University
Yunxiao Chen: London School of Economics and Political Science

Psychometrika, 2024, vol. 89, issue 4, No 6, 1186-1202

Abstract: Abstract The Ising model has become a popular psychometric model for analyzing item response data. The statistical inference of the Ising model is typically carried out via a pseudo-likelihood, as the standard likelihood approach suffers from a high computational cost when there are many variables (i.e., items). Unfortunately, the presence of missing values can hinder the use of pseudo-likelihood, and a listwise deletion approach for missing data treatment may introduce a substantial bias into the estimation and sometimes yield misleading interpretations. This paper proposes a conditional Bayesian framework for Ising network analysis with missing data, which integrates a pseudo-likelihood approach with iterative data imputation. An asymptotic theory is established for the method. Furthermore, a computationally efficient Pólya–Gamma data augmentation procedure is proposed to streamline the sampling of model parameters. The method’s performance is shown through simulations and a real-world application to data on major depressive and generalized anxiety disorders from the National Epidemiological Survey on Alcohol and Related Conditions (NESARC).

Keywords: Ising model; iterative imputation; full conditional specification; network psychometrics; mental health disorders; major depressive disorder; generalized anxiety disorder (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11336-024-09985-2

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