A note on Ising network analysis with missing data

Siliang Zhang and Yunxiao Chen

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 17 pages
Date: 2024-12-31
New Economics Papers: this item is included in nep-ecm
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Published in Psychometrika, 31, December, 2024, 89(4), pp. 1186 - 1202. ISSN: 0033-3123

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:123984

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