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A Note on Ising Network Analysis with Missing Data

Author

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  • Siliang Zhang

    (East China Normal University)

  • Yunxiao Chen

    (London School of Economics and Political Science)

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).

Suggested Citation

  • Siliang Zhang & Yunxiao Chen, 2024. "A Note on Ising Network Analysis with Missing Data," Psychometrika, Springer;The Psychometric Society, vol. 89(4), pages 1186-1202, December.
    • Handle: RePEc:spr:psycho:v:89:y:2024:i:4:d:10.1007_s11336-024-09985-2
    DOI: 10.1007/s11336-024-09985-2
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    References listed on IDEAS

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