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A Note on Ising Network Analysis with Missing Data.
- Source :
-
Psychometrika [Psychometrika] 2024 Jul 06. Date of Electronic Publication: 2024 Jul 06. - Publication Year :
- 2024
- Publisher :
- Ahead of Print
-
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).<br /> (© 2024. The Author(s).)
Details
- Language :
- English
- ISSN :
- 1860-0980
- Database :
- MEDLINE
- Journal :
- Psychometrika
- Publication Type :
- Academic Journal
- Accession number :
- 38971882
- Full Text :
- https://doi.org/10.1007/s11336-024-09985-2