Assessment of generalised Bayesian structural equation models for continuous and binary data

Vamvourellis, Konstantinos, Kalogeropoulos, Konstantinos ORCID: 0000-0002-0330-9105 and Moustaki, Irini ORCID: 0000-0001-8371-1251 (2023) Assessment of generalised Bayesian structural equation models for continuous and binary data. British Journal of Mathematical and Statistical Psychology, 76 (3). 559 - 584. ISSN 0007-1102

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Abstract

The paper proposes a novel model assessment paradigm aiming to address shortcoming of posterior predictive (Figure presented.) -values, which provide the default metric of fit for Bayesian structural equation modelling (BSEM). The model framework presented in the paper focuses on the approximate zero approach (Psychological Methods, 17, 2012, 313), which involves formulating certain parameters (such as factor loadings) to be approximately zero through the use of informative priors, instead of explicitly setting them to zero. The introduced model assessment procedure monitors the out-of-sample predictive performance of the fitted model, and together with a list of guidelines we provide, one can investigate whether the hypothesised model is supported by the data. We incorporate scoring rules and cross-validation to supplement existing model assessment metrics for BSEM. The proposed tools can be applied to models for both continuous and binary data. The modelling of categorical and non-normally distributed continuous data is facilitated with the introduction of an item-individual random effect. We study the performance of the proposed methodology via simulation experiments as well as real data on the ‘Big-5’ personality scale and the Fagerstrom test for nicotine dependence.

Item Type:	Article
Official URL:	https://bpspsychub.onlinelibrary.wiley.com/journal...
Additional Information:	© 2023 The Authors
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics Q Science > QA Mathematics
Date Deposited:	22 Jun 2023 23:24
Last Modified:	18 Nov 2024 18:09
URI:	http://eprints.lse.ac.uk/id/eprint/119473

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