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Bayesian Inference of Dynamic Mediation Models for Longitudinal Data
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Saijun Zhao; Zhiyong Zhang; Hong Zhang – Grantee Submission, 2024
Mediation analysis is widely applied in various fields of science, such as psychology, epidemiology, and sociology. In practice, many psychological and behavioral phenomena are dynamic, and the corresponding mediation effects are expected to change over time. However, most existing mediation methods assume a static mediation effect over time,…
Descriptors: Bayesian Statistics, Statistical Inference, Longitudinal Studies, Attribution Theory
Bayesian Inference of Dynamic Mediation Models for Longitudinal Data
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Saijun Zhao; Zhiyong Zhang; Hong Zhang – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Mediation analysis is widely applied in various fields of science, such as psychology, epidemiology, and sociology. In practice, many psychological and behavioral phenomena are dynamic, and the corresponding mediation effects are expected to change over time. However, most existing mediation methods assume a static mediation effect over time,…
Descriptors: Bayesian Statistics, Statistical Inference, Longitudinal Studies, Attribution Theory
A New Bayesian Structural Equation Modeling Approach with Priors on the Covariance Matrix Parameter
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Haiyan Liu; Wen Qu; Zhiyong Zhang; Hao Wu – Grantee Submission, 2022
Bayesian inference for structural equation models (SEMs) is increasingly popular in social and psychological sciences owing to its flexibility to adapt to more complex models and the ability to include prior information if available. However, there are two major hurdles in using the traditional Bayesian SEM in practice: (1) the information nested…
Descriptors: Bayesian Statistics, Structural Equation Models, Statistical Inference, Statistical Distributions