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Bayesian Nonlinear Structural Equation Modeling
, 2015 Structural Equation Modeling (SEM) is a multivariate method that incorporates ideas from regression, path analysis and factor analysis. SEM has been widely applied in examine inter-relationships among latent and observed variables in social, psychological, and medical research.
Altındağ, İlkay, Genç, Aşır
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Bayesian Structural Equation Modeling in Sport and Exercise Psychology
Journal of Sport and Exercise Psychology, 2015 Bayesian statistics is on the rise in mainstream psychology, but applications in sport and exercise psychology research are scarce. In this article, the foundations of Bayesian analysis are introduced, and we will illustrate how to apply Bayesian structural equation modeling in a sport and exercise psychology setting. More specifically, we contrasted a
Andreas, Stenling +3 more
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Bayesian Lasso for Semiparametric Structural Equation Models [PDF]
Biometrics, 2012 SummaryThere has been great interest in developing nonlinear structural equation models and associated statistical inference procedures, including estimation and model selection methods. In this paper a general semiparametric structural equation model (SSEM) is developed in which the structural equation is composed of nonparametric functions of ...
Guo, Ruixin +3 more
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Bayesian Regularized SEM: Current Capabilities and Constraints
Psych, 2023 An important challenge in statistical modeling is to balance how well our model explains the phenomenon under investigation with the parsimony of this explanation.
Sara van Erp
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Planning and performance in teams: A Bayesian meta-analytic structural equation modeling approach.
PLoS ONE, 2023 We meta-analyzed the relationship between team planning and performance moderated by task, team, context, and methodological factors. For testing our hypothesized model, we used a meta-analytic structural equation modeling approach.
Udo Konradt +2 more
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Contributions to Bayesian Structural Equation Modeling [PDF]
, 2010 Structural equation models (SEMs) are multivariate latent variable models used to model causality structures in data. A Bayesian estimation and validation of SEMs is proposed and identifiability of parameters is studied. The latter study shows that latent variables should be standardized in the analysis to ensure identifiability.
Demeyer, Séverine +2 more
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Structural Equation Modeling of Vocabulary Size and Depth Using Conventional and Bayesian Methods
Frontiers in Psychology, 2020 In classifications of vocabulary knowledge, vocabulary size and depth have often been separately conceptualized (Schmitt, 2014). Although size and depth are known to be substantially correlated, it is not clear whether they are a single construct or two ...
Rie Koizumi, Yo In’nami
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, 2021 Supplemental Materials for a Manuscript Titled: "Performance of Model Fit and Selection Indices for Bayesian Structural Equation ...
Sarah Depaoli, Sonja Désirée Winter
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A Bayesian Modeling Approach for Generalized Semiparametric Structural Equation Models [PDF]
Psychometrika, 2013 In behavioral, biomedical, and psychological studies, structural equation models (SEMs) have been widely used for assessing relationships between latent variables. Regression-type structural models based on parametric functions are often used for such purposes.
Song, Xin-Yuan +3 more
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Parameter Estimation of Structural Equation Modeling Using Bayesian Approach
Cauchy: Jurnal Matematika Murni dan Aplikasi, 2016 Leadership is a process of influencing, directing or giving an example of employees in order to achieve the objectives of the organization and is a key element in the effectiveness of the organization.
Dewi Kurnia Sari +2 more
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