scholarly journals Prior sensitivity analysis in default Bayesian structural equation modeling.

2018 ◽  
Vol 23 (2) ◽  
pp. 363-388 ◽  
Author(s):  
Sara van Erp ◽  
Joris Mulder ◽  
Daniel L. Oberski
Keyword(s):  
Sensitivity Analysis ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Equation Modeling ◽  
Bayesian Structural Equation Modeling

scholarly journals Prior sensitivity analysis in default Bayesian structural equation modeling

2017 ◽  
Author(s):  
Sara van Erp ◽  
Joris Mulder ◽  
Daniel L. Oberski
Keyword(s):  
Sensitivity Analysis ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Empirical Bayes ◽  
Mean Squared Error ◽  
Small Samples ◽  
Equation Modeling ◽  
Model Parameters ◽  
Ml Estimation ◽  
Bayesian Structural Equation Modeling

Bayesian structural equation modeling (BSEM) has recently gained popularity because it enables researchers to fit complex models while solving some of the issues often encountered in classical maximum likelihood (ML) estimation, such as nonconvergence and inadmissible solutions. An important component of any Bayesian analysis is the prior distribution of the unknown model parameters. Often, researchers rely on default priors, which are constructed in an automatic fashion without requiring substantive prior information. However, the prior can have a serious influence on the estimation of the model parameters, which affects the mean squared error (MSE), bias, coverage rates, and quantiles of the estimates.In this paper, we investigate the performance of three different default priors: noninformative improper priors, vague proper priors, and empirical Bayes priors, with the latter being novel in the BSEM literature. Based on a simulation study, we find that these three default BSEM methods may perform very differently, especially with small samples. A careful prior sensitivity analysis is therefore needed when performing a default BSEM analysis. For this purpose, we provide a practical step-by-step guide for practitioners to conducting a prior sensitivity analysis in default BSEM. Our recommendations are illustrated using a well-known case study from the structural equation modeling literature and all code for conducting the prior sensitivity analysis is made available in the online supplemental material.

Further Insights Into the German Version of the Multidimensional Assessment of Interoceptive Awareness (MAIA)

2019 ◽  
Vol 35 (3) ◽  
pp. 317-325 ◽  
Author(s):  
Dorota Reis
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Equation Modeling ◽  
Interoceptive Awareness ◽  
Multidimensional Assessment ◽  
New Instrument ◽  
Confirmatory Factor ◽  
Complex Models ◽  
Approximate Zero ◽  
Bayesian Structural Equation Modeling

Abstract. Interoception is defined as an iterative process that refers to receiving, accessing, appraising, and responding to body sensations. Recently, following an extensive process of development, Mehling and colleagues (2012) proposed a new instrument, the Multidimensional Assessment of Interoceptive Awareness (MAIA), which captures these different aspects of interoception with eight subscales. The aim of this study was to reexamine the dimensionality of the MAIA by applying maximum likelihood confirmatory factor analysis (ML-CFA), exploratory structural equation modeling (ESEM), and Bayesian structural equation modeling (BSEM). ML-CFA, ESEM, and BSEM were examined in a sample of 320 German adults. ML-CFA showed a poor fit to the data. ESEM yielded a better fit and contained numerous significant cross-loadings, of which one was substantial (≥ .30). The BSEM model with approximate zero informative priors yielded an excellent fit and confirmed the substantial cross-loading found in ESEM. The study demonstrates that ESEM and BSEM are flexible techniques that can be used to improve our understanding of multidimensional constructs. In addition, BSEM can be seen as less exploratory than ESEM and it might also be used to overcome potential limitations of ESEM with regard to more complex models relative to the sample size.

scholarly journals Investigating the theoretical structure of the DAS-II core battery at school age using Bayesian structural equation modeling

2017 ◽  
Vol 55 (2) ◽  
pp. 190-207 ◽  
Author(s):  
Stefan C. Dombrowski ◽  
Philippe Golay ◽  
Ryan J. McGill ◽  
Gary L. Canivez
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Equation Modeling ◽  
School Age ◽  
Theoretical Structure ◽  
Bayesian Structural Equation Modeling

Using Ant Colony Optimization for Sensitivity Analysis in Structural Equation Modeling

2021 ◽  
pp. 1-10
Author(s):  
Walter L. Leite ◽  
Zuchao Shen ◽  
Katerina Marcoulides ◽  
Charles L. Fisk ◽  
Jeffrey Harring
Keyword(s):  
Sensitivity Analysis ◽  
Structural Equation Modeling ◽  
Ant Colony Optimization ◽  
Structural Equation ◽  
Ant Colony ◽  
Equation Modeling

scholarly journals Bayesian Structural Equation Modeling of the WISC-IV With a Large Referred U.S. Sample

2014 ◽  
Author(s):  
Philippe Golay ◽  
Thierry Lecerf ◽  
Marley W. Watkins ◽  
Gary L. Canivez
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Equation Modeling ◽  
Bayesian Structural Equation Modeling

scholarly journals Tonic immobility is a measure of boldness toward predators: an application of Bayesian structural equation modeling

2012 ◽  
Vol 23 (3) ◽  
pp. 619-626 ◽  
Author(s):  
Pim Edelaar ◽  
David Serrano ◽  
Martina Carrete ◽  
Julio Blas ◽  
Jaime Potti ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Tonic Immobility ◽  
Equation Modeling ◽  
Bayesian Structural Equation Modeling

scholarly journals Airline Sustainability Modeling: A New Framework with Application of Bayesian Structural Equation Modeling

2016 ◽  
Vol 8 (11) ◽  
pp. 1204 ◽  
Author(s):  
Hashem Salarzadeh Jenatabadi ◽  
Peyman Babashamsi ◽  
Datis Khajeheian ◽  
Nader Seyyed Amiri
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Equation Modeling ◽  
Bayesian Structural Equation Modeling ◽  
New Framework
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