Statistical properties of the single mediator model with latent variables in the bayesian framework

https://keep.lib.asu.edu/_flysystem/fedora/c7/179369/Miocevic_asu_0010E_16679.pdf

The R2WinBUGS package provides convenient functions to call WinBUGS from R. It automatically writes the data and scripts in a format readable by WinBUGS for processing in batch mode, which is possible since version 1.4. After the WinBUGS process has finished, it is possible either to read the resulting data into R by the package itself—which gives a compact graphical summary of inference and convergence diagnostics—or to use the facilities of the coda package for further analyses of the output. Examples are given to demonstrate the usage of this package.

/pdf/r2winbugs-a-package-for-running-winbugs-from-r-voz5bl8rbp.pdf

R2WinBUGS: A Package for Running WinBUGS from R

Methods to assess the significance of mediated effects in education and the social sciences are well studied and fall into two categories: single sample methods and computer-intensive methods. A popular single sample method to detect the significance of the mediated effect is the test of joint significance, and a popular computer-intensive method to detect the significance of the mediated effect is the bias-corrected bootstrap method. Both these methods are used for testing the significance of mediated effects in structural equation models (SEMs). A recent study by Leth-Steensen and Gallitto 2015 provided evidence that the test of joint significance was more powerful than the bias-corrected bootstrap method for detecting mediated effects in SEMs, which is inconsistent with previous research on the topic. The goal of this article was to investigate this surprising result and describe two issues related to testing the significance of mediated effects in SEMs which explain the inconsistent results regarding the power of the test of joint significance and the bias-corrected bootstrap found by Leth-Steensen and Gallitto 2015. The first issue was that the bias-corrected bootstrap method was conducted incorrectly. The bias-corrected bootstrap was used to estimate the standard error of the mediated effect as opposed to creating confidence intervals. The second issue was that the correlation between the path coefficients of the mediated effect was ignored as an important aspect of testing the significance of the mediated effect in SEMs. The results of the replication study confirmed prior research on testing the significance of mediated effects. That is, the bias-corrected bootstrap method was more powerful than the test of joint significance, and the bias-corrected bootstrap method had elevated Type 1 error rates in some cases. Additional methods for testing the significance of mediated effects in SEMs were considered and limitations and future directions were discussed.

http://journals.sagepub.com/doi/pdf/10.1177/0013164415618992

A Note on Testing Mediated Effects in Structural Equation Models: Reconciling Past and Current Research on the Performance of the Test of Joint Significance.

Statistical properties of the single mediator model with latent variables in the bayesian framework

References

R2WinBUGS: A Package for Running WinBUGS from R

A Note on Testing Mediated Effects in Structural Equation Models: Reconciling Past and Current Research on the Performance of the Test of Joint Significance.

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