Structural equation models (SEM) are frequently used in Information Systems (IS) to analyze and test theoretical propositions. As IS researchers frequently reuse measurement instruments and adapt or extend theories, it is not uncommon for a researcher to re-estimate regression relationships in their SEM that have been examined in previous studies. Bayesian statistics offer a statistically sound way to incorporate prior knowledge into SEM estimation, allowing researchers to keep a “running tally” of the best estimates of model parameters. This tutorial on the application of Bayesian principles to SEM estimation discusses when and why the use of Bayesian estimation should be considered by IS researchers, presents an illustrative example using best practices and makes recommendations to guide IS researchers in the application of Bayesian SEM.
JAGS (Just Another Gibbs Sampler) is an open-source implementation of the BUGS language for modeling and estimating Bayesian models. The tutorial was developed using version 3.4.0 but you should download the current version for your system.
OpenBUGS is another implementation ofthe BUGS language. The tutorial was developed using version 3.2.3 but you should download the current version for your system.
The R statistical software is a framework for general statistical computations. It can be extended using packages (libraries). Hundreds of packages exist for many different applications. R is open-source. The tutorial was developed using version 2.14.0 but you should download the current version for your system.
Sadly, Microsoft Windows does not come with a decent text editor. Notepad++ is a good open-source editor.
Everything you wanted to know about multi-level modeling. A very good introduction with lots of applications and code snippets. Also includes an easy introduction to Bayesian estimation.
This is a classic. Very comprehensive, but gentle, introduction to Bayesian estimation. This is a must-read for every student or researcher using Bayesian methods. A third edition is available (2013), which I have not yet read.
The reference to the WinBUGS software from the developers. If you're using WinBUGS (or OpenBUGS, or JAGS) for estimating Bayesian models, this is a must-read.
A very comprehensive book. Lee is very much the founder of this field, with many original contributions in the literature. This book is an integrated treatment of many of his articles.
Similar to the above item, a very comprehensive book. Again, an integrated treatment of many of his articles. Get either this or the above, there is significant overlap
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