Tag: Prior

Improving Transparency and Replication in Bayesian Statistics: The WAMBS-Checklist

Bayesian statistical methods are slowly creeping into all fields of science and are becoming ever more popular in applied research. Although it is very attractive to use Bayesian statistics, our personal experience has led us to believe that naively applying Bayesian methods can be dangerous for at least 3 main reasons:

Testing Small Variance Priors Using Prior-Posterior Predictive P-values

Muthen and Asparouhov (2012) propose to evaluate model fit in structural equation models based on approximate (using small variance priors) instead of exact equality of (combinations of) parameters to zero. This is an important development that adequately addresses Cohen’s (1994) “The earth is round (p < .05)”, which stresses that point null-hypotheses are so precise that small and irrelevant differences from the null-hypothesis may lead to their rejection.

Analyzing small data sets using Bayesian estimation: the case of posttraumatic stress symptoms following mechanical ventilation in burn survivors

The analysis of small data sets in longitudinal studies can lead to power issues and often suffers from biased parameter values. These issues can be solved by using Bayesian estimation in conjunction with informative prior distributions.

Facing off with Scylla and Charybdis: a comparison of scalar, partial, and the novel possibility of approximate measurement invariance

Measurement invariance (MI) is a pre-requisite for comparing latent variable scores across groups. The current paper introduces the concept of approximate MI building on the work of Muthén and Asparouhov and their application of Bayesian Structural Equation Modeling (BSEM) in the software Mplus.