Category: Expert Data (Dis)agreement (EDDA)

Dealing with imperfect elicitation results

We provide an overview of the solutions we used for dealing with imperfect elicitation results, so that others can benefit from our experience. We present information about the nature of our project, the reasons for the imperfect results, and how we resolved these sup-ported by annotated R-syntax

Choice of Distance Measure Influences the Detection of Prior-Data Conflict

The present paper contrasts two related criteria for the evaluation of prior-data conflict: the Data Agreement Criterion (DAC; Bousquet, 2008) and the criterion of Nott et al. (2016). We investigated how the choice of a specific distance measure influences the detection of prior-data conflict.

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.

Bayesian PTSD-Trajectory Analysis with Informed Priors

we illustrate how to obtain background information using previous literature in the field of PTSD based on a systematic literature search and by using expert knowledge. Finally, we show how to translate this knowledge into prior distributions and we illustrate how to run a Bayesian LGMM.