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
Bayes with Informed Priors Based on Literature and Expert Elicitation
Bayesian Trajectory Analysis with Informed Priors Based on a Systematic Literature Search and Expert Elicitation in the field of Post Traumatic Stress.
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.
A Systematic Review of Bayesian Papers in Psychology: The Last 25 Years
Although the statistical tools most often used by researchers in the field of psychology over the last 25 years are based on frequentist statistics, it is often claimed that the alternative Bayesian approach to statistics is gaining in popularity.
The GRoLTS-Checklist: Guidelines for Reporting on Latent Trajectory Studies
Estimating models within the mixture model framework, like latent growth mixture modeling (LGMM) or latent class growth analysis (LCGA), involves making various decisions throughout the estimation process. This has led to a wide variety in how results of latent trajectory analysis are reported.
Presentation about GRoLTS-Checklist
At the Meeting of the Working Group SEM (16 and 17 March 2017; Ghent, Belgium) I will present about the Guidelines for Reporting on Latent Trajectory Studies. March 16 at 14:15-14:45 Abstract: Estimating models within the mixture model framework, like Latent Growth Mixture Modeling (LGMM) or Latent Class…
Possible Solution to Publication Bias Through Bayesian Statistics
The present paper argues that an important cause of publication bias resides in traditional frequentist statistics forcing binary decisions. An alternative approach through Bayesian statistics provides various degrees of support for any hypothesis allowing balanced decisions and proper null hypothesis testing, which may prevent publication bias.