Problem In my power simulation of the U-shape experiment, I noticed that the suggested priors (click here) actually do not work very well for Bayes Factor (BF) analysis as they are too flat and hence BF are too conservative. That is because uniform or very flat priors lead to less evidence in favour of the alternative hypothesis.
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This post is based on a short talk that I gave at the MRC CBU on the 29 July 2020 for a session on statistical power. The slides of presentation can be found in my GitHub repository. This repository also includes the data and the scripts that were used to run the simulations and to create the figures.
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Introduction Comparing the four BF methods Evidence for null hypothesis: brms vs. ttestBF() How much more conservative is the bmrs approach? Directed vs. non-directed hypothesis Conclusion Introduction In this post, I’d like to examine how different ways to calcualte Bayes factors (BF) compare with each other for a simple model.
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Aim of this document The aim of this document is to accompany the design analysis for noveltyVR (see here) and to shed light on the deliberations concerning the planned analysis.
The planned design is a 2 x 2 x 2 design with one between and two within subject factors. There will be a novelty and a control group (Factor N) and we will examine recollection/familiarity (Factor M) for weakly/strongly learned words (Factor E).
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Preface Note: This is an old post from my early PhD time that I slightly edited but share again because it would be interesting to one or two people.
This work documents my attempt to understand the model structure of mixed linear models or multilevel models. I adapted a formula that I found on Wikipedia and work myself through the different models and test whether I am able to retrieve the different parameters with lmerTest.
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