I have started this Bayesian trip over a month ago. It was a real journey. I started out as a NHST trained organizational psychologist, so everything was very new and to me. I/O psychology has seen virtually no, or very small Bayesian based empirical papers.
I have read pretty much every introductory book and semi-advanced manuals from JASP to BMRS, and I am pretty close to conducting my first real-world-data analysis, write-up and so on. I am close, but i still have a few closing questions, that are technical in nature…
Issue at hand: I am predicting the performance of work groups as a function of the personality traits of their members controlled for the potential effects of the group size, and i need a parameter estimation.
My model is then:
fit1 <- brm(data = team.data, family = gaussian, performance ~ perceived_group_size + machiaviellism, warmup = 10000, iter = 20000, chains = 4, cores=4, seed = 1)
The default prior is a Student’s t: it has wider tails than the normal distribution. This makes it very useful for modeling small samples or data with outliers (i.e., “robust” analyses); Jebb, 2015, at DOI: 10.1177/1094428114553060. Perfect for me. I have only about 100 teams.
This however, won’t do 100%. I will need a prior prediction check. This is where I am stumped, as I cannot get it into my head how to do this practically. What hyperparameters should i enter? Are they dependent on the scaling of the predictor (machiavelism is a likert scale 1 to 4), or dependent on the outcome variable: performance is Likert 1-5, or should I center the predictor?
Is then normal(2, 1) a normal distribution of mean 2 and of sd = 1? But then a normal( Or, should i center the predictor so that a 'cauchy (0, 0.707) is actually a distribution of mean 0, with SD = 0.707? Or is that done automatically and i just have to enter "cauchy (0, 0.707)?
fit1 <- update(fit0, prior = prior(normal(2, 1), class = Intercept), seed = 1)
And finally, just a minor question for my peace of mind: the JASP vs BRMS approach to linear regression. Why is JASP comparing models to decide, and why is BRMS more parameter oriented? I feel as a novice, pretty confusing.
Greetings from the quarantine,
- Operating System: WIN10
- brms Version: 2.12.0