You should head over to PLOS to read this paper by Jonah Stunt et al. It’s the first qualitative study I’ve come across at PLOS, but it’s definitely worth a read to better understand why we’re still surrounded by p-values.
One thing I missed in the paper is a hint that we don’t have to engage in frequentists null-hypothesis significance testing. I realize that the authors are interested in the sociology of science here, but we have plenty of statements in the article how difficult it’d be to learn about alternative methods. It doesn’t have to be: We do have packages like rstanarm or software like JASP that do not leave much room for such excuses.
Stunt, Jonah, Leonie van Grootel, Lex Bouter, David Trafimow, Trynke Hoekstra, and Michiel de Boer. 2021. “Why We Habitually Engage in Null-Hypothesis Significance Testing: A Qualitative Study.” PLOS ONE 16(10):e0258330. doi: 10.1371/journal.pone.0258330.
There really is no excuse any more: getting started with Bayesian regression analysis in R is really simple.
Step 1: install rstanarm from CRAN
Step 2: replace lm() with stan_glm() in your code
Sure, you’ll probably want to learn about priors, and invest a little in understanding diagnostics such as those provided by ShinyStan. But rstanarm is really designed to work well out of the box (i.e. with your existing code).
What I really appreciate is that it has useful warnings and error messages, and extensive documentation. Sometimes the documentation shows that quantitative analysis has something to do with mathematics, but even those who skip the Greek letters and formulae will get enough guidance. You’ll get nudges to use your own priors rather than rely on the default priors, but in my experience for most simple applications the default priors work reasonably well. You’ll also get suggestions right on your screen what you can do when there are say divergent transitions.
Once you can handle rstanarm, you’ll find it easy to upgrade to brms, where you can still use your trusted syntax for regression models in base R.