I think I accidentally ordered the reading recommendations by order. Most of them are books but you don't need to read all of them or even the entire book. The fundamentals are typically covered in the first few chapters and more advanced stuff that may be less interesting to you is in the end. You choose what you want to learn :)
This one is a very conceptual introduction and is super easy to understand. Don't let the title put you off, it is definitely a serious introduction. Maths is minimal and typically explained with enough detail. Also, it's a very short book and you will get easily through it:
https://nostarch.com/learnbayes
This book gives you a good foundation of Bayesian data analysis with R code that you can run to understand example. In that sense this book is great to learn R while learning Bayesian statistics. It's quite long because of the code examples so you need to pick what you want to read.
https://nyu-cdsc.github.io/learningr/assets/kruschke_bayesian_in_R.pdf
This book is quite similar to the previous one but nicer to read, I think. It's a little bit more technical but still easy to understand, I think.
https://xcelab.net/rm/statistical-rethinking/
This book is closer to the statistical methods you might be using and you're familiar with from year 1 and 2. It involves a lot of regression modelling which is the answer to the statistical problems that most psychologist face. If I remember correctly the later half of the book is mostly dedicated to intervention studies, so even though the book is longer you can easily ignore almost half of it. The first couple of chapters are really important.
https://avehtari.github.io/ROS-Examples/
This book doesn't teach you Bayesian statistics but I thoroughly enjoyed it and it makes you think about contemporary problems in empirical disciplines.
I think I accidentally ordered the reading recommendations by order. Most of them are books but you don't need to read all of them or even the entire book. The fundamentals are typically covered in the first few chapters and more advanced stuff that may be less interesting to you is in the end. You choose what you want to learn :)
This one is a very conceptual introduction and is super easy to understand. Don't let the title put you off, it is definitely a serious introduction. Maths is minimal and typically explained with enough detail. Also, it's a very short book and you will get easily through it: https://nostarch.com/learnbayes
This book gives you a good foundation of Bayesian data analysis with R code that you can run to understand example. In that sense this book is great to learn R while learning Bayesian statistics. It's quite long because of the code examples so you need to pick what you want to read. https://nyu-cdsc.github.io/learningr/assets/kruschke_bayesian_in_R.pdf
This book is quite similar to the previous one but nicer to read, I think. It's a little bit more technical but still easy to understand, I think. https://xcelab.net/rm/statistical-rethinking/
This book is closer to the statistical methods you might be using and you're familiar with from year 1 and 2. It involves a lot of regression modelling which is the answer to the statistical problems that most psychologist face. If I remember correctly the later half of the book is mostly dedicated to intervention studies, so even though the book is longer you can easily ignore almost half of it. The first couple of chapters are really important. https://avehtari.github.io/ROS-Examples/
And then, this is the package and the modelling framework that many people including myself use in practice these days described in a way that is, I think, relatively easy to understand. https://cran.r-project.org/web/packages/brms/vignettes/brms_multilevel.pdf
As bonus, this is the book I mentioned earlier. It's a really good history of contemporary statistics, especially the conceptual awkwardness to testing null-hypotheses using p-values. https://cup.columbia.edu/book/bernoullis-fallacy/9780231199940
This book doesn't teach you Bayesian statistics but I thoroughly enjoyed it and it makes you think about contemporary problems in empirical disciplines.
Any questions, please let me know.