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Fields Andy, "Discovering Statistics" might be what you're looking for.

It provides great intuition as to what test you can use given your data and thoroughly explains the working of each without diving unnecessarily deep into the math. (As far as I remember, the usage of Greek symbols is kept to a minimum)

There are several variants of this book, each directed at a particular statistical software (such as R and SPSS). I'm currently using the version for R and can highly recommend it!

I learned in classes, so I may not be the absolute best person to offer recommendations because I did not self-teach. DataCamp was used for my first class that was taught in R, but if you're doing it yourself, it will not be free. Discovering Statistics Using R by Andy Field is also a common recommendation -- https://www.amazon.com/Discovering-Statistics-Using-Andy-Field/dp/1446200469

Andy Field’s Discovering Statistics is a great, and there are non R based versions too (although why would you want that?). It’s actually a kinda funny book, there is a whole chapter about what to show when making plots that he connects to him as a child learning not to flash his genitalia at school (or something like that it has been a while).

This book has a good flow chart. https://www.amazon.com/Discovering-Statistics-Using-Andy-Field/dp/1446200469/ref=mp_s_a_1_3?adgrpid=56341997896&dchild=1&gclid=CjwKCAjwmv-DBhAMEiwA7xYrd_LFG47vdENBcuGYfdLzJg-uulOJdMzIF8BL9OeN9EDFNm79-QBluhoCCLgQAvD_BwE&hvadid=274734491...

I actually conducted a similar study ~6 months ago, so I've already got a basic grounding in multilevel linear regression (I actually used that Bodo Winter tutorial when I first learned, very useful! I also found Andy Field's chapter in Discovering Statistics Using R to be a fairly useful resource).

My confusion here is simply to do with the two-dimensional nature of the outcome variable. In my previous study, I used a questionnaire-based measure of affect, which provided two one-dimensional scales to use as outcomes. In this study, the measure of affect I'm using will be a single measure on a two-dimensional plane. Of course, I can simply divide them up and look at the two axes (i.e. valence and activation) separately, but I'm not sure if that's the most appropriate way of approaching the analysis.

My program requires a number of stats classes and my advisor requires a number more than that. My program also offers a few data science-related specializations, which are, of course, optional, but great.

For some independent learning, Andy Field's Discovering Statistics Using R -- https://www.amazon.com/Discovering-Statistics-Using-Andy-Field/dp/1446200469/ref=sr_1_1?ie=UTF8&qid=1538060236&sr=8-1&keywords=discovering+statistics+using+r -- and datacamp.com are both handy resources.

> I would've loved to have done this in R (or at least Weka!), but my class uses Excel for analysis. I was hoping I could learn a statistical language instead of using Python for everything academic, but I suppose there's nothing keeping me from doing it on my own time.

There's a lot of great open learning style courses available for R online for free.

Although if you are going to get a book can I recommend Discovering Statistics with R by Andy Field.

i think his books are pretty good. i'm waiting for his R book to drop in price. (on amazon, it's like 75$ for a paperback. boo.)

This book is a amazing: Discovering Statistics Using R by Andy Field

If you are doing self-study, it is easy to lose momentum. This book is hilarious, personal, and transcends the textbook genre.

Amazon Link

Reminds me of Andy Fields book where one of the stats examples was about putting eels up someone’s butt https://www.amazon.com/dp/1446200469/ref=cm_sw_r_cp_awdb_btf_t1_fJ5LFb9CAZ9BE?_encoding=UTF8&psc=1

ahhh sure, self learning statistics was the best thing I did as a hobby. I don't know about your use case(professional or hobbyist) and how much time you have on hand , but what I did was after learning the basic python libraries numpy , matplotlib from the coursera specializations and youtube I mentioned in main thread , I took some break from learning and answered as much quality question as possible on stack overflow related to data analysis(was good for my numpy, matplotlib, seaborn and also my ego coz my reputation was increasing :) )and started a visualization blog.Although it wasn't necessary it helped me a lot and I had enough time. I didn't know R till then , and I started this stats book by Andy Field as I wanted more of application and somewhat less of mathematical derivations(ISLR is good too). It is in R , but I studied the theory portion from it and implemented all its code in Python by myself and doing a lot of research. After completing this 1000 page book in Python , I understood python is good for ml and data science but R is best when it comes to stats.I am presently arranging all the python code I learnt and did while doing this book to push on github both in R and Python(it's wonder no one has redone this book in python).

So , tl;dr ,I did this specialization , and then read Andy Field's R book.And I referred to kaggle and fivethirtyeight along with other sites sometimes for datasets and articles.

Also, if curious, here is a presentation by Andy Field on these sorts of models: https://www.youtube.com/watch?v=UDnGqW0S4cA

He is a book which is very accessible to undergraduates and walks you through how to do them in R: https://www.amazon.com/Discovering-Statistics-Using-Andy-Field/dp/1446200469

I believe he also has a book using SPSS

I never thought that what I was doing was a good idea. I was just trying an experiment originally based on some quantitative research that I did. I know this might sound crazy, but if you really want to trade options successfully, it can pay to learn R and how to perform a variety of statistical tests. (If you're interested in statistics [or weightlifting] this is a very good book: https://www.amazon.com/Discovering-Statistics-Using-Andy-Field/dp/1446200469/.) Finding an edge is kind of like searching for a needle in a haystack.

The challenge is that it's hard to get high-quality data, and even when you can, it's expensive. I took a chance and ran with my findings, based on course-grained, free, data. It worked, but I don't have the confidence to do this again. Also, the analytical work is slow, frustrating, and hard. It's the kind of thing that statisticians who work for investment banks, hedge funds, and prop trading firms do. I tried to fight fire with fire.

Please read a textbook on statistics. I suggest Discovering Statistics Using R

https://www.amazon.com/Discovering-Statistics-Using-Andy-Field/dp/1446200469/ref=sr_1_2?s=books&ie=UTF8&qid=1383850288&sr=1-2

Lots of great suggestions here. My grad program used SPSS but it annoyed me that someone had to pay for it, so I learned R. Like others mention, if you learn R it can be easier to go back to SPSS. Also, others who use SPSS might think you have some kind of superpower.

Like u/bobyfiend says, the best is to do use it on some projects. This forces you to learn something that is important and you have interest in solving. The internet is amazing, and most answers in some form or another can be found on Stack Overflow (make sure to ask the questions in the proper format and search first), /r/rstats (a bit more friendly than stack overflow), or on some of the email lists.

In general, I would say there are a couple of resources that most people could benefit from as they start to learn:

-Andy Field's Discovering Statistics with R - It does have some irreverent humor, but is a good read

-Hadley Wickham's R for Data Science - this resource is free online but can also be bought through Amazon. Hadley is a R celebrity responsible for creating the 'tidyverse' series of packages - packages which make R more beginner friendly imo.

You will definitely want to look at your subspecialty and see if there are any people working in R there. They may have some other resources. Again, you can read books and watch courses all you want, but it is critical to practice (and practice using something you are interested in can help exceptionally). Ultimately, I used my dissertation as an excuse to dive into R - there was pain, and I probably could have done it quicker if I stayed in SPSS - but I learned a lot and now use R and Rmarkdown - and really do not think I plan on going back. Another user mentions looking at others' code, and this has also helped me to make my code more efficient and reproducible - a big strength of R (love that you can use Git).

I used the Andy field book

https://www.amazon.com/Discovering-Statistics-Using-Andy-Field/dp/1446200469/ref=nodl_

Are you reading the Andy field book by chance?

https://www.amazon.com/Discovering-Statistics-Using-Andy-Field/dp/1446200469/ref=nodl_

I recently used Andy Field's Discovering Statistics Using R when I was in a similar position to you are now and wanted to go back over the basics, and found it very useful. It's written in a very light and conversational tone (which you might find very humorous or very annoying, I believe it divides opinion) so it doesn't feel too much like a textbook, although it is one. He's also written a book called An Adventure in Statistics: The Reality Enigma which looks like it could be exactly what you're after, but I've not read it personally so can't say for sure whether it's any good.