Today I am going to continue highlighting resources I found helpful when studying for my prelims (which I successfully passed!). The last post on this topic was about blocking trials, and I’m glad I reviewed blocking and randomization for my exam since it was helpful in answering two of the questions on my exam.
The first resource I’d like to highlight is Lucas Meier’s ANOVA: A Short Intro Using R. I’ve previously recommended his blocking description, but honestly the whole site is great. He starts with important fundamentals of causal relationships and experimental design, and then gets into model statements and code chunks in R. It’s hard to find a resource that is beginner-friendly and non-jargony, but still detailed enough to cover mixed models and other tools that are necessary for analyzing most small plot data, and this site does all that and then some.
If you’re a SAS or MiniTab user looking for a very basic ANOVA review that covers a few different experimental designs (including split-plot and Latin squares), you may find the course website for Penn State’s Stats 502 class to be very helpful. Their website looks more like a traditional textbook with equations interspersed between paragraphs, and it includes a lovely 7-step guide to hypothesis testing that might be helpful for some readers. I like their description of repeated measures and how to identify them in different study designs, but I haven’t tested any of their code (I use neither SAS nor MiniTab on a regular basis). Their description of mixed models made slightly less intuitive sense to me than Meier’s, but maybe if Meier’s description doesn’t work for you this would be helpful.
On the topic of mixed models, a friend sent me this amazingly clear animation of hierarchical models during my studying process. It focuses nested models for regression rather than ANOVA, but it’s still a great way to visualize how hierarchical models can help make sense of data.
There are two more resources I used when studying for my prelims that I just can’t stop thinking about, and I will work on writing up posts for each over the next few weeks. Subscribe to the blog on the homepage or check back later this month to see some great takes on Bayesian stats and clay mineralogy!