As I’ve been developing MBA and doctoral courses on research design and applied statistics, I haven’t been wild about many of the textbooks/resources available for students. It isn’t that the materials are wrong or are bad in the normative sense, rather they tend to fall into one of three camps…

  • Too expensive—Lets face it, most textbooks are inordinately expensive for the value added.
  • Too math heavy—I’m a math geek, don’t get me wrong, but many applied textbooks are heavy on the ‘lets show the proof.’ I like proofs, but students generally don’t need to worry about that part, and it can make the book unapproachable.
  • Too little real-world application—This was a frustration of mine when I was a doctoral student, and I have a hunch that others shared the same perspective. It’s nice to know the technical details of LDV modeling, but what I really wanted to know was what all of the numbers meant in the output!

So I’ve starting creating my own library below of short explanations for many common research design/applied statistics topics. These materials, along with video screencasts where I’ve created them, form the corpus of the readings I assign for my courses. I update the pages periodically with new info and better explanations, so please help me make these better!

By the way, this is by FAR not a cumulative or exhaustive list of the topics within each heading…this is an organic list that will grow over time as I add content and choose to assign these topics in class.

All code is in R, created with RMarkdown and RStudio.