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.

Research Basics

Establishing causality

Null hypothesis testing

P-hacking and the problem of multiple comparisons

Stats Topics

Variance, covariance, and correlation

Interpreting logistic regression – Part I

Interpreting logistic regression – Part II