Section author: Danielle J. Navarro and David R. Foxcroft

Omissions within the topics covered

Even within the topics that I have covered in the book, there are a lot of omissions that I’d like to redress in future version of the book. Just sticking to things that are purely about statistics (rather than things associated with jamovi), the following is a representative but not exhaustive list of topics that I’d like to expand on at some time:

  • Other types of correlations. In chapter Correlation and linear regression I talked about two types of correlation: Pearson and Spearman. Both of these methods of assessing correlation are applicable to the case where you have two continuous variables continuous and want to assess the relationship between them. What about the case where your variables are both nominal scale nominal? Or when one is nominal nominal scale and the other is continuous continuous? There are actually methods for computing correlations in such cases (e.g., polychoric correlation), and it would be good to see these included.
  • More detail on effect sizes. In general, I think the treatment of effect sizes throughout the book is a little more cursory than it should be. In almost every instance, I’ve tended just to pick one measure of effect size (usually the most popular one) and describe that. However, for almost all tests and models there are multiple ways of thinking about effect size, and I’d like to go into more detail in the future.
  • Dealing with violated assumptions. In a number of places in the book I’ve talked about some things you can do when you find that the assumptions of your test (or model) are violated, but I think that I ought to say more about this. In particular, I think it would have been nice to talk in a lot more detail about how you can tranform variables to fix problems. I talked a bit about this in the sections Transforming and recoding a variable and Mathematical functions and operations, but the discussion isn’t detailed enough I think.
  • Interaction terms for regression. In chapter Factorial ANOVA, I talked about the fact that you can have interaction terms in an ANOVA, and I also pointed out that ANOVA can be interpreted as a kind of linear regression model. Yet, when talking about regression in chapter Correlation and linear regression I made not mention of interactions at all. However, there’s nothing stopping you from including interaction terms in a regression model. It’s just a little more complicated to figure out what an “interaction” actually means when you’re talking about the interaction between two continuous predictors continuous, and it can be done in more than one way. Even so, I would have liked to talk a little about this.
  • Method of planned comparison. As I mentioned this in chapter Factorial ANOVA, it’s not always appropriate to be using a post-hoc correction like Tukey’s HSD when doing an ANOVA, especially when you had a very clear (and limited) set of comparisons that you cared about ahead of time. I would like to talk more about this in the future.
  • Multiple comparison methods. Even within the context of talking about post-hoc tests and multiple comparisons, I would have liked to talk about the methods in more detail, and talk about what other methods exist besides the few options I mentioned.