Hierarchical regression is a very common approach to model building that allows you to see the incremental contribution to a model of sets of predictor variables. Popular for linear regression in many fields, the approach can be used in any type of regression model — logistic regression, linear mixed models, or even ANOVA. In this webinar, we’ll go over the concepts and steps, and we’ll look at how it can be useful in different contexts.

And having them all in the variable view window makes things incredibly easy while you're doing your analysis. But sometimes you need to just print them all out--to create a code book for another analyst or to include in the output you're sending to a collaborator. Or even just to print them out for yourself for easy reference.

So it's best to choose a category that makes interpretation of results easier. Here are a few common options for choosing a category. Remember, the regression coefficients will give you the difference in means ( and/or slopes if you've included an interaction term) between each other category and the reference category.

In the previous blogs I wrote about the basics of running a factor analysis. While the step-by-step introduction sounds relatively straightforward, real-life factor analysis can become complicated. Here are some of the more common problems researchers encounter and some possible solutions:

Inter Rater Reliability is one of those statistics I seem to need just seldom enough that I forget all the details and have to look it up every time. Luckily, there are a few really great web sites by experts that explain it (and related concepts) really well, in language that is accessible to non-statisticians. […]

Many variables we want to measure just can’t be directly measured with a single variable. Instead you have to combine a set of variables into a single index. But how do you determine which variables to combine and how best to combine them? Exploratory Factor Analysis.

I would start by putting the literature review before Step 1. You’ll use that to decide on a theoretical research question, as well as ways to operationalize it.. But it will help you other places as well. For example, it helps the sample size calculations to have

After you perform your factor analysis, you may wind up with a good solution that has variables that do not load highly on any of your factors. How will you decide which variables you should exclude from your analysis?

David Lillis, guest presenter for Craft of Statistical Analysis Webinar Ten Data Analysis Tips in R, answers your questions.

Linear, Logistic, Tobit, Cox, Poisson, Zero Inflated… The list of regression models goes on and on before you even get to things like ANCOVA or Linear Mixed Models. In this webinar, we will explore types of regression models, how they differ, how they’re the same, and most importantly, when to use each one.