There are not a lot of statistical methods designed just for ordinal variables. But that doesn’t mean that you’re stuck with few options. There are more than you’d think. Some are better than others, but it depends on the situation and research questions. Here are five options when your dependent variable is ordinal.

## Latest Blog Posts

The scatterplot is a simple display of the relationship between two, or sometimes three, variables. You have a wide range of options for displaying a scatterplot. In particular, you can control the location, size, shape, and color of the points in your scatterplot.

Of all the stressors you’ve got right now, accessing your statistical software from home shouldn’t be one of them. (You know, the one on your office computer). We’ve gotten some updates from some statistical software companies on how they’re making it easier to access the software you have a license to or to extend a […]

One activity in data analysis that can seem impossible is the quest to find the right analysis. I applaud the conscientiousness and integrity that underlies this quest. The problem is in many data situations there isn’t one right analysis.

Learning statistics is difficult enough; throw in some especially confusing terminology and it can feel impossible! There are many ways that statistical language can be confusing. Some terms mean one thing in the English language, but have another (usually more specific) meaning in statistics.

Based on questions I’ve been asked by clients, most analysts prefer using the factor analysis procedures in their general statistical software to run a confirmatory factor analysis. While this can work in some situations, you’re losing out on some key information you’d get from a structural equation model. This article highlights one of these.

Confirmatory factor analysis (CFA) is the fundamental first step in running most types of SEM models. You want to do this first to verify the measurement quality of any and all latent constructs you’re using in your structural equation model.

In this Stat’s Amore Training, Marc Diener will help you make sense of the strange terms and symbols that you find in studies that use multilevel modeling (MLM). You’ll learn about the basic ideas behind MLM, different MLM models, and a close look at one particular model, known as the random intercept model. A running […]

One of the many decisions you have to make when model building is which form each predictor variable should take. One specific version of this decision is whether to combine categories of a categorical predictor. The greater the number of parameter estimates in a model the greater the number of observations that are needed to […]

Learning how to analyze data can be frustrating at times. Why do statistical software companies have to add to our confusion? I do not have a good answer to that question. What I will do is show examples. In upcoming blog posts, I will explain what each output means and how they are used in […]