# categorical outcome

### Member Training: Seven Fundamental Tests for Categorical Data

May 1st, 2020 by

In the world of statistical analyses, there are many tests and methods that for categorical data. Many become extremely complex, especially as the number of variables increases. But sometimes we need an analysis for only one or two categorical variables at a time. When that is the case, one of these seven fundamental tests may come in handy.

These tests apply to nominal data (categories with no order to them) and a few can apply to other types of data as well. They allow us to test for goodness of fit, independence, or homogeneity—and yes, we will discuss the difference! Whether these tests are new to you, or you need a good refresher, this training will help you understand how they work and when each is appropriate to use.

### The Difference Between Logistic and Probit Regression

May 12th, 2017 by

One question that seems to come up pretty often is:

What is the difference between logistic and probit regression?

Both are types of generalized linear models. This means they have this form:

### Pros and Cons of Treating Ordinal Variables as Nominal or Continuous

July 1st, 2016 by

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. (more…)

### When to Check Model Assumptions

March 7th, 2016 by

Like the chicken and the egg, there’s a question about which comes first: run a model or test assumptions? Unlike the chickens’, the model’s question has an easy answer.

There are two types of assumptions in a statistical model.  Some are distributional assumptions about the residuals.  Examples include independence, normality, and constant variance in a linear model.

Others are about the form of the model.  They include linearity and (more…)

### 6 Types of Dependent Variables that will Never Meet the Linear Model Normality Assumption

September 17th, 2009 by

The assumptions of normality and constant variance in a linear model (both OLS regression and ANOVA) are quite robust to departures.  That means that even if the assumptions aren’t met perfectly, the resulting p-values will still be reasonable estimates.

But you need to check the assumptions anyway, because some departures are so far off that the p-values become inaccurate.  And in many cases there are remedial measures you can take to turn non-normal residuals into normal ones.

But sometimes you can’t.

Sometimes it’s because the dependent variable just isn’t appropriate for a linear model.  The (more…)