ANOVA

Same Statistical Models, Different (and Confusing) Output Terms

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 a model.

We will focus on ANOVA and linear regression models using SPSS and Stata software. As you will see, the biggest differences are not across software, but across procedures in the same software.

Member Training: The Anatomy of an ANOVA Table

by Jeff Meyer

Our analysis of linear regression focuses on parameter estimates, z-scores, p-values and confidence levels. Rarely in regression do we see a discussion of the estimates and F statistics given in the ANOVA table above the coefficients and p-values.

And yet, they tell you a lot about your model and your data. Understanding the parts of the table and what they tell you is important for anyone running any regression or ANOVA model.

R-Squared for Mixed Effects Models

by Kim Love 1 Comment

When learning about linear models —that is, regression, ANOVA, and similar techniques—we are taught to calculate an R². The R² has the following useful properties:

The range is limited to [0,1], so we can easily judge how relatively large it is.
It is standardized, meaning its value does not depend on the scale of the variables involved in the analysis.
The interpretation is pretty clear: It is the proportion of variability in the outcome that can be explained by the independent variables in the model.

The calculation of the R² is also intuitive, once you understand the concepts of variance and prediction. [Read more…] about R-Squared for Mixed Effects Models

Member Training: Elements of Experimental Design

by Karen Grace-Martin

Whether or not you run experiments, there are elements of experimental design that affect how you need to analyze many types of studies.

The most fundamental of these are replication, randomization, and blocking. These key design elements come up in studies under all sorts of names: trials, replicates, multi-level nesting, repeated measures. Any data set that requires mixed or multilevel models has some of these design elements. [Read more…] about Member Training: Elements of Experimental Design

Member Training: Non-Parametric Analyses

by guest

Oops—you ran the analysis you planned to run on your data, carefully chosen to answer your research question, but your residuals aren’t normally distributed.

Maybe you’ve tried transforming the outcome variable, or playing around with the independent variables, but still no dice. That’s ok, because you can always turn to a non-parametric analysis, right?

Well, sometimes.
[Read more…] about Member Training: Non-Parametric Analyses

The Difference Between Random Factors and Random Effects

by Karen Grace-Martin 3 Comments

Mixed models are hard.

They’re abstract, they’re a little weird, and there is not a common vocabulary or notation for them.

But they’re also extremely important to understand because many data sets require their use.

Repeated measures ANOVA has too many limitations. It just doesn’t cut it any more.

One of the most difficult parts of fitting mixed models is figuring out which random effects to include in a model. And that’s hard to do if you don’t really understand what a random effect is or how it differs from a fixed effect. [Read more…] about The Difference Between Random Factors and Random Effects