I have recently worked with two clients who were running generalized linear mixed models in SPSS. Both had repeated measures experiments with a binary outcome. The details of the designs were quite different, of course. But both had pretty complicated combinations of within-subjects factors...

These types of errors are not associated with sample-to-sample variability but to sources like selection biases, frame coverage issues, and measurement errors. These are not the kind of errors you want in your survey.

As it is in history, literature, criminology and many other areas, context is important in statistics. Knowing from where your data comes gives clues about what you can do with that data and what inferences you can make from it. In survey samples context is critical because it informs you about how the sample was selected and from what population it was selected...

What do you do when you hear the word error? Do you think you made a mistake? Well in survey statistics, error could imply that things are as they should be. That might be the best news yet--error could mean that things are as they should be. Let's break this down a bit more before you think this might be a typo or even worse, an error...

Do you remember all those probability rules you learned (or didn’t) in intro stats? You know, things like the P(A|B)?While you may have thought that these rules were only about balls and urns (who pulls balls from urns anyway?), it’s actually not true. It turns out that having a good understanding of these rules (as well as actually remembering them) does come in handy when you’re doing data analysis.

If you have significant a significant interaction effect and non-significant main effects, would you interpret the interaction effect? It's a question I get pretty often, and it's a more straightforward answer than most. There is really only one situation possible in which an interaction is significant, but the main effects are not: a cross-over interaction.

In the last lesson we saw how to use qplot to map symbol colour to a categorical variable. Now we see how to control symbol colours and create legend titles..

In this lesson, let’s see how to use qplot to map symbol colour to a categorical variable. .

The qplot (quick plot) system is a subset of the ggplot2 (grammar of graphics) package which you can use to create nice graphs. It is great for creating graphs of categorical data, because you can map symbol colour, size and shape to the levels of your categorical variable. To use qplot first install ggplot2 as follows..

Data analysts can get away without ever understanding matrix algebra, certainly. But there are times when having even a basic understanding of how matrix algebra works and what it has to do with data can really make your analyses make a little more sense.