Someone recently asked me if they need to learn R. In responding, it struck me that this is another way that learning a stat software package is like learning a new language.
The metaphor is extremely helpful for deciding when and how to learn a new stat software, and to keep you going when the going gets rough. (more…)
Let’s create a simple bar chart in R using the barplot() command, which is easy to use.
First, we set up a vector of numbers. Then we count them using the table() command, and then we plot them.
The table() command creates a simple table of counts of the elements in a data set.
H <- c(2,3,3,3,4,5,5,5,5,6)
Now we count the elements using the table() command, as follows: (more…)
Understanding moderation is one of those topics in statistics that is so much harder than it needs to be.
Here are three suggestions to make it just a little easier.
I have spoken with a number of researchers who are surprised to learn that moderation is just another term for interaction.
Perhaps it’s because moderation often appears with discussions of mediation. Or because we tend to think of interaction as being part of ANOVA, but not regression.
In any case, both an interaction and moderation mean the same thing: the effect of one predictor on a response variable is different at different values of the second predictor. (more…)
Do I really need to learn R?
Someone asked me this recently.
Many R advocates would absolutely say yes to everyone who asks.
I don’t.
(I actually gave her a pretty long answer, summarized here).
It depends on what kind of work you do and the context in which you’re working.
I can say that R is (more…)
In Part 10, let’s look at the aggregate command for creating summary tables using R.
You may have a complex data set that includes categorical variables of several levels, and you may wish to create summary tables for each level of the categorical variable.
For example, your data set may include the variable Gender, a two-level categorical variable with levels Male and Female. Your data set may include other categorical variables such as Ethnicity, Hair Colour, the Treatments received by patients in a medical study, or the number of cylinders in motor vehicles.
In any case, you may wish to produce summary statistics for each level of the categorical variable. This is where the aggregate command is so helpful. (more…)
In this series, we’ve already talked about what a complex sample isn’t; why you’d ever bother with a complex sample; and stratified sampling.
All this is in support of our upcoming workshop: Introduction to the Analysis of Complex Survey Data Using SPSS. If you want to learn a lot more on this topic, check that out.
In this article, we’re going to discuss another common design features of complex samples: cluster sampling.
In cluster sampling, you split the population into groups (clusters), randomly choose a sample of clusters, then measure each individual from each selected cluster.
The most common and obvious example of cluster sampling is when school children are sampled. An example I (more…)