In this webinar, we’ll discuss many of the issues involved in measuring time, including censoring, and introduce one specific type of event history model: the logistic model for discrete time events.

Someone recently asked me if they need to learn R. In responding, it struck me that this is another way that learning a stat package is like learning a new language. The metaphor is extremely helpful for deciding when and how to learn a new stat package, and to keep you going when the going gets rough.

Effect Size Statistics are all the rage. Journal editors want to see them in every results section. You need them for performing sample size estimates. (And editors want those too). But statistical software doesn’t always give us the effect sizes we need. In this webinar, we will go over: The difference between standardized and unstandardized […]

In Part 11, let’s see how to create bar charts in R. Let’s create a simple bar chart 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.

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. 1. Realize that moderation just means an interaction I have spoken with a number of researchers who are surprised to learn that moderation is just […]

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).

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 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 […]

In stratified sampling, the target population is first classified into subgroups or strata. (Grammar note: “strata” is plural for “stratum” just as “data” is plural for “datum.”). A simple random sample is then selected within every stratum.

In our last article, we talked about simple random samples. Simple random samples are, well…simple, but they’re not always optimal or even possible. Probability samples that don’t meet the assumptions of Simple Random Samples are called Complex Samples.