Cluster analysis classifies individuals into two or more unknown groups based on a set of numerical variables. It is related to, but distinct from, a few other multivariate techniques including discriminant Function Analysis..

When we talk about moderation, though, there is a specific role to X and Z. One is assigned as the Independent Variable and the other as the Moderator. The Independent Variable is an independent variable based on the third implication listed above: its effect is of primary interest.

In Part 14, let’s see how to create pie charts in R. Let’s create a simple pie chart using the pie() command. As always, we set up a vector of numbers and then we plot them.

One of our instructors–David Lillis–recently gave a talk in front of the Wellington R Users Group highlighting 15 Tips for using the R statistical programming language aimed at the beginner. Below is a video recording of his presentation…  

Complex Surveys use a sampling technique other than a simple random sample. Terms you may have heard in this area include cluster sampling, stratified sampling, oversampling, two-stage sampling, and primary sampling unit. Complex Samples require statistical methods that take the exact sampling design into account to ensure accurate results. This webinar, by guest presenter Dr. […]

In Part 13, let’s see how to create box plotsin R. Let’s create a simple box plot using the boxplot() command, which is easy to use. First, we set up a vector of numbers and then we plot them. Boxplots can be created for individual variables or for variables by group.

In Part 12, let’s see how to create histograms in R. Let’s create a simple histogram using the hist() command, which is easy to use, but actually quite sophisticated.

Multicollinearity isn’t an assumption of regression models; it’s a data issue. And while it can be seriously problematic, more often it’s just a nuisance.

You may have never heard of listwise deletion for missing data, but you’ve probably used it. Listwise deletion means that any individual in a data set is deleted from an analysis if they’re missing data on any variable in the analysis. Although the simplicity of it is a major advantage, it causes big problems in many missing data situations.

Including Z in the model often leads to the relationship between X and Y becoming more significant because Z has explained some of the otherwise unexplained variance in Y. An example of this kind of covariate is when an experimental manipulation (X) on response time (Y) only becomes significant when we control for finger dexterity levels (Z).