Simple Random Samples (SRS) have a few important features. 1. Each element in the population has an equal probability of being selected to the sample. That’s pretty self-explanatory, but it has important consequences and requirements.

In this follow-up to December’s webinar, we’ll finish up our discussion of interactions. There is something about interactions that is incredibly confusing. An interaction between two predictor variables means that one predictor variable affects a third variable differently at different values of the other predictor.

Of course, the main effect for condition in this full model with the interaction will test the same thing, as well as give you additional information at different ages. So your second option is:

by Lucy Fike We know that using SPSS syntax is an easy way to organize analyses so that you can rerun them in the future without having to go through the menu commands. Using Python with SPSS makes it much easier to do complicated programming, or even basic programming, that would be difficult to do […]

In Part 9, let’s look at sub-setting in R. I want to show you two approaches. Let’s provide summary tables on the following data set of tourists from different nations, their gender and numbers of children. Copy and paste the following array into R.

There is something about interactions that is incredibly confusing. An interaction between two predictor variables means that one predictor variable affects a third variable differently at different values of the other predictor.

In Part 8, let’s look at some basic commands in R. Set up the following vector by cutting and pasting from this document: a <- c(3,-7,-3,-9,3,-1,2,-12, -14) b <- c(3,7,-5, 1, 5,-6,-9,16, -8) d <- c(1,2,3,4,5,6,7,8,9) Now figure out what each of the following commands do. You should not need me to explain each command, but I will explain a few.

In Part 7, let’s look at further plotting in R. Try entering the following three commands together (the semi-colon allows you to place several commands on the same line). Let’s take an example with two variables and enhance it.

In our previous posts, we discussed factors and factor loadings and rotations. In this post, I would like to address another important detail for a successful factor analysis, the type of variables that you include in your analysis.

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.