 Sometimes, your data comes to you on scales that don't have any inherent meaning or may not be familiar. A 1 to 5 or 1 to 7 agreement scale, well, it's common, doesn't have inherent meaning. And if you're comparing income information from Nepal to Turkey to Mexico, you're going to be dealing with different currencies, and you're going to need a way of comparing relative standing. The easiest way to do that is with Z scores or standardized scores. Now, if you've had statistics, you know that that simply takes the score, subtract the mean of the variable, and divide by the standard deviation. And a lot of people show you how you can do that manually, and you can set that up in Chamomile that way, but there's a much easier way to do that. Let's do this one. We're going to come here and double-click on this empty variable here, and we're going to choose New Computed Variable. And what I'm going to do is I'm going to change this to, say, Z score. And then I'm going to use the function window and scroll down to the statistical functions to the last one on that particular list. It's Z. I just double-click on that and brings up the function, and then I need to tell it what I want the Z score of. In this case, I want the mean. That's the mean score of those three rating scale questions. I double-click on that, and I can close the window, and there it is. That's all I need to do. A negative Z score indicates that somebody is below the mean. A positive Z score says they're above the mean. And the numbers themselves are units of standard deviations. So this very first score that's highlighted is 0.4 standard deviations below the mean. Quick and easy, and Jmovi makes it a cinch, and that helps you get on the way to taking your variables that are in different scales or arbitrary ones and putting them into something that may be more meaningful and is certainly more comparable from one variable to another.