 Our next topic in Jmovi is factors and the idea here is that really data is a jungle and it can be confusing and it can be overwhelming and truthfully sometimes less is more specifically with data what that means is going from 1000 variables to maybe a dozen or two that you need to deal with it's much easier to find the order and meaning in that you also tend to have more reliable and stable measurements. And the first procedure we'll look at in Jmovi is reliability analysis because you know, sometimes you want to talk about the flock the whole and not each individual sheep. Perhaps you want to combine the variables you need to get a single scale score. But to do that, you first need to make sure that you're dealing with similar variables that they're measuring similar things. And reliability analysis, whether with Chromebox Alpha or McDonald's Omega can allow you to do that. Or if you're trying to find the main dimensions that make up your data like finding the main streets in a city, you can try principal component analysis, or the closely related exploratory factor analysis, which while being based on different mathematics and a different theory about the relationship between observed variables and implicit factors accomplishes basically the same thing. And both of these approaches are exceptionally easy to set up and interpret in Jmovi. Or maybe you already know the bins that you want the variables to go into. And in that case, you can use confirmatory factor analysis, where you put together the factors that the variables go into, and the observed variables, and you see how well the variables match up with those factors, and how well your model explains the covariance in your data. But whichever technique you use, it'll help you get some clarity in your data. So you can start on the path towards insight and action.