 In our previous movie, we looked at the power of the frequencies command, but for basic statistics, another very common choice is descriptives within SPSS. The neat thing about descriptives is it allows you to achieve maximum density. That is how to get a lot of numbers on a lot of variables in just a little space. That's what descriptives is really good for. On the other hand, there is a restriction. It works only with numerical variables, but that's a lot of the data that you might have. And if you have that, it can give you things like the mean, the sum, the standard deviation, the standard error, the variance, the minimum and maximum, the range, the skewness, and kurtosis. Now, I say, but guess what? You know, in case you don't remember, Frequencies does more, but that's okay. There's certain things that the descriptives command does well. Here's what it does well. First, it gives you very concise, compact, tabular output. So it's really easy to see a bunch of information in a small space. Second, it's a really quick way to find obvious errors and coding in your data. Finally, you can get proportions for indicator variables. That's zero one variables. And I'll show you how that works. Also, we have a bonus feature here in descriptives. Descriptives is the home of SPSS's top secret hidden one step Z score procedure. I've seen people knock themselves out trying to get Z scores by getting standard deviations and means you don't have to do any of that. You click one button and you're done. But let's try it in SPSS and I'll show you how it works. Just open up this syntax file, and we'll see what you can do with descriptives. We'll begin as always by opening the data set, we'll be using demo.save. Here's the path on a Macintosh running version 22, and the path on a Windows also running version 22. This is my first command, and it looks really long, but that's because I have a lot of variables in it. All we need to do is come up to analyze to descriptive statistics and descriptives. We click on that. Now, one of the things it does is it only shows you the variables that it can analyze. So gender, which was a string variable, meaning I had just text, that's not in there. But what I can do is I can just select all of them and do a command or control a and then move everything over. And then I'm just going to do the default analysis. I'll just hit OK. And here's our output. We have a whole bunch of variables, and it tells us first the number of observations at 6400, almost all the way down. This question about internet is missing some data, but that appears to be the only one. We have the minimum value and the maximum value. By the way, this is where I talk about quick and easy data checking. If you have a variable that's only supposed to go from one to five or zero to one, if you have a 17, you know something's wrong. And so by simply checking the outer boundaries, that's a fast way of seeing if there are any really obvious errors. We also have the mean and the standard deviation two of the things you generally need the first two moments of a distribution. And so that's a lot of information, and it's in a very concise format. That's a wonderful thing. Now, if we go back to the syntax, I do want to mention this one thing about indicator variables. I said it earlier. It's this. If you have indicator variables, that's a binary or dichotomous variable that has only two possible values. And if that variable is coded as zero and one, then you can in fact get the mean of it. And it tells you something that tells you the proportion of observations that have ones. And this works best if you use the standard programmer format of zero equals false or no, and one equals true or yes. And strangely, in this particular data set, that's true for most of the variables, but not the last one or two and demo dot save. And I have no idea why they switched that. But it's something that you want to check in the coding before you go ahead and do it. And so if I go back to the output, you can see, for instance, that most of these wireless service down through owns fax machine, those are all zero ones where zeros know and one is yes. The mean right here tells us that 99% of the people own TVs 96 on VCR is because this is a long time ago, that 25% had paging services. And I'd like this one, where's the internet on this list, 27% had the internet because this was apparently generated in like, you know, 1990, who knows what, anyhow, those are meaningful data points. And the mean tells you the proportion of ones or yeses. I'll go back to the syntax here. And then let's take a quick look at the Z scores. Now any reasonable person would think that a Z score is a transformation of the data and therefore it would be under the transform menu. But you know what, it's not there. Instead, it's hidden as an option in descriptives. So let's go back to descriptives. And let's do age and income. I'm going to reset this. I'm going to pick age. And I'm going to pick household income. And I'm going to get both of these as Z scores because a lot of procedures work a lot better if you have Z scores. All you have to do is this, click save standardized values as variables. And if I hit OK, what it's done here is it gives me the descriptives because I actually still ran the descriptives command for those two variables. But more significantly, let's take a look at the data set. When I come to the data set, if I scroll to the end here, I have two variables that were not there previously, Z age and Z income. And they have lots of decimal places because you need those with Z scores. Now I'm rephrased. Now under normal circumstances, you would want to save this into the data. I'm not going to do that because this is one of SPS's built in default data sets. But I do want to show you that we can do one other thing here. Let's go back and get descriptives for those Z scores. So I'm going to come to analyze descriptives. I'm going to reset this, come down to see our two new variables. I'll select, do a little shift click to get both of them. Then pop them over here, then I'll hit OK. And as you would expect, a Z score has a mean of zero and a standard deviation of one. And we didn't have to do it manually. We didn't have to remember any values. We didn't have to round things off and do that exactly for us. And so that is what the descriptives command does. It makes a very concise tabular output. And it also allows you to say standardized or Z scores for use in certain procedures.