 The next step in our introduction to SPSS and basic graphics is bar charts. And we like bar charts for a very simple reason. They are simple and simple is good. Or more specifically, bar charts are the most basic graphic for the most basic data, just frequencies for a simple category. It's also a very basic command in SPSS. Now, we actually have a few options on different kinds of bar charts. One, we can make a simple bar chart. So a single variable simply showing the category frequencies in that variable. Two, we can do a grouped bar chart where we break it down by some other variable. And then three, we can do multiple variables and show the bars simultaneously. But let's try this in SPSS. It's really easy to do. Just open up this SPSS syntax file and we'll give it a whirl. Once you've got the file open, you'll need to open the demo data set we've used it before. This is the command for Mac if you're running 22. And this is the command for Windows if you're running 22. Just change the version number if you need to. Once you have the file open, we're going to make some bar graphs. Now I'm going to do it by coming up here to what are called the legacy dialogues. These are specialized one graph only dialogues that come from earlier versions of SPSS. And seriously, I usually use these because I find them so quick and easy to deal with. What we're going to do is we're going to make a bar chart for levels of education in our sample. So I'm going to hit bar. We're going to do a simple bar chart. And we'll do groups of cases. And all I need to do is hit level of education, put it into the category axis and hit OK. And I make the output bigger. And there it is. Absolute piece of cake. And it's also very, very simple syntax. You see the syntax right here. It's really could be one line. And just as a point of comparison, here's the same chart produced with the chart builder. But you see we have this really complicated overwhelming code. The legacy chart produces it in an extremely simple way. So that's a simple bar chart, piece of cake. Now let's do a clustered bar chart for groups of cases. We'll look at levels of education by gender. To do that, we come back up to graphs and to legacy dialogues to bar. And now we're going to cluster it and do a level of education clustered by gender. So I hit define. And we'll get level of education. That's sort of our outcome variable. Put that under category axis and then define clusters by gender. We put that right there. I'll hit OK and make it bigger. And this time it uses nicer colors. But you have the five levels of education broken down where women are in blue and men are in green. But it's really easy to see here the relationship between the two variables. And in this particular data set, it really looks like there's no substantial difference between the men and women. Now I'll say I believe this is an artificial data set. So we wouldn't expect a lot of differences. But this is a nice way to compare them. By the way, come up and you'll see that the code for this is really simple. All it does is it adds by gender. And so again, a very short command. I'm going to go back to the syntax and we're going to do one more here. And that is for multiple variables. So this is a situation in which it can be confusing if you have a lot of categories within each variable. What I'm doing here is I'm going to get the means of variables or the numbers of ones. If you have an indicator variable where zero for no and a one for yes. This is a really nice way of comparing the frequencies of each one of them across. I'll show you how that works. We'll go up to graphs. We'll come back over to bar. And we're going to do a simple one. But this time we're doing separate variables. I'll hit define. Then I'm going to come down here and this data set, again, which I believe is fictional, asked a lot of people about various things that they might do. We're going to ask them about wireless service. And we're going to come down to whether they own a fax machine because this is old data and it's asking about old technology pagers. I've never had a pager. But I simply select all those variables. I put them in here. And as long as they're all in the same scale, it's going to do the mean of each one. And on a zero one, the mean is the proportion of ones. Hit OK. And there we have it. It's a way of looking at the distribution of multiple variables simultaneously. It's a very information dense display. And especially when you as an analyst are exploring your data. This can be a really quick and easy way of getting a feel for your data, which can then direct your further analyses.