 Once you've done the basic graphics for your data and seen what you're dealing with, it's a good idea to move on to basic statistics. And in SPSS, the most basic version of this is frequencies. I like to think of it as putting things into buckets and then simply counting what's in the buckets. So the idea is when you have a limited number of categories in your data, then you should just count how often each category occurs. It's a first step to really some significant insight. But wait, I just want to mention that the frequencies command in SPSS can do so much more than that. And I'm going to show you how it works. For example, it can do charts, it can do bar charts and pie charts and histograms and normal distributions. And they can do a lot of statistics beyond frequencies. It can do quartiles, percentiles, mean, median, mode, standard deviation, variance, skewness, kurtosis, and so on. In fact, because of this, I like to think of frequencies as SPSS's version of the competent man character in literature and movies who can do everything well. You know, somebody like Leonardo da Vinci or Iron Man who seems to be able to do everything, or you know, Marie Curie right here because she won two Nobel Prizes and what have the rest of us done? But anyhow, back to statistics. Let's take a look at frequencies and let's try an SPSS. Just open up this syntax file and we'll see the things that it's able to do for you. As always, we need to begin by opening a dataset. We'll use demo.save and you can use this command in Mac or this command in Windows to do that. Once you have the dataset open, it's a very simple thing to get the frequencies. Now, I have the syntax saved here. But really, it's more as a record of what I've done because I use the dropdown menus to create these commands. So I'm going to come up to frequencies and I'm going to get the frequencies for gender and job satisfaction. To do that, I come to analyze to descriptive statistics. And then the first option there is frequencies. And what I'm going to get is gender, just right here, I'll just double click to move it over. And we'll also get job satisfaction. I'll double click and move that over. Now, what's important is these are two different kinds of variables. Gender is a categorical variable, nominal. And job satisfaction here is a scaled variable. And so normally you don't do the same kind of things for these. But frequencies is very flexible. So I'm just going to hit OK and we'll see the default output for frequencies. The first thing that it shows us is how many valid observations are. So how many of our 6400 cases have data on these variables? The answer is all of them. There's no missing data here. And then it comes down and it gives us frequency tables where it lists every value or possible score on the variable and then says how often each one occurs. So for gender, we have 3179 female respondents. That's 49.7%. And the percent and the valid percent would be different if we had missing data. But we don't so we can ignore that. And then the cumulative simply adds up to 100. And then job satisfaction. This is a scaled variable which has 12345 as answers. And here you can see how many people put each of the answers. 17% highly dissatisfied, 21.8 neutral, 19.1 highly satisfied. And that's a quick look at the frequencies that we're dealing with. It's a nice way also to check if your variables are coded well. But what we can do is more than that we can also turn off the tables and we can do bar charts using the frequencies command. So I'm going to keep those same two variables gender and job satisfaction. But this time I'm just going to make bar charts. I'll go back to my recent commands frequencies. And what I'm going to do is I'm going to click this. It's going to give me a little error message because I haven't changed the other thing first. And I'm going to come to charts right here. I'm going to tell it to make bar charts. Obviously it can make pie charts and histograms as well. I'll click continue and then click OK. And now the same general command frequencies is not producing tables but is producing charts. And here you can see that we are very closely matched in terms of the number of male and female respondents. And here you can see job satisfaction sort of peaks at neutral and somewhat satisfied. And so that's a really nice thing. You don't even have to use the bar chart command. You can do it right here. You can also get more kinds of statistics in there. So for instance this one I'm going to keep the tables off but I'm going to ask for a few extra things. In fact let me just come back to this one. We'll go to Analyze, Descriptives and Frequencies. And this time I'm going to do Age, Reside and Job Sat. So I'm going to remove my one categorical variable here. I'll just reset that. I'll do Age or the other two, Reside and Job Sat. And then I think that's this one right here. And we'll come down to Job Satisfaction. And we'll move that over. But they're all scaled variables. What I'm going to do here is first I'm going to come to Statistics. And I have really an impressive range of things I can get. I can get the mean, I can get the median, the mode. If you want the mode I think this is the only place to get it in SPSS. I can get quartile values. Now it doesn't do the minimum and the maximum. You have to select those separately down here. But you can also get cut points. Now, a cut point is an interesting one. The quartiles are cut points. It splits the data into four equal size groups with the same number of people in each. Sometimes you want something other than that. So for instance, I know that if you're doing propensity scores, it's not uncommon to use five equal groups, quintiles. And also there are situations in which you want not the most extreme scores, but near the most. And so I'm going to put, for instance, the 2.5 percentile and the 97.5 percentile because those frame the middle 95 percent of the data. I can also get the standard deviation in the variance. Is there anything else I want right here? I want skewness and kurtosis. I'm going to hit continue. Then I'm going to come back to this one. I'm going to turn off the frequency tables because otherwise I have a lot of different possible answers here. I'd have a lot going on. And this time I'm going to ask for histograms and we'll put a normal curve on top of each histogram. Click continue and click OK. And so here's what we get. It starts with the statistical output. Here are the three variables I selected. It gives us the mean, the standard deviation, the variance, skewness and standard error of skewness, kurtosis. We have the minimum and maximum scores. And then the percentiles. Now it's a funny list here because I've got three things intermingled. The quartiles, that's something I asked for. So we have the 25th percentile, the 50th percentile and the 75th percentile. Those are the quartile values. I had the minimum and maximum up here. So those are the zero and 100 percent quartiles. But I also asked for quintiles. And so that splits it at 20, 40, 60 and 80 percent. And then finally I manually entered the two and a half percentile and then 97 and a half percentile. And so they're all put there together, but it's really easy to see the changes in the distribution. Beneath that we have the histograms. And we have each variable has its own histogram, along with a normal distribution with the same mean and standard deviation laid on top. Age is pretty close to normal. Here's a current address however you can see is really skewed because most people haven't lived there that long. And then finally job satisfaction is a little flatter than we would expect if it were normally distributed. The point of this is that I'm able to do a tremendous amount of statistical and graphical work using a single command, the frequencies function in SPSS, one of the most versatile commands you'll ever use.