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In this section on charts for the associations between variables, we've looked at how we can depict the association between two categorical variables, for example, with clustered bar charts, and the association between two scale variables, for example, scatter plots. At this point, we'll move on to charts that show the association between two kinds of variables. THat is, charts that look at one categorical variable and how's it's connected with the scale variable. Whereas the other combinations of variables had clear preferences for the charts.
there are actually several useful options for charting associations for categorical and scale variables in combination. The first of these is a simple variation on the bar chart, adapted to show the mean score for each group. In this example, I am going to use the GSS dataset and I'm going to show family income as a function of the highest level of education of the respondent. To do that, I first go up to Graphs and click on the Chart Builder. From there, I come down to Bar in the Gallery and I simply drag this simple bar into the canvas.
On the X-axis, I am going to put my categorical predictor variable, which is the highest degree of education. That's called highest degree, and I drag that down to X-axis. Now on the left of that, on the Y-axis it says Count. However, if I come to the variable list and I get family income and I drag that over, it changes from Count to Mean. That's because it's a scale variable. Now if I wanted to, I could get other statistics. I could get the Median, the Group Median, the Mode, and truthfully, a very large range of statistics, but I am going to leave it with the Mean.
I am going to do one small variation, however. I am going to ask it to put on what are called error bars confidence intervals. These give some sort of indication of what the difference might be in the general population, as opposed to just a sample. Once I check that, then I need to come down and click Apply and then I come over to the box and I click OK. And here we see five bars that show different levels of education, from Did Not Finish High School, which has an average family income of about $20,000 a year in this particular data set, off through Bachelor's Degree and Graduate Degree, which have averages of about $50,000 a year in this particular data set.
Now I do feel it's important to clean this chart up a little bit, so like the others what I'm going to do is I am going to double-click on it and I am going to make a few clarifications, because you want to reduce the amount of clutter in the chart. So what I am going to do first, so I am going to click on this thing that says Error Bars and just delete that. Then I am going to change the error bars, because I find the end to them distracting. I come up to Bar Options and change them to just Whiskers here under Boxplot and Error Bar Styles. Click OK. I am going to change the color of the bars. I find that an unattractive color.
Maybe I will make it a light green and then I might want to make the text here a little bit larger. Now I could do something interesting when I do that. There we go. It just changes the space a little bit and I find this to be a much clearer diagram of the relationship between the two. So I am going to close this now. I'll close there and then I'll come up to the editing window and click the red X and there you have it. A bar chart that shows the association between income and between levels of education.
So bar charts are a great way to show the association between categorical variables and scale variables in general. They are very clean and very easy to interpret. As a note, one of the nice things about SPSS is that it keeps things clean. So while it's possible to edit the bars and give them shadows or a foster dimension, those options are hidden, which is good, because they are almost always bad ideas. Those sorts of effects are often called chart junk and most spreadsheets and presentation packages make it way too easy to engage in these unfortunate practices.
SPSS on the other hand keeps thing simple, keeps them clean, and keeps them easy to interpret, which is the entire purpose of data graphics. Anyhow, with that in mind, we'll move from bar charts to a fancier kind of display for the association between a dichotomous variable, that is one which has two categories and a scale variable, using something called a population pyramid.
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