# Creating simple bar charts of group means

## Video: Creating simple bar charts of group means

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.

## Creating simple bar charts of group means

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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#### This video is part of

SPSS Statistics Essential Training (2011)

52 video lessons · 20041 viewers

Author

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1. ### Introduction

2m 58s
1. Welcome
1m 5s
2. Using the exercise files
40s
3. Using a different version of the software
1m 13s
2. ### 1. Getting Started

19m 0s
1. Taking a first look at the interface
11m 49s
7m 11s
3. ### 2. Charts for One Variable

21m 54s
1. Creating bar charts for categorical variables
7m 18s
2. Creating pie charts for categorical variables
2m 54s
3. Creating histograms for quantitative variables
5m 45s
4. Creating box plots for quantitative variables
5m 57s
4. ### 3. Modifying Data

33m 10s
1. Recoding variables
5m 33s
2. Recoding with visual binning
5m 33s
3. Recoding by ranking cases
5m 26s
4. Computing new variables
5m 37s
5. Combining or excluding outliers
5m 21s
6. Transforming outliers
5m 40s
5. ### 4. Working with the Data File

28m 12s
1. Selecting cases
6m 44s
2. Using the Split File command
5m 12s
3. Merging files
5m 33s
4. Using the Multiple Response command
10m 43s
6. ### 5. Descriptive Statistics for One Variable

22m 14s
1. Calculating frequencies
8m 43s
2. Calculating descriptives
5m 31s
3. Using the Explore command
8m 0s
7. ### 6. Inferential Statistics for One Variable

16m 3s
1. Calculating inferential statistics for a single proportion
6m 6s
2. Calculating inferential statistics for a single mean
5m 39s
3. Calculating inferential statistics for a single categorical variable
4m 18s
8. ### 7. Charts for Two Variables

30m 43s
1. Creating clustered bar charts
7m 10s
2. Creating scatterplots
5m 8s
3. Creating time series
3m 24s
4. Creating simple bar charts of group means
4m 17s
5. Creating population pyramids
3m 0s
6. Creating simple boxplots for groups
3m 3s
7. Creating side-by-side boxplots
4m 41s
9. ### 8. Descriptive and Inferential Statistics for Two Variables

45m 28s
1. Calculating correlations
8m 17s
2. Computing a bivariate regression
6m 27s
3. Creating crosstabs for categorical variables
6m 34s
4. Comparing means with the Means procedure
6m 33s
5. Comparing means with the t-test
6m 4s
6. Comparing means with a one-way ANOVA
6m 30s
7. Comparing paired means
5m 3s
10. ### 9. Charts for Three or More Variables

24m 30s
1. Creating clustered bar charts for frequencies
6m 34s
2. Creating clustered bar charts for means
3m 45s
3. Creating scatterplots by group
4m 13s
4. Creating 3-D scatterplots
4m 25s
5. Creating scatterplot matrices
5m 33s
11. ### 10. Descriptive Statistics for Three or More Variables

30m 57s
1. Using Automatic Linear Models
11m 52s
2. Calculating multiple regression
9m 3s
3. Comparing means with a two-factor ANOVA
10m 2s
12. ### 11. Formatting and Exporting Tables and Charts

29m 29s
1. Formatting descriptive statistics
6m 1s
2. Formatting correlations
7m 49s
3. Formatting regression
10m 19s
4. Exporting charts and tables
5m 20s
13. ### Conclusion

51s
1. What's next
51s

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