# Creating clustered bar charts for means

## Video: Creating clustered bar charts for means

In the last movie, we looked at how you can make a clustered bar chart to show the association between three different categorical variables. In this movie, we'll look at how to show the associations between two categorical predictor variables and a single outcome variable that is scaled or quantitative. For example, you may want to show the average purchase price of items bought by men and women in two different retail categories. Surprisingly, this kind of chart is even simpler than the categorical version we just covered, because that one required that we use panels to show all three variables.

## Creating clustered bar charts for means

In the last movie, we looked at how you can make a clustered bar chart to show the association between three different categorical variables. In this movie, we'll look at how to show the associations between two categorical predictor variables and a single outcome variable that is scaled or quantitative. For example, you may want to show the average purchase price of items bought by men and women in two different retail categories. Surprisingly, this kind of chart is even simpler than the categorical version we just covered, because that one required that we use panels to show all three variables.

With the scaled outcome though, we can use just a single panel like this. In this example, I am going to be using the General Social Survey data. GSS.sav again. To make the chart, let's go up to Graphs to Chart Builder. From there, we are going to come down to the Gallery to Bar Charts and choose a clustered bar chart. We'll drag that up here and what we are going to do is get our two predictor variables, placed one on the X-axis, one to set the cluster on X, the set color, and the third one, the Y-axis, will be our outcome variable.

In this case, I'm going to try to predict family income. That will be my outcome variable. So I'll just grab family income and take it over to the Y-axis and I am going to use two variables to predict that. One is whether a person is a male or female. I am going to drag that down here to the X-axis. And another one is whether a person has children or not. I'll bring that over here. I think it'd also be helpful to put on error bars and I'll click Apply.

Then I'll come back over here and click OK. There is a lot of code that goes into this so we can save and reuse later if we want. But here's the actual chart. So what we have here is women on the left and men on the right. People who do not have children are in blue and people who do have children are in green, and what's charted on the Y-axis is the mean family income that people reported. What's interesting about this is we have an interaction and that is, for women, those who do not have children reported a slightly higher average family income than those who do have children, although the standard deviation, the spread on these, is pretty big.

On the other hand, for men, the exact opposite is observed. That men who have children report a substantially higher family income than those who do not have children. That's about 25,000-40,000. Now again, all this chart is showing us there is an association between the variables. It doesn't explain why those differences are there. There are a lot of reasons that go into that and it could actually require some pretty nuanced investigation. Nevertheless, this is a very simple chart that shows how two predictor variables, male/female as one category, and having children, yes or no, as another, can be used to predict scores on a third quantitative or scale variable, in this case, family income.

So clustered bar charts for me is an easy and informative way to show how two categorical predictors are associated with the scaled outcome or an indicator outcome if you are doing 01. They also give a good idea of what the results of the inferential test would be. This kind of clustered bar chart can be one of the most effective tools that you have in exploring, analyzing, and presenting your own data. In the next movie, we'll look at another simple variation on a chart for when you have just one categorical variable and two scaled variables.

In this case, the scatter plot as group markers.

Show transcript

#### This video is part of

SPSS Statistics Essential Training (2011)

52 video lessons · 19134 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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