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Creating pie charts for categorical variables

From: SPSS Statistics Essential Training (2011)

Video: Creating pie charts for categorical variables

In the previous movie, I showed you how to use SPSS's Chart Builder, its unified interface, for nearly every chart the program can make. And with it, we made a bar chart. In this example, I want to show you how to make another kind of categorical chart, the pie chart, that's a common choice for categorical variables. The procedure is very similar to that of bar charts; however, there are a couple of important differences. These have to do with the demands that pie charts place on the nature of the data. These are that the data must be exhaustive and mutually exclusive. What that is is exhaustive means that all the categories need to cover all of the possibilities and add up to 100%.

Creating pie charts for categorical variables

In the previous movie, I showed you how to use SPSS's Chart Builder, its unified interface, for nearly every chart the program can make. And with it, we made a bar chart. In this example, I want to show you how to make another kind of categorical chart, the pie chart, that's a common choice for categorical variables. The procedure is very similar to that of bar charts; however, there are a couple of important differences. These have to do with the demands that pie charts place on the nature of the data. These are that the data must be exhaustive and mutually exclusive. What that is is exhaustive means that all the categories need to cover all of the possibilities and add up to 100%.

That may require that you create an Other response category or a No Response category. Mutually exclusive means that each person needs to fall into just one category. And while there are many situations where the condition of mutual exclusivity isn't a problem, for instance a person can be born in only one country, there are least as many situations where it doesn't work-- for instance, college attended, as many people have attended more than one. This can create a real limitation in the applicability of pie charts. Also, there is another issue in that bar charts are pretty easy to read, because you simply have to be able to judge the length of the height of a bar.

That's a linear measure. Pie charts generally require that a person be able to judge angles and areas, both of which are rather difficult. And so these are challenges for pie charts, the demands they place on the data of being exclusive and comprehensive, and also the interpretability. Nevertheless, they are very common choices, so I will show you how to do these quickly in SPSS. Like all of the other charts we are going to do, you want to start by going up to Graphs, to the Chart Builder. From there, on the Gallery list, come down to Pie. Click on that and just drag the pie up in into the canvas. From there I'm going to pick Genre and put that down right there, and then I can press OK.

Like in the basic bar chart, it's very colorful. You can see that the yellow slice is the largest of all--that's Drama--and that the purple is probably the next biggest, and the others are a little bit smaller. Now there are ways to customize the pie chart in SPSS, but given that I have think that pie charts are generally a little harder to read, I generally encourage you to try bar charts instead. But this is another common option for depicting the categorical variable in SPSS.

So creating a pie chart in SPSS is a simple affair, and it still gives a lot of options to control over how it looks. However, given the challenges of reading pie charts, and the restrictions they place on the data, you may want to consider using a bar chart instead. On the other hand, in your corporate culture, pie charts may be the lingua franca, they may be what's expected, and you may want to introduce some variety in your charts, so they can be a viable option in SPSS.

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This video is part of

Image for SPSS Statistics Essential Training (2011)
SPSS Statistics Essential Training (2011)

52 video lessons · 18810 viewers

Barton Poulson
Author

 
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  1. 2m 58s
    1. Welcome
      1m 5s
    2. Using the exercise files
      40s
    3. Using a different version of the software
      1m 13s
  2. 19m 0s
    1. Taking a first look at the interface
      11m 49s
    2. Reading data from a spreadsheet
      7m 11s
  3. 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. 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. 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. 22m 14s
    1. Calculating frequencies
      8m 43s
    2. Calculating descriptives
      5m 31s
    3. Using the Explore command
      8m 0s
  7. 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. 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. 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. 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. 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. 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. 51s
    1. What's next
      51s

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