# Creating population pyramids

## Video: Creating population pyramids

In the last movie we looked at how you can create pie charts to show the mean or maybe the median, for each group on a categorical variable. However sometimes, it can be more helpful to see not just a single summary statistic, but the entire distribution of scores for each group. One way to do this, provided your categorical variable is a dichotomy, that is it has just two values, is a variation on the histogram or bell curve that we looked at back in the section on univariate charts. In this case what we are going to create is a pair of back-to-back histograms, what SPSS calls a population pyramid.

## Creating population pyramids

In the last movie we looked at how you can create pie charts to show the mean or maybe the median, for each group on a categorical variable. However sometimes, it can be more helpful to see not just a single summary statistic, but the entire distribution of scores for each group. One way to do this, provided your categorical variable is a dichotomy, that is it has just two values, is a variation on the histogram or bell curve that we looked at back in the section on univariate charts. In this case what we are going to create is a pair of back-to-back histograms, what SPSS calls a population pyramid.

For this example, I'm going to be using the Searches.sav data file, and I am going to be looking at relative interest in NBA, as a search term, and compare that with whether a state has an NBA team or not. Now I am going to do this by going up to Graphs, to Chart Builder, and from there, I come down to Histogram, because the pyramid plot is a variation on the Histogram. This one on the far right, Population Pyramid, I drag that up to the canvas, and then what I'm going to do is I am going to come on this variable list and scroll down until I find the results for NBA as a Google search term, and I take that over to the distribution variable. We are trying to find out how common that is.

Then I am going to split it by whether the state has an NBA team. That's this variable right here and I take that up to the split variable, and from there I can just press OK. And what we find in this one is that the states that have an NBA team, the ones on the right side in the green, tend to have the higher scores on the relative interest in NBA as a search term in Google, as opposed to the states that don't have NBA teams. For instance, on the right we see that there are two states that have relative interest in NBA, right around three standard deviations above the mean.

On the other hand we see of the states that don't have NBA teams, a lot of them are below zero, around negative one. And so this is a way of looking at things back to back in Histogram and making the differences between the two sets really obvious. Now if you want to, you can double- click on this chart and you can change the colors on each side,. You can change the bins. You can change the number of decimal places on the side, the same way that we've edited nearly everything else. But this one is probably clear enough as it is.

So a population pyramid, that is, a back- to-back histogram, this can be a new way to compare the distribution of a scale variable across two different groups. Like a regular Univariate Histogram, it lets you examine the shape of the distribution, let's you check visually for outliers, and lets you identify any possible quirks in the data that might throw off later analyses. In the next movie, we will look at one final display for showing the association between the categorical variable and skilled variable, what's called grouped boxplots.

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#### This video is part of

SPSS Statistics Essential Training (2011)

52 video lessons · 20127 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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