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Creating population pyramids

From: SPSS Statistics Essential Training (2011)

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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SPSS Statistics Essential Training (2011)

52 video lessons · 20127 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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