# Creating simple boxplots for groups

## Video: Creating simple boxplots for groups

In this movie, on graphing the association between two variables, we will look at what SPSS calls simple boxplots, which is a series of boxplots for a single scale variable, broken down by the groups in a single categorical variable. One of the main benefits of this particular chart is that it allows you to check for outliers separately for each group. This is important because a variable may not have any outliers, when all of the cases are considered together, but can have an outlier when groups are separated.

## Creating simple boxplots for groups

In this movie, on graphing the association between two variables, we will look at what SPSS calls simple boxplots, which is a series of boxplots for a single scale variable, broken down by the groups in a single categorical variable. One of the main benefits of this particular chart is that it allows you to check for outliers separately for each group. This is important because a variable may not have any outliers, when all of the cases are considered together, but can have an outlier when groups are separated.

For example, enough people in the sample might be 6'4" tall, that it might not be considered an outlier overall, but that it almost certainly would be an outlier, if you looked at the heights of men and women separately. So, here's how to break boxplots down by various categories. For this example, I am going to be using the Searches database again from Google, Searches.sav, and except in this case I am going to be looking at the relative interest in search for this one variable, Modern Dance as a search term and break it down by region.

To do this, I am going to go up to Graphs, to Chart Builder, and I am going to come down to Boxplot, and I am going to take this first one which is called the Simple Boxplot and drag it up to this canvas, and from there I'm going to get the Region variable, that's this one, Census Bureau region, and drag that down to the X axis. Then I'm going to get the variable that shows the relative interest in Modern Dance as a search term. From there I'm going to add group and point IDs. This is helpful when you're labeling outliers, which often show up in boxplots.

So I'm going to come down and click on Point ID label, and then I am going to get the State Code from the variable list, and drag that over, and that's all I need for right now. So I am going to come down and press OK. And what you find rather surprisingly is that Utah is an extraordinarily high outlier on the far right, been four-and-a-half Standard Deviations above the national average in the relative mind sharing interest in Modern Dance as a Google search term.

You might associate Modern Dance with the city like New York and the Northeast, and you do see that New York is an outlier on the left side, but still it's at only about a value of one standard deviation above the mean. And you can see that there are others at a much lower interest and the Midwest is generally below 0, that they are negative. And so, this is a good way of looking at the relative differences in distributions especially in outliers of one group across another.

The Simple Boxplot is a great way to compare the distributions of a single scale variable, for the different groups in the categorical variable, and again because it's especially important to identify outliers because they can wreak havoc with the statistical procedures, it's an important consideration before going on to further analysis, like the inferential statistics for associations that we will cover in the next several movies.

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#### This video is part of

SPSS Statistics Essential Training (2011)

52 video lessons · 20020 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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