Correlation Matrix

The Correlation Matrix tool explores the relationship between variables by measuring the correlation between values across variables. The tool visualises the correlation co-efficient of the variables through a matrix-like pictorial presentation. A correlation matrix is a matrix giving the correlation co-efficient between all pairs of data sets. A correlation matrix is a square symmetrical (MxM) matrix with the element equal to the correlation coefficient between the variables. The diagonal elements (correlations of variables with themselves) are always equal to one. Likewise, the Correlation tool measures the correlation coefficient in a textual matrix.


To illustrate the use of the Correlation Matrix tool, we will use a dataset with a number of variables in it that can be related to each other – Voting patterns across Adelaide, South Australia for the 2019 Federal Election. To do this:

  • Select the Greater Adelaide GCCSA as your area
  • Select AEC – Federal Election – First Party Preference by Polling Place (Point) 2019 as your dataset with the following variables:
    • Polling Place ID
    • Liberal Percent
    • United Australia Party Percent
    • Australian Labor Party Percent

Once you have added the dataset, and all of its attributes, you are ready to use the Correlation Matrix tool – follow on to learn about the input options.



Once you have set up your data, open the Correlation Matrix tool (Tools → Charts → Correlation Matrix). The input fields are as follows;

  • Dataset Input: The dataset containing the variables that you would like to run through the Correlation Matrix Tool. Select AEC – Federal Election – First Party Preference by Polling Place (Point) 2019.
  • Variables: Check the variables you would like to include in the analysis. Select the three political party variables.
  • Use Variable Titles: Display the variable titles on the chart instead of the (machine-readable) name.
  • Chart Title: Title of the chart to display.
  • Greyscale: Produce the chart in a greyscale colour scheme.

The input parameters are summarised in the image below, once complete click Run Tool.


Once the tool has run, click the Display button on the pop-up dialogue box that appears. This will open a window with the outputs of your Correlation Matrix result, which should look like the image below.

On the bottom left of the matrix are the correlation coefficients (r) for each pair-wise correlation. On the top right of the matrix are pictorial representations of each of the correlation values. Ellipses that are yellow represent positive correlation statistics; green ellipses represent negative correlation statistics. Narrow ellipses represent correlation statistics that are stronger (closer to -1 or 1), while wider ellipses represent correlation statistics that are weaker (closer to 0).

We can see that the strongest relationship is a negative one between the percentage of people who voted for the ALP and for people who voted for the Liberal party. The next is a positive correlation between the ALP and United Australian Party, and finally a neutral relationship between the voters of the Liberal Party and United Australia Party.

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