Displayr can conduct correspondence analysis on a non-Displayr table by importing it from Excel or CSV using one of the methods described in How to Import Table Data into Displayr.
For example:
The output will show row numbers which then need to be replaced with the row names.
To turn this into a Correspondence Analysis Map:
- From the toolbar, go to Visualization
> Dimension Reduction > Correspondence Analysis of a Table.
- Select the input table and any other options you might want. In this example, I selected Normalization > Symmetrical (1/2).
The results are as follows:
Analyzing numeric and other forms of data
Although the theory of correspondence analysis is designed with categorical variables in mind, it can be used to analyze any data tables. There are some general principles to keep in mind:
- It is generally advisable to have data tables that show things in the same scale. For example, one column showing percentages and another showing millions of people will likely be problematic.
- Often the most valid approach is to quantize variables (e.g., turn a Number question into a categorical question and collapse categories).
You can create the chart either from raw data or a table.
Overlaying additional information onto maps
When using multiple correspondence analysis, you can select Save Variable(s) > Components/Dimensions to create additional variables. These additional variables are scaled by the canonical correlates (i.e., they are principal coordinates). You can add additional information on the map by crosstabbing these factors with other variables. The resulting means will show the appropriate coordinates on the map.
Using multiple correspondence analysis to form segments
There are two basic ways to use correspondence analysis to form segments:
- Using judgment. For example, with a map showing brand associations, judgment can be used to group together brands that are on similar positions on the map, and consumers can be assigned to segments based on their relationship with the brands (e.g., consumers can be grouped into segments based on the brands that they buy or like).
- When using multiple correspondence analysis, the factors can be saved and then segments can be formed using either judgment, cluster analysis, or latent class analysis (This is because approaches such as this introduce two separate forms of error into the modeling – the error in forming the factors and the error in forming the segments – whereas latent class analysis only has the one form of error and will thus have less error in the model; furthermore, the mathematics of latent class analysis guarantee it will usually not have more error than cluster analysis). Where the intent is to use judgment, this approach can be appropriate, but in general, it is preferable to use latent class. When using the factors from multiple correspondence analysis, it is important to remember that each factor has a smaller standard deviation; the easiest way is to form segments using latent class analysis.
Next
How to Do Traditional Correspondence Analysis