Consumer Research • Multiple Correspondence Analysis • Example of Multiple Correspondence Analysis
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Example of Multiple Correspondence Analysis
This example uses the Car Poll.jmp sample data table, which contains data collected from car polls. The data include aspects about the individuals polled, such as sex, marital status, and age. The data also include aspects about the car that they own, such as the country of origin, the size, and the type. You want to explore relationships between sex, marital status, country and size of car to identify consumer preferences.
1.
Select Help > Sample Data Library and open Car Poll.jmp.
2.
Select Analyze > Consumer Research > Multiple Correspondence Analysis.
3.
Select sex, marital status, country, and size and click Y, Response.
In MCA, usually all columns are considered responses rather than some being responses and others explanatory.
4.
Click OK.
Completed Multiple Correspondence Analysis Launch Window
The Multiple Correspondence Analysis report is shown in Multiple Correspondence Analysis Report. Note that some of the outlines are closed because of space considerations.
The Variable Summary report provides a concise view of the analysis completed.
The Correspondence Analysis report shows the cloud of categories of the four variables as projected onto the two principal axes. From this cloud, you can see that Americans have a strong association with the large car size while Japanese are highly associated with the small car size. Also, males are strongly associated with the small car type and females are associated with the medium car size. This information could be used in market research to identify target audiences for advertisements.
Multiple Correspondence Analysis Report
Launch the Multiple Correspondence Analysis Platform
Launch the Multiple Correspondence Analysis platform by selecting Analyze > Consumer Research > Multiple Correspondence Analysis. This example uses the Car Poll.jmp sample data table.
Multiple Correspondence Analysis Launch Window
Y, Response
Assigns the categorical columns to be analyzed. Usually, in MCA, you are interested in the associations between variables, but there are not explicit “explanatory” and “response” variables.
X, Factor
Assigns the categorical columns to be used as factor, or explanatory, variables.
Z, Supplementary Variable
Assigns the columns to be used as supplementary variables. These variables are those you are interested in identifying associations with but not include in the calculations.
Supplementary ID
Assigns the column that identifies rows to be used as supplementary. A supplementary ID column usually has 1s and 0s. The rows associated with ID 0 are treated as supplementary rows. The Supplementary ID option functions if only one Y and only one X column have been specified.
Freq
Assigns a frequency variable to this role. This is useful if your data are summarized.
By
Produces a separate report for each level of the By variable. If more than one By variable is assigned, a separate report is produced for each possible combination of the levels of the By variables.