Data considerations for Multiple Correspondence Analysis

To ensure that your results are valid, consider the following guidelines when you collect data, perform the analysis, and interpret your results.

You can have raw data or data in indicator variable form
If you have raw categorical data, you can have one or more classification columns with each row representing one observation. The data represent categories and may be numeric, text, or date/time. If your data are in indicator variable form, each row also represents one observation and you have one indicator column for each category level. The data in this form consist of 0s, except for 1s that identify the categories the observations belong to. For more information, go to Enter your data for Multiple Correspondence Analysis. You must delete rows with missing data from the worksheet before using this analysis.
You can use supplementary data
You might have additional or supplementary data in the same form as the main data set for the analysis. These supplementary data could be further information from the same study, information from other studies, or target profiles. You can use the supplementary data to validate the components, often with a historical value or known standard. You can also explore the scores of auxiliary data, such as outliers that you remove from the analysis. Supplementary data appear in the output but do not affect the components.
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