Principal Components Analysis
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|Principal Components Analysis|
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Principal Components Analysis identifies interrelationships between variables. It is useful for identifying underlying dimensions of consumer behavior, summarizing data and identifying redundant questions in questionnaires.
A principal components analysis (PCA) is run in Q by:
- Select the question you wish to analyze in the Blue Drop-down Menu. If there are multiple questions, you will need to first combine them into a single question.
- Change the question's Question Type to Number - Multi (Q will also analyze a Pick Any question, but you will find the outputs harder to interpret).
- Select Create: Traditional Multivariate Analysis > Principal Components Analysis to run a principal components analysis (PCA).
Buttons, options and fields
Principal components analysis is a technique which turns a set of numeric variables into another, smaller, set of numeric variables.
Rule for selecting components
- Kaiser rule Selects components with eigenvalues greater than or equal to 1.
- Broken stick Selects components with eigenvalues greater than predicted by a broken stick distribution.
- Eigenvalues over Specify a cutoff point for retaining eigenvalues.
Number of components Retains this number of components (the largest components are retained).
Varimax Performs a Varimax rotation of the components (and loadings) to facilitate interpretation.
Ignore NET and SUM Excludes the NET or SUM row from the analysis.