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:

  1. 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.
  2. 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).
  3. Select Create: Traditional Multivariate Analysis > Principal Components Analysis to run a principal components analysis (PCA).


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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.

See also

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