# Principal Components Analysis

From Q

Related Online Training modules | |
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Principal Components Analysis | |

Generally it is best to access online training from within Q by selecting Help > Online Training |

**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.

## See also

- Confirmatory Factor Analysis Using R
- SurveyAnalysis.org for an overview of Principal Components Analysis.