Glossary

Factor analysis

Definition

Factor analysis looks for a small number of hidden (latent) factors that explain the correlations among many measured variables — e.g. items of a satisfaction survey cluster into "price", "quality" and "service" factors. Each variable has loadings on the factors; rotation (Varimax) simplifies the structure so the factors can be named.

Multivariate methods

Typical uses: questionnaire development and validation, psychometrics, marketing research, reducing a set of indicators. Judge data suitability by the correlation matrix and the Kaiser–Meyer–Olkin criterion; you usually need 5–10 observations per variable.

Factor scores can be saved as new variables and used in regression or segmentation. Exploratory factor analysis searches for the structure; confirmatory analysis (in structural equation modelling) tests it against a predefined theory.

In Statistica

Statistics → Multivariate Exploratory Techniques → Factor Analysis offers principal-components and maximum-likelihood extraction, Varimax, Quartimax, Equamax and oblique rotations, a loadings plot, a scree plot and saving of factor scores. Reliability & Item Analysis adds scale reliability (Cronbach's α).

Related terms

Knowledgebase guides

FAQ

How many factors should I extract?
By eigenvalues above 1, the scree plot and above all interpretability — a factor you cannot name is usually not useful.
What is rotation?
Turning the factor axes so that each variable loads on one factor as far as possible. Varimax keeps factors uncorrelated; oblique rotations (Oblimin) allow correlation.

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