Factor analysis
Factor Analysis
Factor analysis (/ˈfæktər əˈnælɪsɪs/) is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors.
Etymology
The term "factor analysis" was first coined by Charles Spearman in the early 20th century. The word "factor" comes from the Latin "factorum", meaning "doer or maker", and "analysis" comes from the Greek "analusis", meaning "a breaking up".
Definition
Factor analysis is a technique that is used to reduce a large number of variables into fewer numbers of factors. The observed variables are modelled as linear combinations of the potential factors, plus "error" terms. The information gained about the interdependencies between observed variables can be used later to reduce the set of variables in a dataset.
Related Terms
- Principal Component Analysis: A statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.
- Exploratory Factor Analysis: A statistical method used to uncover the underlying structure of a relatively large set of variables.
- Confirmatory Factor Analysis: A multivariate statistical procedure used to test how well the measured variables represent the number of constructs.
- Latent Variable: A variable that is not directly observed but is inferred or estimated from other variables that are observed or directly measured.
- Eigenvalues and Eigenvectors: In linear algebra, an eigenvector or characteristic vector of a linear transformation is a nonzero vector that changes at most by a scalar factor when that linear transformation is applied to it.
See Also
External links
- Medical encyclopedia article on Factor analysis
- Wikipedia's article - Factor analysis
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