# Factor Analysis

## Factor Analysis

(Statistical Analysis > Factor Analysis)

Description
Factor Analysis is a technique to reduce the number of variables into a smaller number of unobservable factors. Factor Analysis can be performed using maximum-likelihood solution on a covariance matrix or data matrix.

## Inputs

Factor Analysis Dataset Input – select a dataset that contains the variables of interest.
Factor Analysis Variable Names – a set of independent variables.
Factor Analysis Number of Factors – number of factors to extract. Default is 1.
Factor Analysis Rotate – “none”, “varimax” and “promax” are possible functions to be used to rotate the factors: it will be called with first argument the loadings matrix and should return a list with component loadings giving the rotated loadings, or just the rotated loadings.
Factor Analysis Scores – type of scores to produce, if any. The default is none, “regression” gives Thompson’s scores, “Bartlett” given Bartlett’s weighted least-squares scores. Partial matching allows these names to be abbreviated.
Factor Analysis The number of starting values – the number of starting values to be tried. Default 1.
Factor Analysis Lower bound for uniquenesses – the lower bound for uniquenesses during optimization. Should be > 0. Default 0.005.

## Outputs

Output includes the following:
Converged: Rotation converged (TRUE or FALSE)
Uniquenesses : the uniqueness of each variable is computed as the noise left over after the factors are ﬁtted.
Correlation: a measurement of how two variables are related.
Criteria : the results of the optimization: the value of the negative log-likelihood and information on the iterations used, including objective and counts.function and counts.gradient.
Factors : the number of factors.
Dof : the number of degrees of freedom of the factor analysis model.
The Chi square statistic : the significance-test statistic
The p-value: p value, if it can be computed.
N.obs : the number of observations.