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Factor AnalysisStat > Multivariate > Factor Analysis
Use factor analysis, like principal components analysis, to summarize the data covariance structure in a few dimensions of the data. However, the emphasis in factor analysis is the identification of underlying "factors" that might explain the dimensions associated with large data variability.
Variables: Choose the columns containing the variables you want to use in the analysis. If you want to use a stored correlation or covariance matrix, or the loadings from a previous analysis instead of the raw data, click <Options>.
Number of factors to extract: Enter number of factors to extract (required if you use maximum likelihood as your method of extraction). If you don't specify a number with a principal components extraction, Minitab sets it equal to the number of variables in the data set. If you choose too many factors, Minitab will issue a warning in the Session window.
Method of Extraction:
Principal components: Choose to use the principal components method of factor extraction.
Maximum likelihood: Choose to use maximum likelihood for the initial solution.
Type of Rotation: Controls orthogonal rotations.
None: Choose not to rotate the initial solution.
Equimax: Choose to perform an equimax rotation of the initial solution (gamma = number of factors / 2).
Varimax: Choose to perform a varimax rotation of the initial solution (gamma = 1).
Quartimax: Choose to perform a quartimax rotation of the initial solution (gamma = 0).
Orthomax with gamma: Choose to perform an orthomax rotation of the initial solution, then enter value for gamma between 0 and 1.