A Monte Carlo Study of the Raw and Normal Varimax Rotation Criterion in Factor Analysis
Abstract
Factor analysis is a multivariate statistical procedure for analyzing and reducing large data sets. Many factor analysis schemes and techniques are available that lead to strikingly different results from the same data. This research effort used a Monte Carlo approach to investigate the properties of two rotation methods for simple structure, Kaiser's raw and normal varimax criterion. Data sets were developed from a set of contrived experimental factor patterns by multiplying each factor pattern by its transpose to create a covariance matrix. Data sets of multivariate normal deviates were in turn generated from each covariance matrix via the Choleski algorithm. Rotated factor pattern matrices from each data set were compared to their respective experimental factor pattern on the basis of structure, loadings and eigenvalues. These performance issues are addressed through regression analysis and separate factor analysis in which the grand mean of proposed measures of effectiveness are predicted. These measures of effectiveness include structure matching and root mean square error between the experimental and observed factor patterns. Several methods of characterizing factor pattern complexity and predicting rotation criterion performance are explored....Factor analysis, Monte Carlo, Varimax, Choleski.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 23, 1993
- Accession Number
- ADA262516
Entities
People
- Willam M. Ibinson
Organizations
- Air Force Institute of Technology